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android / platform / external / pytorch / 7afe0a8c6d / . / test / test_cuda.py
blob: 015e83614971d1ea0832360cd988caae60e02e38 [file] [log] [blame]
import io
import tempfile
import unittest
import sys
from itertools import repeat
import os
from contextlib import contextmanager
import threading
import math
if sys.version_info[0] == 3:
import queue
else:
import Queue as queue
import torch
import torch.cuda
import torch.cuda.comm as comm
from torch import multiprocessing as mp
from torch._six import inf, nan
from test_torch import _TestTorchMixin
from common_methods_invocations import tri_tests_args, tri_large_tests_args, \
_compare_trilu_indices, _compare_large_trilu_indices
from common_utils import TestCase, get_gpu_type, to_gpu, freeze_rng_state, run_tests, \
PY3, IS_WINDOWS, NO_MULTIPROCESSING_SPAWN, skipIfRocm, TEST_NUMPY, TEST_WITH_ROCM, \
load_tests, slowTest, skipCUDANonDefaultStreamIf
# load_tests from common_utils is used to automatically filter tests for
# sharding on sandcastle. This line silences flake warnings
load_tests = load_tests
# We cannot import TEST_CUDA and TEST_MULTIGPU from common_cuda here,
# because if we do that, the TEST_CUDNN line from common_cuda will be executed
# multiple times as well during the execution of this test suite, and it will
# cause CUDA OOM error on Windows.
TEST_CUDA = torch.cuda.is_available()
TEST_MULTIGPU = TEST_CUDA and torch.cuda.device_count() >= 2
if not TEST_CUDA:
print('CUDA not available, skipping tests')
TestCase = object # noqa: F811
TEST_MAGMA = TEST_CUDA
TEST_LARGE_TENSOR = TEST_CUDA
if TEST_CUDA:
torch.ones(1).cuda() # has_magma shows up after cuda is initialized
TEST_MAGMA = torch.cuda.has_magma
TEST_LARGE_TENSOR = torch.cuda.get_device_properties(0).total_memory >= 12e9
floating_set = {torch.FloatTensor, torch.DoubleTensor, torch.cuda.FloatTensor,
torch.cuda.DoubleTensor, torch.HalfTensor, torch.cuda.HalfTensor}
def is_floating(t):
if not isinstance(t, type):
raise TypeError('t should be an instance of type')
assert t != torch.autograd.Variable
return t in floating_set
def is_half(t):
if isinstance(t, torch.Tensor):
return t.dtype == torch.float16
assert isinstance(t, type)
assert t != torch.autograd.Variable
return t in [torch.HalfTensor, torch.cuda.HalfTensor]
types = [
torch.FloatTensor,
torch.DoubleTensor,
torch.LongTensor,
torch.IntTensor,
torch.ShortTensor,
torch.CharTensor,
torch.ByteTensor,
torch.HalfTensor,
]
signed_types = [
torch.FloatTensor,
torch.DoubleTensor,
torch.LongTensor,
torch.IntTensor,
torch.ShortTensor,
torch.CharTensor,
]
unsigned_types = [
torch.ByteTensor,
]
float_types = [
torch.FloatTensor,
torch.DoubleTensor,
torch.HalfTensor,
]
float_types_no_half = [
torch.FloatTensor,
torch.DoubleTensor,
]
def number(floating, integer, t):
return floating if is_floating(t) else integer
def cast_tensor(tensor, t):
return t(tensor.size()).copy_(tensor)
S = 10
M = 50
G = 275000000
def make_tensor(t, *sizes):
if 'Half' in t.__name__:
return t(*sizes).copy_(torch.randn(*sizes))
else:
tensor = t(*sizes)
if tensor.is_floating_point():
return tensor.normal_()
else:
return tensor.random_(0, 10)
def make_sparse_tensor(t, n, *sizes):
assert t.is_sparse
tensor = t()
i = tensor._indices()
i = i.new(len(sizes), n).copy_(
torch.cat([torch.LongTensor(1, n).random_(s) for s in sizes], 0))
v = tensor._values()
v = v.new(n).copy_(torch.randn(n))
return t(i, v, torch.Size(sizes))
def tensor_clamp(t, min, max):
if is_half(t):
return t.float().clamp(min, max).half()
else:
return t.clamp(min, max)
def tensor_mul(t, scale):
if is_half(t):
return t.float().mul(scale).half()
else:
return t.mul(scale)
def tensor_abs_(t):
if is_half(t):
return t.float().abs_().half()
else:
return t.abs_()
def constant_tensor_sub(a, b):
# helper function to address const - torch.HalfTensor where it doesn't
# have resize_as()
if is_half(b):
return (a - b.float()).half()
else:
return a - b
def constant_tensor_add(a, b):
# helper function to address const + torch.HalfTensor where it doesn't
# have add()
if is_half(b):
return (a + b.float()).half()
else:
return a + b
def small_0d(t):
return make_tensor(t, (1,)).squeeze()
def small_2d(t):
return make_tensor(t, S, S)
def small_2d_scaled(t, scale=10):
return tensor_mul(make_tensor(t, S, S), scale)
def small_2d_oneish(t):
if is_floating(t):
return tensor_clamp(make_tensor(t, S, S), min=0.99, max=1.01)
else:
return t(S, S).fill_(1)
def small_3d(t):
return make_tensor(t, S, S, S)
def medium_1d(t):
return make_tensor(t, M)
def medium_2d(t):
return make_tensor(t, M, M)
def medium_2d_expanded(t):
return t(1).expand(M, M)
def medium_2d_scaled(t, scale=10):
return tensor_mul(make_tensor(t, M, M), scale)
def small_3d_ones(t):
return t(S, S, S).copy_(torch.ones(S, S, S))
def small_3d_positive(t):
# In div_tensor(), half cannot achieve float precision
min_val = 1e-3 if is_floating(t) and not is_half(t) else 2
return tensor_clamp(make_tensor(t, S, S, S), min_val, 120)
def small_3d_unique(t):
return t(S, S, S).copy_(torch.arange(1, S * S * S + 1).view(S, S, S))
def small_1d_lapack(t):
return t(1, 3).copy_(torch.arange(1, 4).view(3))
def small_2d_lapack(t):
return t(3, 3).copy_(torch.arange(1, 10).view(3, 3))
def small_2d_lapack_skinny(t):
return t(3, 4).copy_(torch.arange(1, 13).view(3, 4))
def small_2d_lapack_fat(t):
return t(4, 3).copy_(torch.arange(1, 13).view(4, 3))
def large_2d_lapack(t):
return t(1000, 1000).normal_()
def giant_1d_ones(t):
return t(G).copy_(torch.ones(G))
def long_type(t):
return torch.cuda.LongTensor if 'cuda' in t.__module__ else torch.LongTensor
def new_t(*sizes):
def tmp(t):
return t(*sizes).copy_(torch.randn(*sizes))
return tmp
# Content of each tuple:
# - function name
# - constructor for the tensor, signature: fn(tensor_type) -> tensor
# - constructor for the arguments, signature: fn(tensor_type) -> list
# - postfix name for the test (must be unique for a given function) (default='')
# - tensor types to use (default=types)
# - disable inplace test, if set to True, no inplace test will be done (default=False)
# - decorator, e.g., unittest.skipIf (default is no decorator)
tests = [
('add', small_3d, lambda t: [number(3.14, 3, t)]),
('add', small_3d, lambda t: [small_3d_positive(t)], 'tensor'),
('add', small_3d, lambda t: [number(0.2, 2, t), small_3d_positive(t)], 'scalar_tensor'),
('sub', small_3d, lambda t: [number(3.14, 3, t)]),
('sub', small_3d, lambda t: [small_3d_positive(t)], 'tensor'),
('mul', small_3d, lambda t: [number(3.14, 3, t)]),
('mul', small_3d, lambda t: [small_3d_positive(t)], 'tensor'),
('mul', small_0d, lambda t: [small_0d(torch.IntTensor)], 'scalar', types, True),
('div', small_3d, lambda t: [number(3.14, 3, t)]),
('div', small_3d, lambda t: [small_3d_positive(t)], 'tensor'),
('pow', small_3d, lambda t: [number(3.14, 3, t)], None, float_types),
('pow', small_3d, lambda t: [number(1., 1, t)], 'pow1'),
('pow', small_3d, lambda t: [number(2., 2, t)], 'pow2'),
('pow', small_3d, lambda t: [number(3., 3, t)], 'pow3'),
('pow', small_3d, lambda t: [number(-1., -1, t)], 'pow-1', float_types),
# HalfTensor gives bad result at pow-2 with data sampled from torch.randn
('pow', small_3d, lambda t: [number(-2., -2, t)], 'pow-2', float_types_no_half, False,
"skipIfRocm:FloatTensor"),
('pow', small_3d, lambda t: [tensor_abs_(small_3d(t))], 'tensor', float_types),
('addbmm', small_2d, lambda t: [small_3d(t), small_3d(t)], None, float_types),
('addbmm', small_2d, lambda t: [number(0.4, 2, t), small_3d(t), small_3d(t)], 'scalar'),
('addbmm', small_2d, lambda t: [number(0.5, 3, t), number(0.4, 2, t), small_3d(t), small_3d(t)], 'two_scalars'),
('baddbmm', small_3d, lambda t: [small_3d(t), small_3d(t)],),
('baddbmm', small_3d, lambda t: [number(0.4, 2, t), small_3d(t), small_3d(t)], 'scalar'),
('baddbmm', small_3d, lambda t: [number(0.5, 3, t), number(0.4, 2, t), small_3d(t), small_3d(t)], 'two_scalars'),
('bmm', small_3d, lambda t: [small_3d(t)], '', float_types_no_half),
('addcdiv', small_2d_lapack, lambda t: [tensor_mul(small_2d_lapack(t), 2), small_2d_lapack(t)]),
('addcdiv', small_2d_lapack, lambda t: [number(2.8, 1, t), tensor_mul(small_2d_lapack(t), 2), small_2d_lapack(t)],
'scalar'),
('addcmul', small_3d, lambda t: [small_3d(t), small_3d(t)]),
('addcmul', small_3d, lambda t: [number(0.4, 2, t), small_3d(t), small_3d(t)], 'scalar'),
('addmm', medium_2d, lambda t: [medium_2d(t), medium_2d(t)]),
('addmm', medium_2d, lambda t: [number(0.4, 2, t), medium_2d(t), medium_2d(t)], 'scalar'),
('addmm', medium_2d, lambda t: [number(0.5, 3, t), number(0.4, 2, t), medium_2d(t), medium_2d(t)], 'two_scalars'),
('addmv', medium_1d, lambda t: [medium_2d(t), medium_1d(t)],),
('addmv', medium_1d, lambda t: [number(0.4, 2, t), medium_2d(t), medium_1d(t)], 'scalar'),
('addmv', medium_1d, lambda t: [number(0.5, 3, t), number(0.4, 2, t), medium_2d(t), medium_1d(t)], 'two_scalars'),
('addr', medium_2d, lambda t: [medium_1d(t), medium_1d(t)]),
('addr', medium_2d, lambda t: [number(0.4, 2, t), medium_1d(t), medium_1d(t)], 'scalar'),
('addr', medium_2d, lambda t: [number(0.5, 3, t), number(0.4, 2, t), medium_1d(t), medium_1d(t)], 'two_scalars'),
('atan2', medium_2d, lambda t: [medium_2d(t)], None, float_types + [torch.HalfTensor]),
('fmod', small_3d, lambda t: [3], 'value',),
('fmod', small_3d, lambda t: [small_3d_positive(t)], 'tensor'),
('chunk', medium_2d, lambda t: [4],),
('chunk', medium_2d, lambda t: [4, 1], 'dim'),
('chunk', medium_2d, lambda t: [4, -2], 'neg_dim'),
('clamp', medium_2d_scaled, lambda t: [-1, 5], None, signed_types),
('clamp', medium_2d_scaled, lambda t: [1, 5], None, unsigned_types),
('clone', medium_2d, lambda t: [],),
('contiguous', medium_2d, lambda t: [],),
('cross', new_t(M, 3, M), lambda t: [new_t(M, 3, M)(t)],),
('cumprod', small_3d, lambda t: [1]),
('cumprod', small_3d, lambda t: [-1], 'neg_dim'),
('cumsum', small_3d, lambda t: [1]),
('cumsum', small_3d, lambda t: [-1], 'neg_dim'),
('dim', small_3d, lambda t: [],),
('dist', small_2d, lambda t: [small_2d(t)]),
('dist', small_2d, lambda t: [small_2d(t), 3], '3_norm'),
('dist', small_2d, lambda t: [small_2d(t), 2.5], '2_5_norm'),
('dot', medium_1d, lambda t: [medium_1d(t)], '', types, False, "skipIfRocm:HalfTensor"),
('element_size', medium_1d, lambda t: [],),
('eq', small_3d_ones, lambda t: [small_3d(t)],),
('eq', small_3d_ones, lambda t: [small_3d_ones(t)], 'equal'),
('ne', small_3d_ones, lambda t: [small_3d(t)],),
('ne', small_3d_ones, lambda t: [small_3d_ones(t)], 'equal'),
('equal', small_3d_ones, lambda t: [small_3d_ones(t)], 'equal'),
('equal', small_3d_ones, lambda t: [small_3d(t)],),
('expand', new_t(M, 1, M), lambda t: [M, 4, M],),
('expand_as', new_t(M, 1, M), lambda t: [new_t(M, 4, M)(t)],),
('fill', medium_2d, lambda t: [number(3.14, 3, t)]),
('ge', medium_2d, lambda t: [medium_2d(t)],),
('le', medium_2d, lambda t: [medium_2d(t)],),
('gt', medium_2d, lambda t: [medium_2d(t)],),
('lt', medium_2d, lambda t: [medium_2d(t)],),
('is_contiguous', medium_2d, lambda t: [],),
# TODO: can't check negative case - GPU copy will be contiguous
('is_same_size', medium_2d, lambda t: [small_3d(t)], 'negative'),
('is_same_size', medium_2d, lambda t: [medium_2d(t)], 'positive'),
('is_set_to', medium_2d, lambda t: [medium_2d(t)],),
# TODO: positive case
('kthvalue', small_3d_unique, lambda t: [3],),
('kthvalue', small_3d_unique, lambda t: [3, 1], 'dim'),
('kthvalue', small_3d_unique, lambda t: [3, -1], 'neg_dim'),
('lerp', small_3d, lambda t: [small_3d(t), 0.3]),
('max', small_3d_unique, lambda t: []),
('max', small_3d_unique, lambda t: [1], 'dim'),
('max', small_3d_unique, lambda t: [-1], 'neg_dim'),
('max', medium_2d, lambda t: [medium_2d(t)], 'elementwise'),
('min', small_3d_unique, lambda t: []),
('min', small_3d_unique, lambda t: [1], 'dim'),
('min', small_3d_unique, lambda t: [-1], 'neg_dim'),
('min', medium_2d, lambda t: [medium_2d(t)], 'elementwise'),
('mean', small_3d, lambda t: []),
('mean', small_3d, lambda t: [-1], 'neg_dim'),
('mean', small_3d, lambda t: [1], 'dim'),
('mean', giant_1d_ones, lambda t: [], '64bit_indexing',
# Double here because otherwise the CPU result will be
# wrong.
[torch.DoubleTensor]),
('mode', small_3d, lambda t: []),
('mode', small_3d, lambda t: [1], 'dim'),
('mode', small_3d, lambda t: [-1], 'neg_dim'),
('mvlgamma', lambda t: tensor_clamp(small_2d(t), 0.1, 10), lambda t: [1], '2d_p=1', float_types_no_half),
('mvlgamma', lambda t: tensor_clamp(small_2d(t), 0.6, 10), lambda t: [2], '2d_p=2', float_types_no_half),
('remainder', small_3d, lambda t: [3], 'value',),
('remainder', small_3d, lambda t: [-3], 'negative_value', signed_types),
('remainder', small_3d, lambda t: [small_3d_positive(t)], 'tensor'),
('remainder', small_3d, lambda t: [constant_tensor_sub(0, small_3d_positive(t))], 'negative_tensor', signed_types),
('std', small_3d, lambda t: []),
('std', small_3d, lambda t: [1], 'dim', types, False),
('std', small_3d, lambda t: [-1], 'neg_dim', types, False),
('var', small_3d, lambda t: []),
('var', small_3d, lambda t: [1], 'dim'),
('var', small_3d, lambda t: [-1], 'neg_dim'),
('ndimension', small_3d, lambda t: [],),
('nelement', small_3d, lambda t: [],),
('numel', small_3d, lambda t: [],),
('narrow', small_3d, lambda t: [1, 3, 2],),
('narrow', small_3d, lambda t: [-1, 3, 2], 'neg_dim'),
('nonzero', small_3d, lambda t: [], '', types, False),
('norm', small_3d, lambda t: []),
('norm', small_3d, lambda t: [3], '3_norm'),
('norm', small_3d, lambda t: [3, 0], '3_norm_dim'),
('norm', small_3d, lambda t: [3, -2], '3_norm_neg_dim'),
('ones', small_3d, lambda t: [1, 2, 3, 4, 5],),
('permute', new_t(1, 2, 3, 4), lambda t: [2, 1, 3, 0],),
('put_', new_t(2, 5, 3), lambda t: [long_type(t)([[0], [-2]]), t([[3], [4]])], '', types, False),
('put_', new_t(2, 3), lambda t: [long_type(t)([]), t([])], 'empty'),
('put_', new_t(2, 2), lambda t: [long_type(t)([[1], [-3]]), t([[1], [2]]), True], 'accumulate'),
('prod', small_2d_oneish, lambda t: []),
('prod', small_3d, lambda t: [1], 'dim'),
('prod', small_3d, lambda t: [-1], 'neg_dim'),
('sum', small_2d, lambda t: []),
('sum', small_3d, lambda t: [1], 'dim'),
('sum', small_3d, lambda t: [-1], 'neg_dim'),
('renorm', small_3d, lambda t: [2, 1, 1], '2_norm'),
('renorm', small_3d, lambda t: [2, -1, 1], '2_norm_neg_dim'),
('renorm', small_3d, lambda t: [1.5, 1, 1], '1_5_norm'),
('repeat', small_2d, lambda t: [2, 2, 2],),
('size', new_t(1, 2, 3, 4), lambda t: [],),
('size', new_t(1, 2, 3, 4), lambda t: [1], 'dim'),
('size', new_t(1, 2, 3, 4), lambda t: [-2], 'neg_dim'),
('sort', small_3d_unique, lambda t: [], ''),
('sort', small_3d_unique, lambda t: [1], 'dim'),
('sort', small_3d_unique, lambda t: [-1], 'neg_dim'),
('sort', small_3d_unique, lambda t: [1, True], 'dim_descending'),
('sort', small_3d_unique, lambda t: [-1, True], 'neg_dim_descending'),
('split', small_3d, lambda t: [2],),
('split', small_3d, lambda t: [2, 1], 'dim'),
('split', small_3d, lambda t: [2, -3], 'neg_dim'),
('squeeze', new_t(1, 2, 1, 4), lambda t: [],),
('squeeze', new_t(1, 2, 1, 4), lambda t: [2], 'dim'),
('squeeze', new_t(1, 2, 1, 4), lambda t: [-2], 'neg_dim'),
('t', new_t(1, 2), lambda t: [],),
('take', new_t(3, 4), lambda t: [long_type(t)([[0], [-2]])], '', types, False),
('transpose', new_t(1, 2, 3, 4), lambda t: [1, 2],),
('transpose', new_t(1, 2, 3, 4), lambda t: [-1, -2], 'neg_dim'),
('to_list', small_3d, lambda t: [],),
('topk', small_3d_unique, lambda t: [2, 1, False, True], 'dim_sort',),
('topk', small_3d_unique, lambda t: [2, -1, False, True], 'neg_dim_sort',),
('topk', small_3d_unique, lambda t: [2, 1, True, True], 'dim_desc_sort',),
('trace', medium_2d, lambda t: []),
('tril', medium_2d, lambda t: [],),
('tril', medium_2d_expanded, lambda t: [], 'zero_stride', types, True),
('tril', medium_2d, lambda t: [2], 'positive'),
('tril', medium_2d, lambda t: [-2], 'negative'),
('triu', medium_2d, lambda t: [],),
('triu', medium_2d_expanded, lambda t: [], 'zero_stride', types, True),
('triu', medium_2d, lambda t: [2], 'positive'),
('triu', medium_2d, lambda t: [-2], 'negative'),
('unsqueeze', new_t(2, 3, 4), lambda t: [2],),
('unsqueeze', new_t(2, 3, 4), lambda t: [-2], 'neg_dim'),
('view', small_3d, lambda t: [100, 10], 'contiguous'),
('view_as', small_3d, lambda t: [make_tensor(t, 100, 10)],),
('zero', small_3d, lambda t: [],),
('zeros', small_3d, lambda t: [1, 2, 3, 4],),
('eye', small_2d, lambda t: [3, 4],),
('flip', small_3d, lambda t: [0], 'd0', types, True),
('flip', small_3d, lambda t: [0, 1, 2], 'd012', types, True),
('flip', small_3d, lambda t: [0, 2], 'd02', types, True),
('flip', small_3d, lambda t: [2, 0], 'd20', types, True),
('flip', small_3d, lambda t: [-1], 'neg_d', types, True),
('rot90', small_2d, lambda t: [1, [0, 1]], 'k1_d01', types, True),
('rot90', small_3d, lambda t: [1, [1, 2]], 'k1_d12', types, True),
('rot90', small_3d, lambda t: [1, [1, -1]], 'k1_neg_d', types, True),
('rot90', small_3d, lambda t: [], 'default', types, True),
('rsqrt', lambda t: constant_tensor_add(1, small_3d(t)), lambda t: [], None, float_types),
('sinh', lambda t: tensor_clamp(small_3d(t), -1, 1), lambda t: [], None, float_types),
('tan', lambda t: tensor_clamp(small_3d(t), -1, 1), lambda t: [], None, float_types),
('__lshift__', lambda t: torch.pow(2, cast_tensor(torch.arange(1, 5), t)),
lambda t: [2], None, signed_types),
('__rshift__', lambda t: torch.pow(2, cast_tensor(torch.arange(3, 7), t)),
lambda t: [2], None, signed_types),
# lapack tests
('qr', small_2d_lapack, lambda t: [], 'square', float_types, False,
unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")),
('qr', small_2d_lapack_skinny, lambda t: [], 'skinny', float_types, False,
unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")),
('qr', small_2d_lapack_fat, lambda t: [], 'fat', float_types, False,
unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")),
('qr', large_2d_lapack, lambda t: [], 'big', float_types, False,
unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")),
('geqrf', new_t(20, 20), lambda t: [], None, float_types, False,
unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")),
('svd', new_t(10, 10), lambda t: [], 'square', float_types_no_half, False,
unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")),
('svd', lambda t: new_t(10, 10)(t).t(), lambda t: [True], 'square_col_maj',
float_types_no_half, False,
unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")),
('svd', new_t(20, 5), lambda t: [True], 'tall_some', float_types_no_half, False,
unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")),
('svd', new_t(20, 5), lambda t: [False], 'tall_all', float_types_no_half, False,
unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")),
('svd', lambda t: new_t(5, 20)(t).t(), lambda t: [True],
'tall_some_col_maj', float_types_no_half, False,
unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")),
('svd', lambda t: new_t(5, 20)(t).t(), lambda t: [False],
'tall_all_col_maj', float_types_no_half, False,
unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")),
('eig', new_t(10, 10), lambda t: [True], 'with_eigvec', float_types_no_half, False,
unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")),
]
# TODO: random functions, cat, gather, scatter, index*, masked*,
# resize, resizeAs, storage_offset, storage, stride, unfold
custom_precision = {
'addbmm': 1e-4,
'addmm': 1e-4,
'addmv': 1e-4,
'addr': 1e-4,
'baddbmm': 1e-4,
'rsqrt': 1e-4,
'cumprod': 1e-4,
'qr': 3e-4,
'digamma': 1e0, # large values lead to large absolute error but small relative error
}
custom_half_precision = {
'add': 1e-2,
'acos': 1e-3,
'addbmm': 1e-1,
'addcdiv': 1e-2,
'addcmul': 1e-2,
'addmm': 1e-1,
'addmv': 1e-2,
'addr': 1e-2,
'asin': 1e-3,
'atan2': 1e-3,
'atan': 1e-3,
'baddbmm': 1e-2,
'cos': 1e-3,
'cosh': 1e-2,
'cross': 1e-2,
'cumprod': 1e-2,
'cumsum': 1e-2,
'dist': 1e-2,
'div': 1e-3,
'dot': 1e-2,
'erf': 1e-3,
'erfc': 1e-3,
'erfinv': 1e-3,
'exp': 1e-2,
'expm1': 1e-2,
'fill': 1e-3,
'lerp': 1e-2,
'lgamma': 1e-2,
'log': 1e-2,
'log10': 1e-2,
'log1p': 1e-3,
'log2': 1e-2,
'mean': 1e-3,
'mul': 1e-2,
'norm': 1e-1,
'pow': 1e-1,
'prod': 1e-3,
'reciprocal': 1e-1,
'remainder': 1e-3,
'renorm': 1e-3,
'rsqrt': 1e-2,
'sigmoid': 1e-3,
'sin': 1e-3,
'sinh': 1e-3,
'sqrt': 1e-3,
'std': 1e-3,
'sub': 1e-2,
'sum': 1e-2,
'tan': 1e-3,
'tanh': 1e-3,
'trace': 1e-3,
'var': 1e-3,
'__lshift__': 1e-3,
'__rshift__': 1e-3,
}
simple_pointwise = [
'abs',
'sign',
]
for fn in simple_pointwise:
tests.append((fn, small_3d, lambda t: []))
simple_pointwise_float = [
'log',
'log10',
'log1p',
'log2',
'sigmoid',
'sin',
'sqrt',
'tanh',
'acos',
'asin',
'atan',
'cos',
'cosh',
'erf',
'erfc',
'erfinv',
'exp',
'expm1',
'reciprocal',
'floor',
'frac',
'neg',
'round',
'trunc',
'ceil',
'lgamma',
'digamma',
'trigamma',
]
for fn in simple_pointwise_float:
tests.append((fn, small_3d, lambda t: [], None, float_types))
_cycles_per_ms = None
def get_cycles_per_ms():
"""Approximate number of cycles per millisecond for torch.cuda._sleep"""
global _cycles_per_ms
if _cycles_per_ms is None:
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
start.record()
torch.cuda._sleep(1000000)
end.record()
end.synchronize()
_cycles_per_ms = 1000000 / start.elapsed_time(end)
return _cycles_per_ms
def compare_cpu_gpu(tensor_constructor, arg_constructor, fn, t, precision=1e-5):
def tmp(self):
cpu_tensor = tensor_constructor(t)
gpu_tensor = to_gpu(cpu_tensor)
cpu_args = arg_constructor(t)
gpu_args = [to_gpu(arg) for arg in cpu_args]
if is_half(t):
cpu_tensor = cpu_tensor.float()
cpu_args = [arg.float() if isinstance(arg, torch.Tensor) and is_half(arg) else arg for arg in cpu_args]
cpu_result = getattr(cpu_tensor, fn)(*cpu_args)
try:
gpu_result = getattr(gpu_tensor, fn)(*gpu_args)
except RuntimeError as e:
reason = e.args[0]
data_type_reasons = {'only supports floating-point types',
'unimplemented data type',
'not implemented for'}
if any(data_type_reason in reason for data_type_reason in data_type_reasons):
raise unittest.SkipTest('unimplemented data type')
raise
except AttributeError as e:
reason = e.args[0]
if 'object has no attribute' in reason:
raise unittest.SkipTest('unimplemented data type')
raise
# If one changes, another should change as well
self.assertEqual(cpu_tensor, gpu_tensor, precision)
self.assertEqual(cpu_args, gpu_args, precision)
# Compare results
if fn == 'element_size' and t.__name__ == 'HalfTensor':
# Workaround since cpu_result is float
self.assertEqual(2, gpu_result)
else:
self.assertEqual(cpu_result, gpu_result, precision)
return tmp
class TestCuda(TestCase):
_do_cuda_memory_leak_check = True
# See https://github.com/pytorch/pytorch/issues/21589
# We used to have this turned on for the tests in this file which
# we had tested to be OK, but when people added new tests to
# this file, it would trigger nondeterministic failures that
# are hard to debug. Since there are KNOWN bugs with our
# stream handling, we shouldn't turn this on by default.
# If you decide to make this True, be sure to run the test suite
# under cuda-memcheck
_do_cuda_non_default_stream = False
FIFTY_MIL_CYCLES = 50000000
@staticmethod
def _test_memory_stats_generator(self, device=None, N=35):
if device is None:
device = torch.cuda.current_device()
m0 = torch.cuda.memory_allocated(device)
last_m_arr = [torch.cuda.memory_allocated(device)]
max_m_arr = [torch.cuda.max_memory_allocated(device)]
last_c_arr = [torch.cuda.memory_cached(device)]
max_c_arr = [torch.cuda.max_memory_cached(device)]
def alloc(*size):
with torch.cuda.device(device):
# NOTE: do **not** use methods that can have additional
# memory overhead, e.g., inplace random sampling methods.
# they can leave some memory occupied even after being
# deallocated, e.g., initialized RNG state, causing some
# memory checks below to fail.
return torch.cuda.FloatTensor(*size)
def assert_change(comp=1, empty_cache=False, reset_max_alloc=False, reset_max_cached=False):
# comp > 0: increased
# comp = 0: equal
# comp < 0: decreased
new_m = torch.cuda.memory_allocated(device)
new_max_m = torch.cuda.max_memory_allocated(device)
if comp > 0:
self.assertGreater(new_m, last_m_arr[0])
elif comp < 0:
self.assertLess(new_m, last_m_arr[0])
else:
self.assertEqual(new_m, last_m_arr[0])
self.assertLessEqual(new_m, new_max_m)
self.assertGreaterEqual(new_max_m, max_m_arr[0])
last_m_arr[0] = new_m
max_m_arr[0] = new_max_m
new_c = torch.cuda.memory_cached(device)
new_max_c = torch.cuda.max_memory_cached(device)
# emptying cache may happen (due to allocation or empty_cache), so
# we can't assert new_c >= last_c
self.assertLessEqual(new_c, new_max_c)
self.assertGreaterEqual(new_max_c, max_c_arr[0])
last_c_arr[0] = new_c
max_c_arr[0] = new_max_c
if empty_cache:
torch.cuda.empty_cache()
new_c = torch.cuda.memory_cached(device)
new_max_c = torch.cuda.max_memory_cached(device)
self.assertLessEqual(new_c, last_c_arr[0])
self.assertLessEqual(new_c, new_max_c)
self.assertEqual(new_max_c, max_c_arr[0])
last_c_arr[0] = new_c
if reset_max_alloc:
torch.cuda.reset_max_memory_allocated(device)
self.assertEqual(torch.cuda.memory_allocated(device), last_m_arr[0])
self.assertEqual(torch.cuda.max_memory_allocated(device), last_m_arr[0])
max_m_arr[0] = last_m_arr[0]
self.assertEqual(torch.cuda.memory_cached(device), last_c_arr[0])
self.assertEqual(torch.cuda.max_memory_cached(device), max_c_arr[0])
if reset_max_cached:
torch.cuda.reset_max_memory_cached(device)
self.assertEqual(torch.cuda.memory_allocated(device), last_m_arr[0])
self.assertEqual(torch.cuda.max_memory_allocated(device), max_m_arr[0])
self.assertEqual(torch.cuda.memory_cached(device), last_c_arr[0])
self.assertEqual(torch.cuda.max_memory_cached(device), last_c_arr[0])
max_c_arr[0] = last_c_arr[0]
assert_change(0)
assert_change(0, reset_max_alloc=True)
assert_change(0, empty_cache=True)
assert_change(0, reset_max_cached=True)
assert_change(0)
yield
tensors1 = [alloc(1), alloc(10, 20), alloc(200, 300, 2000)]
m1 = torch.cuda.memory_allocated(device)
assert_change(1)
yield
tensors2 = []
for i in range(1, int(N / 2) + 1):
# small ones
tensors2.append(alloc(i, i * 4))
assert_change(1)
yield
for i in range(5, int(N / 2) + 5):
# large ones
tensors2.append(alloc(i, i * 7, i * 9, i * 11))
assert_change(1, reset_max_alloc=(i % 2 == 0), reset_max_cached=(i % 2 == 1))
yield
tensors2.append(alloc(0, 0, 0))
assert_change(0)
yield
permute = []
for i in torch.randperm(len(tensors2)):
permute.append(tensors2[i])
assert_change(0)
yield
del tensors2
assert_change(0)
yield
tensors2 = permute
assert_change(0)
yield
del permute
assert_change(0, reset_max_alloc=True)
yield
for i in range(int(N / 2)):
x = tensors2[i].numel()
del tensors2[i]
assert_change(-x) # in case that tensors2[i] is empty
yield
for i in range(2, int(2 * N / 3) + 2):
tensors2.append(alloc(i, i * 3, i * 8))
assert_change(1)
yield
del tensors2
assert_change(-1, reset_max_cached=True)
assert_change(0)
self.assertEqual(torch.cuda.memory_allocated(device), m1)
yield True
del tensors1
assert_change(-1, reset_max_alloc=True)
self.assertEqual(torch.cuda.memory_allocated(device), m0)
# test empty_cache and reset_max_memory_*
assert_change(0, empty_cache=True)
assert_change(0, reset_max_cached=True)
assert_change(0, reset_max_alloc=True)
def test_memory_stats(self):
torch.cuda.empty_cache()
for _ in self._test_memory_stats_generator(self):
pass
def test_cuda_get_device_name(self):
# Testing the behaviour with None as an argument
current_device = torch.cuda.current_device()
current_device_name = torch.cuda.get_device_name(current_device)
device_name_None = torch.cuda.get_device_name(None)
self.assertEqual(current_device_name, device_name_None)
# Testing the behaviour for No argument
device_name_no_argument = torch.cuda.get_device_name()
self.assertEqual(current_device_name, device_name_no_argument)
def test_cuda_get_device_capability(self):
# Testing the behaviour with None as an argument
current_device = torch.cuda.current_device()
current_device_capability = torch.cuda.get_device_capability(current_device)
device_capability_None = torch.cuda.get_device_capability(None)
self.assertEqual(current_device_capability, device_capability_None)
# Testing the behaviour for No argument
device_capability_no_argument = torch.cuda.get_device_capability()
self.assertEqual(current_device_capability, device_capability_no_argument)
@unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
def test_memory_stats_multigpu(self):
# advance a generator with a end flag
def advance(gen, end):
if not end:
try:
next(gen)
except StopIteration:
end = True
return end
# interlace
torch.cuda.empty_cache()
gen0 = self._test_memory_stats_generator(self, device='cuda:0', N=35)
gen1 = self._test_memory_stats_generator(self, device=torch.device('cuda:1'), N=35)
end0 = end1 = False
while not (end0 and end1):
end0 = advance(gen0, end0)
end1 = advance(gen1, end1)
# semi-random order
torch.cuda.empty_cache()
gen0 = self._test_memory_stats_generator(self, device=0, N=35)
gen1 = self._test_memory_stats_generator(self, device=torch.device('cuda:1'), N=35)
end0 = end1 = False
while not (end0 and end1):
end0 = advance(gen0, end0)
if not end0:
gen1_max_times = torch.LongTensor(1).random_(0, 3)[0]
else:
gen1_max_times = inf
t = 0
while t < gen1_max_times and not end1:
end1 = advance(gen1, end1)
t += 1
def test_out_of_memory(self):
tensor = torch.zeros(1024, device='cuda')
with self.assertRaisesRegex(RuntimeError, "Tried to allocate 80.00 GiB"):
torch.empty(1024 * 1024 * 1024 * 80, dtype=torch.int8, device='cuda')
# ensure out of memory error doesn't disturb subsequent kernel
tensor.fill_(1)
self.assertTrue((tensor == 1).all())
@unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
def test_autogpu(self):
x = torch.randn(5, 5).cuda()
y = torch.randn(5, 5).cuda()
self.assertEqual(x.get_device(), 0)
self.assertEqual(x.get_device(), 0)
with torch.cuda.device(1):
z = torch.randn(5, 5).cuda()
self.assertEqual(z.get_device(), 1)
q = x.add(y)
self.assertEqual(q.get_device(), 0)
w = torch.randn(5, 5).cuda()
self.assertEqual(w.get_device(), 1)
self.assertEqual(y.cuda().get_device(), 1)
z = z.cuda()
self.assertEqual(z.get_device(), 0)
@unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
def test_new(self):
x = torch.randn(3, 3).cuda()
self.assertEqual(x.new([0, 1, 2]).get_device(), 0)
self.assertEqual(x.new([0, 1, 2], device=1).get_device(), 1)
with torch.cuda.device(1):
self.assertEqual(x.new([0, 1, 2]).get_device(), 0)
self.assertEqual(x.new([0, 1, 2], device=1).get_device(), 1)
@unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
def test_copy_device(self):
x = torch.randn(5, 5).cuda()
with torch.cuda.device(1):
y = x.cuda()
self.assertEqual(y.get_device(), 1)
self.assertIs(y.cuda(), y)
z = y.cuda(0)
self.assertEqual(z.get_device(), 0)
self.assertIs(z.cuda(0), z)
x = torch.randn(5, 5)
with torch.cuda.device(1):
y = x.cuda()
self.assertEqual(y.get_device(), 1)
self.assertIs(y.cuda(), y)
z = y.cuda(0)
self.assertEqual(z.get_device(), 0)
self.assertIs(z.cuda(0), z)
def _test_copy_sync_current_stream(self, x, y):
x_plus_one = x + 1
s0 = torch.cuda.Stream(device=x.device)
s1 = torch.cuda.Stream(device=y.device)
s2 = torch.cuda.Stream(device=x.device)
s3 = torch.cuda.Stream(device=y.device)
# same dst stream different src streams
with torch.cuda.stream(s0):
torch.cuda._sleep(TestCuda.FIFTY_MIL_CYCLES)
with torch.cuda.stream(s1):
y.copy_(x_plus_one)
with torch.cuda.stream(s2), torch.cuda.stream(s1):
y.copy_(x)
s1.synchronize()
# The copy() is synchronized on the current streams of both src and dst.
# In the above test, the _sleep() op on s0 will not block the copy() on
# s2, but both copies are synchronized on s1 in the dst device. Hence,
# x is copied to y after x_plus_one is copied to y. If x and y are on
# the same device, both copy() ops are synchronized on s1.
self.assertEqual(y, x)
# same src stream different dst streams
with torch.cuda.stream(s1):
torch.cuda._sleep(TestCuda.FIFTY_MIL_CYCLES)
with torch.cuda.stream(s0):
y.copy_(x_plus_one)
with torch.cuda.stream(s3), torch.cuda.stream(s0):
y.copy_(x)
s0.synchronize()
# Similarly, both copy() ops are synchronized on s0.
self.assertEqual(y, x)
@unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
def test_copy_streams(self):
d0 = torch.device('cuda:0')
x0 = torch.zeros(5, 5, device=d0)
d1 = torch.device('cuda:1')
x1 = torch.zeros(5, 5, device=d1)
self._test_copy_sync_current_stream(x0, x1)
x2 = torch.zeros(5, 5, device=d0)
self._test_copy_sync_current_stream(x0, x2)
def test_copy_non_blocking(self):
def _test_copy_non_blocking(a, b):
event = torch.cuda.Event()
a.copy_(b, non_blocking=True)
event.record()
self.assertFalse(event.query())
event.synchronize()
self.assertEqual(a, b)
# 10MB copies
x = torch.ones(10000000, dtype=torch.uint8).cuda()
y = torch.zeros(10000000, dtype=torch.uint8).pin_memory()
_test_copy_non_blocking(x, y)
x = torch.zeros(10000000, dtype=torch.uint8).pin_memory()
y = torch.ones(10000000, dtype=torch.uint8).cuda()
_test_copy_non_blocking(x, y)
def test_copy_broadcast(self):
x = torch.randn(10, 5)
y = torch.randn(5, device='cuda')
x.copy_(y)
self.assertEqual(x[3], y.cpu())
x = torch.randn(10, 5, device='cuda')
y = torch.randn(5)
x.copy_(y)
self.assertEqual(x[3].cpu(), y)
def test_copy_noncontig(self):
def do_test(d0, d1):
x = torch.tensor([1.5, 2.5, 3.5, 4.5, 5.5, 6.5], device=d0)
y = torch.tensor([0, 0, 0, 0, 0, 0], device=d1)
self.assertNotEqual(x.dtype, y.dtype)
y[::2].copy_(x[::2])
self.assertEqual(y, [1, 0, 3, 0, 5, 0])
do_test('cpu', 'cuda')
do_test('cuda', 'cpu')
if TEST_MULTIGPU:
do_test('cuda:0', 'cuda:1')
def test_serialization_array_with_storage(self):
x = torch.randn(5, 5).cuda()
y = torch.IntTensor(2, 5).fill_(0).cuda()
q = [x, y, x, y.storage()]
with tempfile.NamedTemporaryFile() as f:
torch.save(q, f)
f.seek(0)
q_copy = torch.load(f)
self.assertEqual(q_copy, q, 0)
q_copy[0].fill_(5)
self.assertEqual(q_copy[0], q_copy[2], 0)
self.assertTrue(isinstance(q_copy[0], torch.cuda.DoubleTensor))
self.assertTrue(isinstance(q_copy[1], torch.cuda.IntTensor))
self.assertTrue(isinstance(q_copy[2], torch.cuda.DoubleTensor))
self.assertTrue(isinstance(q_copy[3], torch.cuda.IntStorage))
q_copy[1].fill_(10)
self.assertTrue(q_copy[3], torch.cuda.IntStorage(10).fill_(10))
def test_type_conversions(self):
x = torch.randn(5, 5)
self.assertIsInstance(x.float(), torch.FloatTensor)
self.assertIsInstance(x.cuda(), torch.cuda.DoubleTensor)
self.assertIsInstance(x.cuda().float(), torch.cuda.FloatTensor)
self.assertIsInstance(x.cuda().float().cpu(), torch.FloatTensor)
self.assertIsInstance(x.cuda().float().cpu().int(), torch.IntTensor)
y = x.storage()
self.assertIsInstance(y.float(), torch.FloatStorage)
self.assertIsInstance(y.cuda(), torch.cuda.DoubleStorage)
self.assertIsInstance(y.cuda().float(), torch.cuda.FloatStorage)
self.assertIsInstance(y.cuda().float().cpu(), torch.FloatStorage)
self.assertIsInstance(y.cuda().float().cpu().int(), torch.IntStorage)
def test_mul_intertype_scalar(self):
def test_mul(dtype):
x = torch.tensor(1.5, dtype=dtype, device='cuda')
y = torch.tensor(3, dtype=torch.int32, device='cuda')
self.assertEqual(x * y, 4.5)
self.assertEqual(y * x, 4.5)
with self.assertRaisesRegex(RuntimeError, "doesn't match the desired"):
y *= x
x *= y
self.assertEqual(x, 4.5)
test_mul(torch.float16)
test_mul(torch.float32)
test_mul(torch.float64)
@unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
def test_type_conversions_same_gpu(self):
x = torch.randn(5, 5).cuda(1)
self.assertEqual(x.int().get_device(), 1)
self.assertEqual(x.type(torch.int).get_device(), 1)
self.assertEqual(x.to(torch.int).get_device(), 1)
def test_abs_zero(self):
# Both abs(0.0) and abs(-0.0) should result in 0.0
for dtype in (torch.float, torch.double):
abs_zeros = torch.tensor([0.0, -0.0], device='cuda', dtype=dtype).abs().tolist()
for num in abs_zeros:
self.assertGreater(math.copysign(1.0, num), 0.0)
def test_neg(self):
_TestTorchMixin._test_neg(self, lambda t: t.cuda())
def test_bitwise_not(self):
_TestTorchMixin._test_bitwise_not(self, 'cuda')
def test_isinf(self):
_TestTorchMixin._test_isinf(self, lambda t: t.cuda())
def test_inplace_unary_mem_overlap(self):
_TestTorchMixin._test_inplace_unary_mem_overlap(self, device='cuda')
def test_inplace_binary_mem_overlap(self):
_TestTorchMixin._test_inplace_binary_mem_overlap(self, device='cuda')
@unittest.skipIf(not TEST_LARGE_TENSOR, "not enough memory")
def test_arithmetic_large_tensor(self):
x = torch.empty(2**30, device='cuda')
x.fill_(1)
self.assertEqual(x.sum(), 2**30)
x += 1
self.assertEqual(x.sum(), 2**31)
x.fill_(1)
x -= 0.5
self.assertEqual(x.sum(), 2**29)
x.fill_(1)
x *= 2
self.assertEqual(x.sum(), 2**31)
x.fill_(1)
x /= 2
self.assertEqual(x.sum(), 2**29)
def _test_broadcast(self, input):
if not TEST_MULTIGPU:
raise unittest.SkipTest("only one GPU detected")
result = comm.broadcast(input, (0, 1))
for i, t in enumerate(result):
self.assertEqual(t.get_device(), i)
self.assertEqual(t, input)
if input.is_cuda and input.get_device() == i:
self.assertEqual(t.data_ptr(), input.data_ptr())
def test_broadcast_cpu(self):
self._test_broadcast(torch.randn(5, 5))
def test_broadcast_gpu(self):
self._test_broadcast(torch.randn(5, 5).cuda())
def test_min_max_nan(self):
tests = [(lambda x: x.min(), 'min'),
(lambda x: x.max(), 'max'),
(lambda x: x.min(0)[0], 'min_dim'),
(lambda x: x.max(0)[0], 'max_dim')]
for f, name in tests:
a = torch.arange(25.0).view(5, 5)
a[2, 2] = nan
actual = f(a.cuda()).cpu()
expected = f(a).cpu()
self.assertEqual(torch.isnan(actual), torch.isnan(expected), 'nans for {}'.format(name))
self.assertEqual(actual[~torch.isnan(actual)],
expected[~torch.isnan(expected)], 'nans for {}'.format(name))
@staticmethod
def _test_broadcast_coalesced(self, tensors, buffer_size):
b_tensors = [comm.broadcast(t, (0, 1)) for t in tensors]
for (_, bt), t in zip(b_tensors, tensors):
self.assertEqual(bt.get_device(), 1)
self.assertEqual(bt, t)
self.assertIsInstance(bt, type(t))
bc_tensors = comm.broadcast_coalesced(tensors, (0, 1), buffer_size=buffer_size)
bc_tensors_t = list(zip(*bc_tensors))
self.assertEqual(b_tensors, bc_tensors_t)
for (_, bt), (_, bct) in zip(b_tensors, bc_tensors_t):
self.assertEqual(bt.get_device(), bct.get_device())
self.assertIsInstance(bct, type(bt))
# check that tensors on device[0] are returned as-is
for out_tensors in (b_tensors, bc_tensors_t):
for inp_t, (out_t, _) in zip(tensors, out_tensors):
self.assertIs(inp_t, out_t)
# check that the tensors not on device[0] have different version counters
# NOTE [ Version Counter in comm.*_coalesced ]
versions = [t._version for _, t in bc_tensors_t]
for old_version, (_, t) in zip(versions, bc_tensors_t):
self.assertEqual(t._version, old_version)
t.zero_()
self.assertEqual(t._version, old_version + 1)
@unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
# Note: fails sometimes on the CI, passes on dual gfx906
def test_broadcast_coalesced(self):
numel = 5
num_bytes = numel * 8
tensors = [
make_sparse_tensor(torch.cuda.sparse.DoubleTensor, 1, 2, 3),
torch.randn(numel).long().cuda(),
torch.randn(numel).cuda(),
make_sparse_tensor(torch.cuda.sparse.DoubleTensor, 10, 2, 3),
make_sparse_tensor(torch.cuda.sparse.DoubleTensor, 5, 2, 3),
make_sparse_tensor(torch.cuda.sparse.LongTensor, 7, 3, 3),
make_sparse_tensor(torch.cuda.sparse.FloatTensor, 2, 2, 3),
torch.randn(numel).long().cuda(),
torch.randn(numel).long().cuda(),
make_sparse_tensor(torch.cuda.sparse.LongTensor, 3, 2, 7),
torch.randn(numel * 2).int().cuda(), # int is 2x shorter
torch.randn(numel).cuda(),
]
self._test_broadcast_coalesced(self, tensors, num_bytes * 5 // 2)
@unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
def test_broadcast_coalesced_dense_only(self):
numel = 5
num_bytes = numel * 8
tensors = [
torch.randn(numel).long().cuda(),
torch.randn(numel).cuda(),
torch.randn(numel).long().cuda(),
torch.randn(numel).long().cuda(),
torch.randn(numel * 2).int().cuda(), # int is 2x shorter
torch.randn(numel).cuda(),
]
self._test_broadcast_coalesced(self, tensors, num_bytes * 5 // 2)
@unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
def test_reduce_add(self):
x = torch.randn(5, 5)
y = torch.randn(5, 5)
x_cuda = x.cuda(0)
y_cuda = y.cuda(1)
result = comm.reduce_add((x_cuda, y_cuda))
self.assertEqual(result.get_device(), 0)
self.assertEqual(result.cpu(), x + y)
@staticmethod
def _test_reduce_add_coalesced(self, tensors, buffer_size):
dup_tensors = [tensors, list(map(lambda t: t.cuda(1), tensors))]
r_tensors = list(map(comm.reduce_add, zip(*dup_tensors)))
for r, t in zip(r_tensors, tensors):
self.assertEqual(r.get_device(), t.get_device())
self.assertEqual(r, t * 2)
self.assertEqual(r.type(), t.type())
rc_tensors = comm.reduce_add_coalesced(dup_tensors, buffer_size=buffer_size)
self.assertEqual(r_tensors, rc_tensors)
for r, rc in zip(r_tensors, rc_tensors):
self.assertEqual(rc.get_device(), r.get_device())
self.assertEqual(rc.type(), r.type())
# Since we have both cuda:0 and cuda:1 inputs, the outputs must be new.
# We can check that they have different version counters.
# NOTE [ Version Counter in comm.*_coalesced ]
versions = [t._version for t in rc_tensors]
for old_version, t in zip(versions, rc_tensors):
self.assertEqual(t._version, old_version)
t.zero_()
self.assertEqual(t._version, old_version + 1)
@unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
def test_reduce_add_coalesced(self):
numel = 5
num_bytes = numel * 8
tensors = [
make_sparse_tensor(torch.cuda.sparse.DoubleTensor, 1, 2, 3),
torch.randn(numel).long().cuda(),
torch.randn(numel).cuda(),
make_sparse_tensor(torch.cuda.sparse.DoubleTensor, 10, 2, 3),
make_sparse_tensor(torch.cuda.sparse.DoubleTensor, 5, 2, 3),
make_sparse_tensor(torch.cuda.sparse.LongTensor, 7, 3, 3),
make_sparse_tensor(torch.cuda.sparse.FloatTensor, 2, 2, 3),
torch.randn(numel).long().cuda(),
torch.randn(numel).long().cuda(),
make_sparse_tensor(torch.cuda.sparse.LongTensor, 3, 2, 7),
torch.randn(numel * 2).int().cuda(), # int is 2x shorter
torch.randn(numel).cuda(),
]
self._test_reduce_add_coalesced(self, tensors, num_bytes * 5 // 2)
@unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
def test_reduce_add_coalesced_dense_only(self):
numel = 5
num_bytes = numel * 8
tensors = [
torch.randn(numel).long().cuda(),
torch.randn(numel).cuda(),
torch.randn(numel).long().cuda(),
torch.randn(numel).long().cuda(),
torch.randn(numel * 2).int().cuda(), # int is 2x shorter
torch.randn(numel).cuda(),
]
self._test_reduce_add_coalesced(self, tensors, num_bytes * 5 // 2)
def _test_scatter(self, input, chunk_sizes=None, dim=0):
if not TEST_MULTIGPU:
raise unittest.SkipTest("only one GPU detected")
result = comm.scatter(input, (0, 1), chunk_sizes, dim)
self.assertEqual(len(result), 2)
if chunk_sizes is None:
chunk_sizes = tuple(repeat(input.size(dim) // 2, 2))
chunk_start = 0
for i, r in enumerate(result):
chunk_end = chunk_start + chunk_sizes[i]
index = [slice(None, None), slice(None, None)]
index[dim] = slice(chunk_start, chunk_end)
self.assertEqual(r, input[tuple(index)], 0)
chunk_start = chunk_end
def test_scatter_cpu(self):
self._test_scatter(torch.randn(4, 4), dim=0)
def test_scatter_cpu_dim(self):
self._test_scatter(torch.randn(4, 4), dim=1)
def test_scatter_cpu_neg_dim(self):
self._test_scatter(torch.randn(4, 4), dim=-2)
def test_scatter_cpu_sizes(self):
self._test_scatter(torch.randn(6, 4), chunk_sizes=(2, 4))
def test_scatter_gpu(self):
self._test_scatter(torch.randn(4, 4).cuda(), dim=0)
def test_scatter_gpu_dim(self):
self._test_scatter(torch.randn(4, 4).cuda(), dim=1)
def test_scatter_gpu_neg_dim(self):
self._test_scatter(torch.randn(4, 4).cuda(), dim=-2)
def test_scatter_gpu_sizes(self):
self._test_scatter(torch.randn(6, 4).cuda(), chunk_sizes=(2, 4))
def _test_gather(self, dim):
if not TEST_MULTIGPU:
raise unittest.SkipTest("only one GPU detected")
x = torch.randn(2, 5).cuda(0)
y = torch.randn(2, 5).cuda(1)
result = comm.gather((x, y), dim)
expected_size = list(x.size())
expected_size[dim] += y.size(dim)
expected_size = torch.Size(expected_size)
self.assertEqual(result.get_device(), 0)
self.assertEqual(result.size(), expected_size)
index = [slice(None, None), slice(None, None)]
index[dim] = slice(0, x.size(dim))
self.assertEqual(result[tuple(index)], x)
index[dim] = slice(x.size(dim), x.size(dim) + y.size(dim))
self.assertEqual(result[tuple(index)], y)
# Bool test case
t = torch.tensor([[False, True], [True, True]], device='cuda')
self.assertEqual(torch.gather(t, 1, torch.tensor([[0, 0], [1, 0]], device='cuda')),
torch.tensor([[False, False], [True, True]], device='cuda'))
def test_gather(self):
self._test_gather(0)
def test_gather_dim(self):
self._test_gather(1)
def test_from_sequence(self):
seq = [list(range(i * 4, i * 4 + 4)) for i in range(5)]
reference = torch.arange(0, 20).resize_(5, 4)
for t in types:
cuda_type = get_gpu_type(t)
self.assertEqual(cuda_type(seq), reference)
def test_torch_manual_seed_seeds_cuda_devices(self):
with freeze_rng_state():
x = torch.zeros(4, 4).float().cuda()
torch.manual_seed(2)
self.assertEqual(torch.cuda.initial_seed(), 2)
x.uniform_()
torch.manual_seed(2)
y = x.clone().uniform_()
self.assertEqual(x, y)
self.assertEqual(torch.cuda.initial_seed(), 2)
def test_manual_seed(self):
with freeze_rng_state():
x = torch.zeros(4, 4).float().cuda()
torch.cuda.manual_seed(2)
self.assertEqual(torch.cuda.initial_seed(), 2)
x.uniform_()
a = torch.bernoulli(torch.full_like(x, 0.5))
torch.cuda.manual_seed(2)
y = x.clone().uniform_()
b = torch.bernoulli(torch.full_like(x, 0.5))
self.assertEqual(x, y)
self.assertEqual(a, b)
self.assertEqual(torch.cuda.initial_seed(), 2)
@unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
def test_cat_autogpu(self):
x = torch.randn(4, 4).cuda(1)
y = torch.randn(4, 4).cuda(1)
z = torch.cat([x, y], 0)
self.assertEqual(z.get_device(), x.get_device())
def test_clamp(self):
_TestTorchMixin._test_clamp(self, 'cuda')
def test_cat(self):
SIZE = 10
for dim in range(-3, 3):
pos_dim = dim if dim >= 0 else 3 + dim
x = torch.rand(13, SIZE, SIZE).transpose(0, pos_dim).cuda()
y = torch.rand(17, SIZE, SIZE).transpose(0, pos_dim).cuda()
z = torch.rand(19, SIZE, SIZE).transpose(0, pos_dim).cuda()
res1 = torch.cat((x, y, z), dim)
self.assertEqual(res1.narrow(pos_dim, 0, 13), x, 0)
self.assertEqual(res1.narrow(pos_dim, 13, 17), y, 0)
self.assertEqual(res1.narrow(pos_dim, 30, 19), z, 0)
x = torch.randn(20, SIZE, SIZE).cuda()
self.assertEqual(torch.cat(torch.split(x, 7)), x)
self.assertEqual(torch.cat(torch.chunk(x, 7)), x)
y = torch.randn(1, SIZE, SIZE).cuda()
z = torch.cat([x, y])
self.assertEqual(z.size(), (21, SIZE, SIZE))
def test_cat_empty_legacy(self):
_TestTorchMixin._test_cat_empty_legacy(self, use_cuda=True)
def test_cat_empty(self):
_TestTorchMixin._test_cat_empty(self, use_cuda=True)
def test_bernoulli(self):
_TestTorchMixin._test_bernoulli(self, torch.float32, torch.float64, 'cuda')
_TestTorchMixin._test_bernoulli(self, torch.float32, torch.float16, 'cuda')
_TestTorchMixin._test_bernoulli(self, torch.float16, torch.float64, 'cuda')
_TestTorchMixin._test_bernoulli(self, torch.float16, torch.float16, 'cuda')
# test that it works with integral tensors
_TestTorchMixin._test_bernoulli(self, torch.uint8, torch.float64, 'cuda')
_TestTorchMixin._test_bernoulli(self, torch.uint8, torch.float16, 'cuda')
_TestTorchMixin._test_bernoulli(self, torch.int64, torch.float64, 'cuda')
_TestTorchMixin._test_bernoulli(self, torch.int64, torch.float16, 'cuda')
def test_cat_bad_input_sizes(self):
x = torch.randn(2, 1).cuda()
y = torch.randn(2, 1, 1).cuda()
z = torch.randn(2, 1, 1).cuda()
self.assertRaises(RuntimeError, lambda: torch.cat([x, y, z]))
x = torch.randn(2, 1, 2).cuda()
y = torch.randn(2, 1, 1).cuda()
z = torch.randn(2, 2, 1).cuda()
self.assertRaises(RuntimeError, lambda: torch.cat([x, y, z], dim=1))
@unittest.skipIf(torch.cuda.device_count() >= 10, "Loading a cuda:9 tensor")
@unittest.skipIf(not PY3, "Tensor was serialized with Python 3")
def test_load_nonexistent_device(self):
# Setup: create a serialized file object with a 'cuda:9' restore location
tensor = torch.randn(2, device='cuda')
buf = io.BytesIO()
torch.save(tensor, buf)
# NB: this might not work in the future if serialization changes
buf = io.BytesIO(buf.getvalue().replace(b'cuda:0', b'cuda:9'))
msg = r'Attempting to deserialize object on CUDA device 9'
with self.assertRaisesRegex(RuntimeError, msg):
_ = torch.load(buf)
def test_serialization(self):
x = torch.randn(4, 4).cuda()
with tempfile.NamedTemporaryFile() as f:
torch.save(x, f)
f.seek(0)
x_copy = torch.load(f)
self.assertEqual(x_copy, x)
self.assertIs(type(x_copy), type(x))
self.assertEqual(x_copy.get_device(), x.get_device())
def test_serialization_array_with_empty(self):
x = [torch.randn(4, 4).cuda(), torch.cuda.FloatTensor()]
with tempfile.NamedTemporaryFile() as f:
torch.save(x, f)
f.seek(0)
x_copy = torch.load(f)
for original, copy in zip(x, x_copy):
self.assertEqual(copy, original)
self.assertIs(type(copy), type(original))
self.assertEqual(copy.get_device(), original.get_device())
@unittest.skipIf(not TEST_MULTIGPU, "detected only one GPU")
def test_multigpu_serialization(self):
x = [torch.randn(4, 4).cuda(0), torch.randn(4, 4).cuda(1)]
with tempfile.NamedTemporaryFile() as f:
torch.save(x, f)
f.seek(0)
x_copy = torch.load(f)
for original, copy in zip(x, x_copy):
self.assertEqual(copy, original)
self.assertIs(type(copy), type(original))
self.assertEqual(copy.get_device(), original.get_device())
@unittest.skipIf(not TEST_MULTIGPU, "detected only one GPU")
def test_multigpu_serialization_remap(self):
x = [torch.randn(4, 4).cuda(0), torch.randn(4, 4).cuda(1)]
def gpu_remap(storage, location):
if location == 'cuda:1':
return storage.cuda(0)
with tempfile.NamedTemporaryFile() as f:
torch.save(x, f)
f.seek(0)
x_copy = torch.load(f, map_location=gpu_remap)
for original, copy in zip(x, x_copy):
self.assertEqual(copy, original)
self.assertIs(type(copy), type(original))
self.assertEqual(copy.get_device(), 0)
@unittest.skipIf(not TEST_MULTIGPU, "detected only one GPU")
def test_multigpu_serialization_remap_dict(self):
x = [torch.randn(4, 4).cuda(0), torch.randn(4, 4).cuda(1)]
with tempfile.NamedTemporaryFile() as f:
torch.save(x, f)
f.seek(0)
x_copy = torch.load(f, map_location={'cuda:1': 'cuda:0'})
for original, copy in zip(x, x_copy):
self.assertEqual(copy, original)
self.assertIs(type(copy), type(original))
self.assertEqual(copy.get_device(), 0)
@unittest.skipIf(not TEST_MULTIGPU, "detected only one GPU")
def test_multigpu_storage_clone(self):
x = torch.randn(4, 4, device='cuda:1').storage()
y = x.clone()
self.assertEqual(x.get_device(), y.get_device())
for t in ['byte', 'char', 'short', 'int', 'long', 'half', 'double']:
self.assertEqual(getattr(x, t)().get_device(), x.get_device())
@unittest.skipIf(not TEST_MULTIGPU, "detected only one GPU")
def test_cuda_set_device(self):
x = torch.randn(5, 5)
with torch.cuda.device(1):
self.assertEqual(x.cuda().get_device(), 1)
torch.cuda.set_device(0)
self.assertEqual(x.cuda().get_device(), 0)
with torch.cuda.device(1):
self.assertEqual(x.cuda().get_device(), 1)
self.assertEqual(x.cuda().get_device(), 0)
torch.cuda.set_device(1)
self.assertEqual(x.cuda().get_device(), 0)
def test_is_tensor(self):
for t in types:
tensor = get_gpu_type(t)()
self.assertTrue(torch.is_tensor(tensor))
self.assertTrue(torch.is_tensor(torch.cuda.HalfTensor()))
def test_cuda_synchronize(self):
torch.cuda.synchronize()
torch.cuda.synchronize('cuda')
torch.cuda.synchronize('cuda:0')
torch.cuda.synchronize(0)
torch.cuda.synchronize(torch.device('cuda:0'))
if TEST_MULTIGPU:
torch.cuda.synchronize('cuda:1')
torch.cuda.synchronize(1)
torch.cuda.synchronize(torch.device('cuda:1'))
with self.assertRaisesRegex(ValueError, "Expected a cuda device, but"):
torch.cuda.synchronize(torch.device("cpu"))
with self.assertRaisesRegex(ValueError, "Expected a cuda device, but"):
torch.cuda.synchronize("cpu")
@unittest.skipIf(not TEST_MULTIGPU, "detected only one GPU")
def test_current_stream(self):
d0 = torch.device('cuda:0')
d1 = torch.device('cuda:1')
s0 = torch.cuda.current_stream()
s1 = torch.cuda.current_stream(device=1)
s2 = torch.cuda.current_stream(device=0)
self.assertEqual(d0, s0.device)
self.assertEqual(d1, s1.device)
self.assertEqual(d0, s2.device)
self.assertEqual(s0, s2)
with torch.cuda.device(d1):
s0 = torch.cuda.current_stream()
s1 = torch.cuda.current_stream(1)
s2 = torch.cuda.current_stream(d0)
self.assertEqual(d1, s0.device)
self.assertEqual(d1, s1.device)
self.assertEqual(d0, s2.device)
self.assertEqual(s0, s1)
with self.assertRaisesRegex(ValueError,
"Expected a cuda device, but got: cpu"):
torch.cuda.current_stream(torch.device('cpu'))
@unittest.skipIf(not TEST_MULTIGPU, "detected only one GPU")
@skipCUDANonDefaultStreamIf(True)
def test_default_stream(self):
d0 = torch.device('cuda:0')
d1 = torch.device('cuda:1')
with torch.cuda.device(d0):
s0 = torch.cuda.default_stream()
with torch.cuda.device(d1):
s1 = torch.cuda.default_stream()
s2 = torch.cuda.default_stream(device=0)
s3 = torch.cuda.default_stream(d1)
self.assertEqual(d0, s0.device)
self.assertEqual(d1, s1.device)
self.assertEqual(d0, s2.device)
self.assertEqual(d1, s3.device)
self.assertEqual(s0, s2)
self.assertEqual(s1, s3)
with torch.cuda.device(d0):
self.assertEqual(torch.cuda.current_stream(), s0)
with torch.cuda.device(d1):
self.assertEqual(torch.cuda.current_stream(), s1)
with self.assertRaisesRegex(ValueError,
"Expected a cuda device, but got: cpu"):
torch.cuda.default_stream(torch.device('cpu'))
@skipCUDANonDefaultStreamIf(True)
def test_streams(self):
default_stream = torch.cuda.current_stream()
user_stream = torch.cuda.Stream()
self.assertEqual(torch.cuda.current_stream(), default_stream)
self.assertNotEqual(default_stream, user_stream)
self.assertEqual(default_stream.cuda_stream, 0)
self.assertNotEqual(user_stream.cuda_stream, 0)
with torch.cuda.stream(user_stream):
self.assertEqual(torch.cuda.current_stream(), user_stream)
self.assertTrue(user_stream.query())
# copy 10 MB tensor from CPU-GPU which should take some time
tensor1 = torch.ByteTensor(10000000).pin_memory()
tensor2 = tensor1.cuda(non_blocking=True)
self.assertFalse(default_stream.query())
default_stream.synchronize()
self.assertTrue(default_stream.query())
@unittest.skipIf(not TEST_MULTIGPU, "detected only one GPU")
def test_stream_event_device(self):
d0 = torch.device('cuda:0')
d1 = torch.device('cuda:1')
e0 = torch.cuda.Event()
self.assertEqual(None, e0.device)
with torch.cuda.device(d0):
s0 = torch.cuda.current_stream()
s0.record_event(e0)
with torch.cuda.device(d1):
s1 = torch.cuda.Stream()
e1 = s1.record_event()
self.assertEqual(s0.device, torch.device('cuda:0'))
self.assertEqual(e0.device, torch.device('cuda:0'))
self.assertEqual(s1.device, torch.device('cuda:1'))
self.assertEqual(e1.device, torch.device('cuda:1'))
def test_stream_event_repr(self):
s = torch.cuda.current_stream()
self.assertTrue("torch.cuda.Stream" in s.__repr__())
e = torch.cuda.Event()
self.assertTrue("torch.cuda.Event" in e.__repr__())
s.record_event(e)
self.assertTrue("torch.cuda.Event" in e.__repr__())
@unittest.skipIf(not TEST_MULTIGPU, "detected only one GPU")
# Note: fails sometimes on the CI, passes on dual gfx906
@skipIfRocm
def test_stream_context(self):
s0 = torch.cuda.current_stream()
s1 = torch.cuda.Stream(device=1)
s2 = torch.cuda.Stream(device=0)
with torch.cuda.device(s1.device):
prev_stream_on_cuda1 = torch.cuda.current_stream()
self.assertEqual(torch.cuda.current_stream(), s0)
self.assertEqual(0, torch.cuda.current_device())
with torch.cuda.stream(s1):
self.assertEqual(torch.cuda.current_stream(), s1)
self.assertEqual(1, torch.cuda.current_device())
with torch.cuda.stream(s2):
self.assertEqual(torch.cuda.current_stream(), s2)
self.assertEqual(0, torch.cuda.current_device())
with torch.cuda.stream(s0):
self.assertEqual(torch.cuda.current_stream(), s0)
self.assertEqual(0, torch.cuda.current_device())
self.assertEqual(torch.cuda.current_stream(), s2)
self.assertEqual(0, torch.cuda.current_device())
self.assertEqual(torch.cuda.current_stream(), s1)
self.assertEqual(1, torch.cuda.current_device())
with torch.cuda.device(s1.device):
self.assertEqual(prev_stream_on_cuda1, torch.cuda.current_stream())
self.assertEqual(torch.cuda.current_stream(), s0)
self.assertEqual(0, torch.cuda.current_device())
@unittest.skipIf(not TEST_MULTIGPU, "detected only one GPU")
def test_streams_multi_gpu(self):
default_stream = torch.cuda.current_stream()
self.assertEqual(default_stream.device, torch.device('cuda:0'))
stream = torch.cuda.Stream(device=1)
self.assertEqual(stream.device, torch.device('cuda:1'))
with torch.cuda.device(1):
self.assertEqual(
torch.cuda.current_stream().device, torch.device('cuda:1'))
self.assertNotEqual(torch.cuda.current_stream(), default_stream)
@unittest.skipIf(not TEST_MULTIGPU, "detected only one GPU")
def test_streams_multi_gpu_query(self):
d0 = torch.device('cuda:0')
d1 = torch.device('cuda:1')
torch.cuda.synchronize(d0)
torch.cuda.synchronize(d1)
with torch.cuda.device(d0):
s0 = torch.cuda.current_stream()
with torch.cuda.device(d1):
s1 = torch.cuda.current_stream()
torch.cuda._sleep(TestCuda.FIFTY_MIL_CYCLES)
self.assertTrue(s0.query())
self.assertFalse(s1.query())
with torch.cuda.device(d0):
self.assertTrue(s0.query())
self.assertFalse(s1.query())
with torch.cuda.device(d1):
self.assertTrue(s0.query())
self.assertFalse(s1.query())
# deliberately using a different device
with torch.cuda.device(d0):
s1.synchronize()
self.assertTrue(s0.query())
self.assertTrue(s1.query())
with torch.cuda.device(d0):
self.assertTrue(s0.query())
self.assertTrue(s1.query())
with torch.cuda.device(d1):
self.assertTrue(s0.query())
self.assertTrue(s1.query())
@unittest.skipIf(not TEST_MULTIGPU, "detected only one GPU")
def test_streams_multi_gpu_eq(self):
d0 = torch.device('cuda:0')
d1 = torch.device('cuda:1')
with torch.cuda.device(d0):
s0 = torch.cuda.current_stream()
s1 = torch.cuda.current_stream()
with torch.cuda.device(d1):
s2 = torch.cuda.current_stream()
s3 = torch.cuda.current_stream()
self.assertTrue(s0 == s0)
self.assertTrue(s0 == s1)
self.assertTrue(s2 == s2)
self.assertTrue(s2 == s3)
self.assertFalse(s0 == s2)
self.assertFalse(s1 == s3)
self.assertEqual(s0.device, s1.device)
self.assertEqual(s0.cuda_stream, s1.cuda_stream)
self.assertEqual(s2.device, s3.device)
self.assertEqual(s2.cuda_stream, s3.cuda_stream)
self.assertNotEqual(s0.device, s3.device)
self.assertEqual(hash(s0), hash(s1))
self.assertEqual(hash(s2), hash(s3))
self.assertNotEqual(hash(s0), hash(s3))
@unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
@skipIfRocm
def test_streams_priority(self):
low, high = torch.cuda.Stream.priority_range()
s0 = torch.cuda.Stream(device=0, priority=low)
self.assertEqual(low, s0.priority)
self.assertEqual(torch.device('cuda:0'), s0.device)
s1 = torch.cuda.Stream(device=1, priority=high)
self.assertEqual(high, s1.priority)
self.assertEqual(torch.device('cuda:1'), s1.device)
@unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
def test_tensor_device(self):
self.assertEqual(torch.cuda.FloatTensor(1).get_device(), 0)
self.assertEqual(torch.cuda.FloatTensor(1, device=1).get_device(), 1)
with torch.cuda.device(1):
self.assertEqual(torch.cuda.FloatTensor(1).get_device(), 1)
self.assertEqual(torch.cuda.FloatTensor(1, device=0).get_device(), 0)
self.assertEqual(torch.cuda.FloatTensor(1, device=None).get_device(), 1)
def test_events(self):
stream = torch.cuda.current_stream()
event = torch.cuda.Event(enable_timing=True)
self.assertTrue(event.query())
start_event = torch.cuda.Event(enable_timing=True)
stream.record_event(start_event)
torch.cuda._sleep(int(50 * get_cycles_per_ms()))
stream.record_event(event)
self.assertFalse(event.query())
event.synchronize()
self.assertTrue(event.query())
self.assertGreater(start_event.elapsed_time(event), 0)
@staticmethod
def _stream_synchronize(self, spin_time_cycles):
s = torch.cuda.current_stream()
e_tik = torch.cuda.Event(enable_timing=True)
e_tok = torch.cuda.Event(enable_timing=True)
e_tik.record(s)
torch.cuda._sleep(spin_time_cycles)
e_tok.record(s)
s.synchronize()
self.assertTrue(s.query())
# not necessary to check e_tik and e_tok, as elapsed_time would throw
# exception if otherwise.
return e_tik.elapsed_time(e_tok)
@staticmethod
def _event_synchronize(self, spin_time_cycles):
s = torch.cuda.current_stream()
e_tik = torch.cuda.Event(enable_timing=True)
e_tok = torch.cuda.Event(enable_timing=True)
e_tik.record(s)
torch.cuda._sleep(spin_time_cycles)
s.record_event(e_tok)
e_tok.synchronize()
self.assertTrue(s.query())
# not necessary to check e_tik and e_tok, as elapsed_time would throw
# exception if otherwise.
return e_tik.elapsed_time(e_tok)
@staticmethod
def _event_wait(self, spin_time_cycles):
s0 = torch.cuda.current_stream()
s1 = torch.cuda.Stream()
e_tik = torch.cuda.Event(blocking=True, enable_timing=True)
e_tok = torch.cuda.Event(blocking=True, enable_timing=True)
e_tik.record(s0)
torch.cuda._sleep(spin_time_cycles - 10)
e_sync = torch.cuda.Event(blocking=True)
e_sync.record()
e_sync.wait(s1)
with torch.cuda.stream(s1):
torch.cuda._sleep(10)
s1.synchronize()
s1.record_event(e_tok)
self.assertTrue(s0.query())
self.assertTrue(s1.query())
self.assertTrue(e_sync.query())
# not necessary to check e_tik and e_tok, as elapsed_time would throw
# exception if otherwise.
return e_tik.elapsed_time(e_tok)
@staticmethod
def _test_stream_event_nogil(self, sync_func, p2c, c2p):
with torch.cuda.device('cuda:1'):
c2p.put(0)
p2c.get()
c2p.put(sync_func(self, TestCuda.FIFTY_MIL_CYCLES))
@unittest.skipIf(not TEST_MULTIGPU, "detected only one GPU")
@skipIfRocm
def test_stream_event_nogil(self):
for sync_func in [TestCuda._stream_synchronize,
TestCuda._event_synchronize,
TestCuda._event_wait]:
p2c = queue.Queue()
c2p = queue.Queue()
e_tik = torch.cuda.Event(enable_timing=True)
e_tok = torch.cuda.Event(enable_timing=True)
t = threading.Thread(
target=TestCuda._test_stream_event_nogil,
args=(self, sync_func, p2c, c2p))
t.daemon = True
t.start()
c2p.get()
with torch.cuda.device('cuda:0'):
e_tik.record()
p2c.put(0)
parent_time = sync_func(self, TestCuda.FIFTY_MIL_CYCLES)
child_time = c2p.get()
e_tok.record()
e_tok.synchronize()
total_time = e_tik.elapsed_time(e_tok)
# Without GIL, synchronizations in parent and child threads can
# overlap. The total execution time should be a little bit longer
# than spinning fifty million cycles and much shorter than twice of
# that. However, testing absolute execution time is not reliable as
# it may vary on different hardware in different environments.
# Therefore, this test uses relative comparisons, checking if the
# sum of parent and child threads execution time is greater than the
# real execution time by least 40%.
self.assertGreater(parent_time + child_time, total_time * 1.4)
@unittest.skipIf(not TEST_MULTIGPU, "detected only one GPU")
def test_events_wait(self):
d0 = torch.device('cuda:0')
d1 = torch.device('cuda:1')
torch.cuda.synchronize(d0)
torch.cuda.synchronize(d1)
with torch.cuda.device(d0):
s0 = torch.cuda.current_stream()
torch.cuda._sleep(TestCuda.FIFTY_MIL_CYCLES)
e0 = torch.cuda.Event()
s0.record_event(e0)
with torch.cuda.device(d1):
s1 = torch.cuda.current_stream()
self.assertFalse(s0.query())
self.assertTrue(s1.query())
s1.wait_event(e0)
s1.synchronize()
self.assertTrue(e0.query())
self.assertTrue(s0.query())
self.assertTrue(s1.query())
@unittest.skipIf(not TEST_MULTIGPU, "detected only one GPU")
@skipIfRocm
def test_events_multi_gpu_query(self):
d0 = torch.device('cuda:0')
d1 = torch.device('cuda:1')
with torch.cuda.device(d0):
s0 = torch.cuda.current_stream()
e0 = s0.record_event()
with torch.cuda.device(d1):
s1 = torch.cuda.current_stream()
torch.cuda._sleep(TestCuda.FIFTY_MIL_CYCLES)
e1 = s1.record_event()
self.assertTrue(e0.query())
self.assertFalse(e1.query())
with torch.cuda.device(d0):
self.assertTrue(e0.query())
self.assertFalse(e1.query())
with torch.cuda.device(d1):
self.assertTrue(e0.query())
self.assertFalse(e1.query())
# deliberately using a different device
with torch.cuda.device(d0):
e1.synchronize()
self.assertTrue(e0.query())
self.assertTrue(e1.query())
with torch.cuda.device(d0):
self.assertTrue(e0.query())
self.assertTrue(e1.query())
with torch.cuda.device(d1):
self.assertTrue(e0.query())
self.assertTrue(e1.query())
@unittest.skipIf(not TEST_MULTIGPU, "detected only one GPU")
@skipIfRocm
def test_events_multi_gpu_elapsed_time(self):
d0 = torch.device('cuda:0')
d1 = torch.device('cuda:1')
with torch.cuda.device(d0):
s0 = torch.cuda.current_stream()
e0 = torch.cuda.Event(enable_timing=True)
torch.cuda._sleep(10)
s0.record_event(e0)
with torch.cuda.device(d1):
s1 = torch.cuda.current_stream()
e1 = torch.cuda.Event(enable_timing=True)
torch.cuda._sleep(TestCuda.FIFTY_MIL_CYCLES)
s1.record_event(e1)
e0.synchronize()
e1.synchronize()
with torch.cuda.device(d0):
with self.assertRaises(RuntimeError):
self.assertGreater(e0.elapsed_time(e1), 0)
with torch.cuda.device(d1):
with self.assertRaises(RuntimeError):
self.assertGreater(e0.elapsed_time(e1), 0)
with torch.cuda.device(d0):
s0 = torch.cuda.current_stream()
e2 = torch.cuda.Event(enable_timing=True)
torch.cuda._sleep(TestCuda.FIFTY_MIL_CYCLES)
s0.record_event(e2)
s0.synchronize()
self.assertGreater(e0.elapsed_time(e2), 0)
# deliberately calling from a different device
with torch.cuda.device(d1):
self.assertGreater(e0.elapsed_time(e2), 0)
def test_record_stream(self):
cycles_per_ms = get_cycles_per_ms()
t = torch.FloatTensor([1, 2, 3, 4]).pin_memory()
result = torch.cuda.FloatTensor(t.size())
stream = torch.cuda.Stream()
ptr = [None]
# Performs the CPU->GPU copy in a background stream
def perform_copy():
with torch.cuda.stream(stream):
tmp = t.cuda(non_blocking=True)
ptr[0] = tmp.data_ptr()
torch.cuda.current_stream().wait_stream(stream)
tmp.record_stream(torch.cuda.current_stream())
torch.cuda._sleep(int(50 * cycles_per_ms)) # delay the copy
result.copy_(tmp)
perform_copy()
with torch.cuda.stream(stream):
tmp2 = torch.cuda.FloatTensor(t.size())
tmp2.zero_()
self.assertNotEqual(tmp2.data_ptr(), ptr[0], 'allocation re-used to soon')
self.assertEqual(result.tolist(), [1, 2, 3, 4])
# Check that the block will be re-used after the main stream finishes
torch.cuda.current_stream().synchronize()
with torch.cuda.stream(stream):
tmp3 = torch.cuda.FloatTensor(t.size())
self.assertEqual(tmp3.data_ptr(), ptr[0], 'allocation not re-used')
def test_noncontiguous_pinned_memory(self):
# See issue #3266
x = torch.arange(0, 10).view((2, 5))
self.assertEqual(x.t(), x.t().pin_memory())
def test_caching_pinned_memory(self):
cycles_per_ms = get_cycles_per_ms()
# check that allocations are re-used after deletion
t = torch.FloatTensor([1]).pin_memory()
ptr = t.data_ptr()
del t
t = torch.FloatTensor([1]).pin_memory()
self.assertEqual(t.data_ptr(), ptr, 'allocation not reused')
# check that the allocation is not re-used if it's in-use by a copy
gpu_tensor = torch.cuda.FloatTensor([0])
torch.cuda._sleep(int(50 * cycles_per_ms)) # delay the copy
gpu_tensor.copy_(t, non_blocking=True)
del t
t = torch.FloatTensor([1]).pin_memory()
self.assertNotEqual(t.data_ptr(), ptr, 'allocation re-used too soon')
self.assertEqual(list(gpu_tensor), [1])
@unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
def test_caching_pinned_memory_multi_gpu(self):
# checks that the events preventing pinned memory from being re-used
# too early are recorded on the correct GPU
cycles_per_ms = get_cycles_per_ms()
t = torch.FloatTensor([1]).pin_memory()
ptr = t.data_ptr()
gpu_tensor0 = torch.cuda.FloatTensor([0], device=0)
gpu_tensor1 = torch.cuda.FloatTensor([0], device=1)
with torch.cuda.device(1):
torch.cuda._sleep(int(50 * cycles_per_ms)) # delay the copy
gpu_tensor1.copy_(t, non_blocking=True)
del t
t = torch.FloatTensor([2]).pin_memory()
self.assertNotEqual(t.data_ptr(), ptr, 'allocation re-used too soon')
with torch.cuda.device(0):
gpu_tensor0.copy_(t, non_blocking=True)
self.assertEqual(gpu_tensor1[0], 1)
self.assertEqual(gpu_tensor0[0], 2)
def test_caching_allocator_record_stream_oom(self):
"""allocations delayed by a record_stream call should still be freed on
an out-of-memory in cuda_malloc_retry. see issue #19219"""
stream = torch.cuda.Stream()
with torch.cuda.stream(stream):
y = torch.zeros(40 * 1024 * 1024, device='cuda')
for _ in range(100):
x = torch.empty(40 * 1024 * 1024, device='cuda')
with torch.cuda.stream(stream):
y += x
# delays re-use of `x` until after all operations in `stream`
x.record_stream(stream)
del x
# we've made a mess by allocating up to the device capacity. free any
# cached blocks in case it affects future tests.
torch.cuda.empty_cache()
def test_reduction_gpu_memory_accessing(self):
x = torch.ones(512, 8, dtype=torch.float32, device='cuda')
torch.sum(x, 0)
def test_sum_cpu_gpu_mismatch(self):
x = torch.randn(20, dtype=torch.float32, device='cuda:0')
y = torch.randn(1, dtype=torch.float32)
with self.assertRaisesRegex(RuntimeError,
'expected device cpu and dtype Float but got device cuda:0 and dtype Float'):
torch.sum(x, dim=[0], dtype=torch.float32, out=y)
# makeing sure half to float promotion is also properly working.
x = x.half()
with self.assertRaisesRegex(RuntimeError,
'expected device cpu and dtype Float but got device cuda:0 and dtype Half'):
torch.sum(x, dim=[0], dtype=torch.float32, out=y)
@skipIfRocm
def test_sum_noncontig(self):
x = torch.randn(1, 75, 57, 20, device='cuda').permute(0, 3, 1, 2)
y = x.cpu()
self.assertEqual(x.sum().cpu(), y.sum())
self.assertEqual(x.sum(dim=(-1, -2)).cpu(), y.sum(dim=(-1, -2)))
self.assertEqual(x.sum(dim=(1, 3)).cpu(), y.sum(dim=(1, 3)))
def test_sum_fp16(self):
x = torch.zeros(10, device='cuda', dtype=torch.float16)
self.assertEqual(x.sum(), 0)
x = torch.ones(65504, device='cuda', dtype=torch.float16)
self.assertEqual(x.sum(), 65504)
self.assertEqual(x.sum(dtype=torch.float32), 65504)
x = torch.ones(65536, device='cuda', dtype=torch.float16)
self.assertEqual(x.sum(dtype=torch.float32), 65536)
a = torch.zeros(1203611).bernoulli_(0.0005)
x = a.to(device='cuda', dtype=torch.float16)
self.assertEqual(x.sum().item(), a.sum().item())
a = torch.zeros(100, 121, 80).bernoulli_(0.0005)
x = a.to(device='cuda', dtype=torch.float16)
self.assertEqual(x.sum((0, 2)).float().cpu(), a.sum((0, 2)))
def test_mean_fp16(self):
x = torch.ones(65536, device='cuda', dtype=torch.float16)
self.assertEqual(x.mean(), 1)
x = torch.ones(65536, device='cuda', dtype=torch.float16)
self.assertEqual(x.mean(dtype=torch.float32), 1)
def test_prod_large(self):
# tests global reduction (should_global_reduce = true) in case of non-zero identity element
x = torch.ones(240000, device='cuda', dtype=torch.float32)
self.assertEqual(x.prod(), 1)
@staticmethod
def _select_broadcastable_dims(dims_full=None):
return _TestTorchMixin._select_broadcastable_dims(dims_full)
@unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
def test_inverse(self):
_TestTorchMixin._test_inverse(self, lambda t: t.cuda())
@slowTest
@unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
def test_inverse_many_batches(self):
_TestTorchMixin._test_inverse_slow(self, lambda t: t.cuda())
@unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
def test_pinverse(self):
_TestTorchMixin._test_pinverse(self, lambda t: t.cuda())
@unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
def test_matrix_rank(self):
_TestTorchMixin._test_matrix_rank(self, lambda x: x.cuda())
@unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
def test_matrix_power(self):
_TestTorchMixin._test_matrix_power(self, conv_fn=lambda t: t.cuda())
def test_chain_matmul(self):
_TestTorchMixin._test_chain_matmul(self, cast=lambda t: t.cuda())
@unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
def test_det_logdet_slogdet(self):
_TestTorchMixin._test_det_logdet_slogdet(self, 'cuda')
@unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
def test_det_logdet_slogdet_batched(self):
_TestTorchMixin._test_det_logdet_slogdet_batched(self, 'cuda')
@unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
def test_solve(self):
_TestTorchMixin._test_solve(self, lambda t: t.cuda())
@unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
def test_solve_batched(self):
_TestTorchMixin._test_solve_batched(self, lambda t: t.cuda())
@slowTest
@unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
def test_solve_batched_many_batches(self):
_TestTorchMixin._test_solve_batched_many_batches(self, lambda t: t.cuda())
@unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
def test_solve_batched_dims(self):
_TestTorchMixin._test_solve_batched_dims(self, lambda t: t.cuda())
@unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
def test_cholesky_solve(self):
_TestTorchMixin._test_cholesky_solve(self, lambda t: t.cuda())
@unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
def test_cholesky_solve_batched(self):
_TestTorchMixin._test_cholesky_solve_batched(self, lambda t: t.cuda())
@slowTest
@unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
def test_cholesky_solve_batched_many_batches(self):
_TestTorchMixin._test_cholesky_solve_batched_many_batches(self, lambda t: t.cuda())
@unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
def test_cholesky_solve_batched_dims(self):
_TestTorchMixin._test_cholesky_solve_batched_dims(self, lambda t: t.cuda())
@unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
def test_cholesky_inverse(self):
_TestTorchMixin._test_cholesky_inverse(self, lambda t: t.cuda())
@unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
def test_cholesky(self):
_TestTorchMixin._test_cholesky(self, lambda t: t.cuda())
@unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
def test_cholesky_batched(self):
_TestTorchMixin._test_cholesky_batched(self, lambda t: t.cuda())
def test_view(self):
_TestTorchMixin._test_view(self, lambda t: t.cuda())
def test_flip(self):
_TestTorchMixin._test_flip(self, use_cuda=True)
def test_rot90(self):
_TestTorchMixin._test_rot90(self, use_cuda=True)
def test_signal_window_functions(self):
_TestTorchMixin._test_signal_window_functions(self, device=torch.device('cuda'))
@skipIfRocm
def test_fft_ifft_rfft_irfft(self):
_TestTorchMixin._test_fft_ifft_rfft_irfft(self, device=torch.device('cuda'))
@contextmanager
def plan_cache_max_size(n, device=None):
if device is None:
plan_cache = torch.backends.cuda.cufft_plan_cache
else:
plan_cache = torch.backends.cuda.cufft_plan_cache[device]
original = plan_cache.max_size
plan_cache.max_size = n
yield
plan_cache.max_size = original
with plan_cache_max_size(max(1, torch.backends.cuda.cufft_plan_cache.size - 10)):
_TestTorchMixin._test_fft_ifft_rfft_irfft(self, device=torch.device('cuda'))
with plan_cache_max_size(0):
_TestTorchMixin._test_fft_ifft_rfft_irfft(self, device=torch.device('cuda'))
torch.backends.cuda.cufft_plan_cache.clear()
# check that stll works after clearing cache
with plan_cache_max_size(10):
_TestTorchMixin._test_fft_ifft_rfft_irfft(self, device=torch.device('cuda'))
with self.assertRaisesRegex(RuntimeError, r"must be non-negative"):
torch.backends.cuda.cufft_plan_cache.max_size = -1
with self.assertRaisesRegex(RuntimeError, r"read-only property"):
torch.backends.cuda.cufft_plan_cache.size = -1
with self.assertRaisesRegex(RuntimeError, r"but got device with index"):
torch.backends.cuda.cufft_plan_cache[torch.cuda.device_count() + 10]
if TEST_MULTIGPU:
# Test that different GPU has different cache
x0 = torch.randn(2, 3, 3, device='cuda:0')
x1 = x0.cuda(1)
self.assertEqual(x0.rfft(2), x1.rfft(2))
# If a plan is used across different devices, the following line (or
# the assert above) would trigger illegal memory access. Other ways
# to trigger the error include
# (1) setting CUDA_LAUNCH_BLOCKING=1 (pytorch/pytorch#19224) and
# (2) printing a device 1 tensor.
x0.copy_(x1)
# Test that un-indexed `torch.backends.cuda.cufft_plan_cache` uses current device
with plan_cache_max_size(10, device='cuda:0'):
with plan_cache_max_size(11, device='cuda:1'):
self.assertEqual(torch.backends.cuda.cufft_plan_cache[0].max_size, 10)
self.assertEqual(torch.backends.cuda.cufft_plan_cache[1].max_size, 11)
self.assertEqual(torch.backends.cuda.cufft_plan_cache.max_size, 10) # default is cuda:0
with torch.cuda.device(1):
self.assertEqual(torch.backends.cuda.cufft_plan_cache.max_size, 11) # default is cuda:1
with torch.cuda.device(0):
self.assertEqual(torch.backends.cuda.cufft_plan_cache.max_size, 10) # default is cuda:0
self.assertEqual(torch.backends.cuda.cufft_plan_cache[0].max_size, 10)
with torch.cuda.device(1):
with plan_cache_max_size(11): # default is cuda:1
self.assertEqual(torch.backends.cuda.cufft_plan_cache[0].max_size, 10)
self.assertEqual(torch.backends.cuda.cufft_plan_cache[1].max_size, 11)
self.assertEqual(torch.backends.cuda.cufft_plan_cache.max_size, 11) # default is cuda:1
with torch.cuda.device(0):
self.assertEqual(torch.backends.cuda.cufft_plan_cache.max_size, 10) # default is cuda:0
self.assertEqual(torch.backends.cuda.cufft_plan_cache.max_size, 11) # default is cuda:1
# passes on ROCm w/ python 2.7, fails w/ python 3.6
@skipIfRocm
def test_stft(self):
_TestTorchMixin._test_stft(self, device=torch.device('cuda'))
def test_multinomial(self):
_TestTorchMixin._test_multinomial(self, torch.cuda.FloatTensor)
# Test two corner cases from older PyTorch (Issue #4858)
freqs = torch.cuda.FloatTensor([
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.03178183361887932, 0.027680952101945877, 0.033176131546497345,
0.046052902936935425, 0.07742464542388916, 0.11543981730937958,
0.14148041605949402, 0.15784293413162231, 0.13180233538150787,
0.08271478116512299, 0.049702685326337814, 0.027557924389839172,
0.018125897273421288, 0.011851548217236996, 0.010252203792333603,
0.007422595750540495, 0.005372154992073774, 0.0045109698548913,
0.0036087757907807827, 0.0035267581697553396, 0.0018864056328311563,
0.0024605290964245796, 0.0022964938543736935, 0.0018453967059031129,
0.0010662291897460818, 0.0009842115687206388, 0.00045109697384759784,
0.0007791675161570311, 0.00020504408166743815, 0.00020504408166743815,
0.00020504408166743815, 0.00012302644609007984, 0.0,
0.00012302644609007984, 4.100881778867915e-05, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0])
torch.cuda.manual_seed(11042)
sample = torch.multinomial(freqs, 1000, True)
self.assertNotEqual(freqs[sample].min(), 0)
p = torch.zeros(3421, 2, device="cuda", dtype=torch.float)
p[:, 1] = 1
torch.cuda.manual_seed(5214)
r = torch.multinomial(p, 1)
self.assertNotEqual(r.min().item(), 0)
# test corner case from Issue #13867
torch.cuda.manual_seed(33)
probs = torch.randn(1000000, device='cuda').clamp(min=0) * 3e-5
samples = probs.multinomial(1000000, replacement=True)
self.assertGreater(probs[samples].min().item(), 0)
@skipCUDANonDefaultStreamIf(True)
def test_multinomial_alias(self):
_TestTorchMixin._test_multinomial_alias(self, lambda t: t.cuda())
@staticmethod
def mute():
os.dup2(os.open(os.devnull, os.O_WRONLY), sys.stderr.fileno())
def _spawn_method(self, method, arg):
ctx = mp.get_context("spawn")
with ctx.Pool(1, initializer=self.mute) as pool:
errors = pool.map(method, [arg])
for e in errors:
if 'device-side assert triggered' not in str(e):
self.fail(e)
@staticmethod
def _test_multinomial_invalid_probs_cuda(probs):
try:
with torch.random.fork_rng(devices=[0]):
torch.multinomial(probs.to('cuda'), 2)
torch.cuda.synchronize()
return False # Should not be reached
except RuntimeError as e:
return e
@unittest.skipIf(NO_MULTIPROCESSING_SPAWN, "Disabled for environments that \
don't support multiprocessing with spawn start method")
@unittest.skipIf(IS_WINDOWS, 'FIXME: CUDA OOM error on Windows')
@unittest.skipIf(not PY3,
"spawn start method is not supported in Python 2, \
but we need it for creating another process with CUDA")
@skipIfRocm
def test_multinomial_invalid_probs_cuda(self):
test_method = TestCuda._test_multinomial_invalid_probs_cuda
self._spawn_method(test_method, torch.Tensor([1, -1, 1]))
self._spawn_method(test_method, torch.Tensor([1, inf, 1]))
self._spawn_method(test_method, torch.Tensor([1, -inf, 1]))
self._spawn_method(test_method, torch.Tensor([1, 1, nan]))
self._spawn_method(test_method, torch.Tensor([0, 1, 0]))
def test_broadcast(self):
_TestTorchMixin._test_broadcast(self, lambda t: t.cuda())
def test_contiguous(self):
_TestTorchMixin._test_contiguous(self, lambda t: t.cuda())
def test_broadcast_fused_matmul(self):
_TestTorchMixin._test_broadcast_fused_matmul(self, lambda t: t.cuda())
def test_broadcast_batched_matmul(self):
_TestTorchMixin._test_broadcast_batched_matmul(self, lambda t: t.cuda())
def test_index(self):
_TestTorchMixin._test_index(self, lambda t: t.cuda())
@skipCUDANonDefaultStreamIf(True)
def test_advancedindex(self):
_TestTorchMixin._test_advancedindex(self, lambda t: t.cuda())
def test_advancedindex_mixed_cpu_cuda(self):
def test(x, ia, ib):
# test getitem
self.assertEqual(x[:, ia, None, ib, 0].cpu(),
x.cpu()[:, ia.cpu(), None, ib.cpu(), 0])
self.assertEqual(x[ia], x.cpu()[ia.cpu()])
# test setitem
x_clone1 = x.clone()
x_clone2 = x.clone()
first_shape = x[:, ia, None, ib, 0].shape
second_shape = x[ia].shape
x_clone1[:, ia, None, ib, 0] = torch.randn(first_shape).to(x_clone1)
x_clone2[ia] = torch.randn(second_shape).to(x_clone2)
cpu = torch.device('cpu')
for device in ['cuda:0', 'cuda:1'] if torch.cuda.device_count() > 1 else ['cuda']:
# Index cpu tensor with cuda tensor
x = torch.randn(3, 4, 4, 4, 3)
ia = torch.tensor([0, 2, 1]).to(device)
ib = torch.tensor([0, 2, 1]).to(device)
test(x, ia, ib)
# Index cuda tensor with cpu tensor
x = x.to(device)
ia = ia.to(cpu)
ib = ib.to(cpu)
test(x, ia, ib)
# Index cpu tensor with mixed cpu, cuda tensors
x = x.to(cpu)
ia = ia.to(cpu)
ib = ib.to(device)
test(x, ia, ib)
# Index cuda tensor with mixed cpu, cuda tensors
x = x.to(device)
ia = ia.to(cpu)
ib = ib.to(device)
test(x, ia, ib)
if torch.cuda.device_count() > 1:
other_device = 'cuda:0' if device != 'cuda:0' else 'cuda:1'
# Index cuda tensor with mixed cpu, cuda tensors on different devices
x = x.to(device)
ia = ia.to(cpu)
ib = ib.to(other_device)
test(x, ia, ib)
def test_advancedindex_big(self):
_TestTorchMixin._test_advancedindex_big(self, lambda t: t.cuda())
@slowTest
@unittest.skipIf(not TEST_LARGE_TENSOR, "not enough memory")
def test_huge_index(self):
src = torch.empty(15000000, 45, device='cuda', dtype=torch.long).random_(0, 2**22)
idx = torch.randperm(src.shape[0], device='cuda')
res = src[idx]
res_cpu = src.cpu()[idx.cpu()]
self.assertEqual(res.cpu(), res_cpu)
def test_kthvalue(self):
_TestTorchMixin._test_kthvalue(self, device='cuda')
@unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
def test_lu(self):
_TestTorchMixin._test_lu(self, lambda t: t.cuda(), pivot=False)
_TestTorchMixin._test_lu(self, lambda t: t.cuda(), pivot=True)
@unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
def test_lu_solve(self):
_TestTorchMixin._test_lu_solve(self, lambda t: t.cuda(), pivot=False)
_TestTorchMixin._test_lu_solve(self, lambda t: t.cuda(), pivot=True)
@slowTest
@unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
def test_lu_solve_batched_many_batches(self):
_TestTorchMixin._test_lu_solve_batched_many_batches(self, lambda t: t.cuda())
@unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
def test_lu_unpack(self):
_TestTorchMixin._test_lu_unpack(self, lambda t: t.cuda(), pivot=False)
_TestTorchMixin._test_lu_unpack(self, lambda t: t.cuda(), pivot=True)
def test_dim_reduction(self):
_TestTorchMixin._test_dim_reduction(self, lambda t: t.cuda())
def test_tensor_gather(self):
_TestTorchMixin._test_gather(self, lambda t: t.cuda(), False)
def test_tensor_scatter(self):
_TestTorchMixin._test_scatter_base(self, lambda t: t.cuda(), 'scatter_', test_bounds=False)
def test_tensor_scatterAdd(self):
_TestTorchMixin._test_scatter_base(self, lambda t: t.cuda(), 'scatter_add_', test_bounds=False)
def test_tensor_scatterFill(self):
_TestTorchMixin._test_scatter_base(self, lambda t: t.cuda(), 'scatter_', True, test_bounds=False)
def test_min_max_inits(self):
# Testing if THC_reduceAll received the correct index initialization.
# This affects the result of THC_reduceAll operations at extreme values
x = torch.cuda.ByteTensor([0])
y = torch.cuda.ByteTensor([255])
expected = torch.cuda.LongTensor([0])[0]
_, v = x.max(dim=0)
self.assertEqual(v, expected)
_, v = y.min(dim=0)
self.assertEqual(v, expected)
def test_max_with_inf(self):
_TestTorchMixin._test_max_with_inf(self, (torch.half, torch.float, torch.double), 'cuda')
def test_min_with_inf(self):
_TestTorchMixin._test_min_with_inf(self, (torch.half, torch.float, torch.double), 'cuda')
def test_rpow(self):
_TestTorchMixin._test_rpow(self, lambda x: x.cuda())
def test_int_pow(self):
_TestTorchMixin._test_int_pow(self, lambda x: x.cuda())
def test_remainder_overflow(self):
_TestTorchMixin._test_remainder_overflow(self, dtype=torch.int64, device='cuda')
def test_var(self):
cpu_tensor = torch.randn(2, 3, 3)
gpu_tensor = cpu_tensor.cuda()
self.assertEqual(gpu_tensor.var(), cpu_tensor.var())
self.assertEqual(gpu_tensor.var(1), cpu_tensor.var(1))
self.assertEqual(gpu_tensor.var(2), cpu_tensor.var(2))
self.assertEqual(gpu_tensor.std(), cpu_tensor.std())
self.assertEqual(gpu_tensor.std(1), cpu_tensor.std(1))
self.assertEqual(gpu_tensor.var(2), cpu_tensor.var(2))
cpu_tensor = torch.randn(100)
gpu_tensor = cpu_tensor.cuda()
self.assertEqual(gpu_tensor.var(), cpu_tensor.var())
def test_var_unbiased(self):
tensor = torch.randn(100).cuda()
self.assertEqual(tensor.var(0), tensor.var(0, unbiased=True))
self.assertEqual(tensor.var(), tensor.var(unbiased=True))
self.assertEqual(tensor.var(unbiased=False), tensor.var(0, unbiased=False))
tensor = torch.FloatTensor([1.0, 2.0]).cuda()
self.assertEqual(tensor.var(unbiased=True), 0.5)
self.assertEqual(tensor.var(unbiased=False), 0.25)
tensor = torch.randn(100).cuda()
self.assertEqual(tensor.std(0), tensor.std(0, unbiased=True))
self.assertEqual(tensor.std(), tensor.std(unbiased=True))
self.assertEqual(tensor.std(unbiased=False), tensor.std(0, unbiased=False))
def test_var_large_input(self):
# Large, not-nice input
tensor_cpu = torch.randn(2 * 32 * 1024 + 1, 2, 67)
tensor_cuda = tensor_cpu.cuda()
self.assertEqual(tensor_cpu.var(2), tensor_cuda.var(2).cpu())
def test_var_stability(self):
tensor = torch.FloatTensor([2281.5, 2281.25]).cuda()
# Stability for inner dim
self.assertEqual(tensor.var(0), 0.03125)
# General stability
self.assertEqual(tensor.var(), 0.03125)
# Stability for outer dimensions
tensor = tensor.unsqueeze(1)
self.assertEqual(tensor.var(0), 0.03125)
@skipIfRocm
def test_digamma(self):
def test(use_double=False):
cpu_tensor = torch.randn(10, 10, 10)
gpu_tensor = cpu_tensor.cuda()
zeros = torch.zeros(10, 10, 10)
if (use_double):
cpu_tensor = cpu_tensor.double()
gpu_tensor = gpu_tensor.double()
zeros = zeros.double()
cpu_out = cpu_tensor.digamma()
gpu_out = gpu_tensor.digamma()
norm_errors = (gpu_out - cpu_out.cuda()) / gpu_out
self.assertEqual(norm_errors, zeros)
test(True)
test(False)
# Test float32 behavior near and at poles.
cpu_tensor = torch.tensor([-0.999999994, -1.999999994, -2.0000000111,
-100.99999994, -1931.99999994, 0.000000111,
-0.000000111, 0, -1, -2, -931])
expected_errors = torch.tensor([0, 0, 0, 0, 0, 0, 0, nan, nan, nan, nan])
gpu_tensor = cpu_tensor.cuda()
cpu_out = cpu_tensor.digamma()
gpu_out = gpu_tensor.digamma()
norm_errors = (gpu_out - cpu_out.cuda()) / gpu_out
self.assertEqual(norm_errors, expected_errors)
@skipIfRocm
def test_polygamma(self):
def test(use_double=False):
cpu_tensor = torch.randn(10, 10, 10)
gpu_tensor = cpu_tensor.cuda()
zeros = torch.zeros(10, 10, 10)
if (use_double):
cpu_tensor = cpu_tensor.double()
gpu_tensor = gpu_tensor.double()
zeros = zeros.double()
for n in [0, 1]:
cpu_out = cpu_tensor.polygamma(n)
gpu_out = gpu_tensor.polygamma(n)
norm_errors = (gpu_out - cpu_out.cuda()) / gpu_out
self.assertEqual(norm_errors, zeros)
test(True)
test(False)
@unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
def test_symeig(self):
_TestTorchMixin._test_symeig(self, lambda t: t.cuda())
@unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
def test_svd(self):
_TestTorchMixin._test_svd(self, lambda t: t.cuda())
@unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
def test_svd_no_singularvectors(self):
_TestTorchMixin._test_svd_no_singularvectors(self, lambda t: t.cuda())
def test_arange(self):
for t in ['IntTensor', 'LongTensor', 'FloatTensor', 'DoubleTensor']:
a = torch.cuda.__dict__[t]()
torch.arange(0, 10, out=a)
b = torch.__dict__[t]()
torch.arange(0, 10, out=b)
self.assertEqual(a, b.cuda())
def test_linspace(self):
a = torch.linspace(0, 10, 10, device='cuda')
b = torch.linspace(0, 10, 10)
self.assertEqual(a, b.cuda())
def test_logspace(self):
a = torch.logspace(1, 10, 10, device='cuda')
b = torch.logspace(1, 10, 10)
self.assertEqual(a, b.cuda())
# Check non-default base=2
a = torch.logspace(1, 10, 10, 2, device='cuda')
b = torch.logspace(1, 10, 10, 2)
self.assertEqual(a, b.cuda())
def test_lerp(self):
_TestTorchMixin._test_lerp(self, lambda t: t.cuda())
def test_diagonal(self):
_TestTorchMixin._test_diagonal(self, dtype=torch.float32, device='cuda')
def test_diagflat(self):
_TestTorchMixin._test_diagflat(self, dtype=torch.float32, device='cuda')
@unittest.skipIf(not TEST_NUMPY, "NumPy not found")
@unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
@skipCUDANonDefaultStreamIf(True)
def test_norm(self):
_TestTorchMixin._test_norm(self, device='cuda')
@unittest.skipIf(not TEST_NUMPY, "Numpy not found")
@unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
@skipCUDANonDefaultStreamIf(True)
def test_nuclear_norm_axes_small_brute_force(self):
_TestTorchMixin._test_nuclear_norm_axes(self, device='cuda')
@unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
@skipCUDANonDefaultStreamIf(True)
def test_nuclear_norm_exceptions(self):
_TestTorchMixin._test_nuclear_norm_exceptions(self, device='cuda')
def test_dist(self):
_TestTorchMixin._test_dist(self, device='cuda')
@unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
def test_geqrf(self):
_TestTorchMixin._test_geqrf(self, lambda t: t.cuda())
@unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
@skipCUDANonDefaultStreamIf(True)
def test_triangular_solve(self):
_TestTorchMixin._test_triangular_solve(self, lambda t: t.cuda())
@unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
def test_triangular_solve_batched(self):
_TestTorchMixin._test_triangular_solve_batched(self, lambda t: t.cuda())
@slowTest
@unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
def test_triangular_solve_batched_many_batches(self):
_TestTorchMixin._test_triangular_solve_batched_many_batches(self, lambda t: t.cuda())
@unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
def test_triangular_solve_batched_dims(self):
_TestTorchMixin._test_triangular_solve_batched_dims(self, lambda t: t.cuda())
@unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
def test_lstsq(self):
_TestTorchMixin._test_lstsq(self, 'cuda')
@unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
def test_qr(self):
_TestTorchMixin._test_qr(self, lambda t: t.cuda())
@unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
def test_get_set_rng_state_all(self):
states = torch.cuda.get_rng_state_all()
before0 = torch.cuda.FloatTensor(100, device=0).normal_()
before1 = torch.cuda.FloatTensor(100, device=1).normal_()
torch.cuda.set_rng_state_all(states)
after0 = torch.cuda.FloatTensor(100, device=0).normal_()
after1 = torch.cuda.FloatTensor(100, device=1).normal_()
self.assertEqual(before0, after0, 0)
self.assertEqual(before1, after1, 0)
@skipIfRocm
def test_nvtx(self):
# Just making sure we can see the symbols
torch.cuda.nvtx.range_push("foo")
torch.cuda.nvtx.mark("bar")
torch.cuda.nvtx.range_pop()
def test_randperm_cuda(self):
_TestTorchMixin._test_randperm(self, device='cuda')
def test_random_neg_values(self):
_TestTorchMixin._test_random_neg_values(self, use_cuda=True)
def test_bincount_cuda(self):
_TestTorchMixin._test_bincount(self, device='cuda')
# ensure CUDA code coverage
input_size = (5000,)
w = torch.randn(input_size, device='cuda')
w_cpu = w.cpu()
# test shared memory impl
t = torch.randint(50, input_size, dtype=torch.int8, device='cuda')
self.assertEqual(t.cpu().bincount(), t.bincount())
self.assertEqual(t.cpu().bincount(w_cpu), t.bincount(w))
# test multi block memory impl
# see `THRESH_NUMBER_BINS_FOR_MULTI_BLOCK_MEM` in SummaryOps.cu
t = torch.randint(500, input_size, dtype=torch.int64, device='cuda')
self.assertEqual(t.cpu().bincount(), t.bincount())
self.assertEqual(t.cpu().bincount(w_cpu), t.bincount(w))
# test global memory impl
# see `THRESH_NUMBER_BINS_FOR_GLOBAL_MEM` in SummaryOps.cu
t = torch.randint(2000, input_size, dtype=torch.int64, device='cuda')
self.assertEqual(t.cpu().bincount(), t.bincount())
self.assertEqual(t.cpu().bincount(w_cpu), t.bincount(w))
def test_tiny_half_norm_(self):
a = torch.arange(25).cuda().float()
a /= 100000000
b = a.half()
self.assertGreater(b.norm().item(), 0)
def test_norm_type_conversion(self):
a = torch.ones(65536).cuda().half()
self.assertEqual(a.norm(p=0, dtype=torch.float32), 65536)
# Note: This test fails on ROCm CI gfx900 but passes on gfx906
@skipIfRocm
# Test that wrap_with_cuda_memory_check successfully detects leak
def test_cuda_memory_leak_detection(self):
l = []
@self.wrap_with_cuda_memory_check
def no_leak():
pass
@self.wrap_with_cuda_memory_check
def leak_gpu0():
l.append(torch.tensor(10, device=torch.device("cuda:0")))
no_leak()
with self.assertRaisesRegex(AssertionError, r"leaked \d+ bytes CUDA memory on device 0"):
leak_gpu0()
if TEST_MULTIGPU:
@self.wrap_with_cuda_memory_check
def leak_gpu1():
l.append(torch.tensor(10, device=torch.device("cuda:1")))
with self.assertRaisesRegex(AssertionError, r"leaked \d+ bytes CUDA memory on device 1"):
leak_gpu1()
def test_cuda_memory_leak_detection_propagates_errors(self):
with self.assertRaisesRegex(RuntimeError, r"The size of tensor a \(3\) must match"):
with self.assertLeaksNoCudaTensors():
x = torch.randn(3, 1, device='cuda')
y = torch.randn(2, 1, device='cuda')
z = x + y
def test_trilu_indices(self):
for test_args in tri_tests_args:
_compare_trilu_indices(self, *test_args, device='cuda')
# test default options
x = torch.ones(
3, 3, dtype=torch.long, device='cuda', layout=torch.strided)
self.assertEqual(
x.tril(0).nonzero().transpose(0, 1),
torch.tril_indices(3, 3, device='cuda'))
self.assertEqual(
x.triu(0).nonzero().transpose(0, 1),
torch.triu_indices(3, 3, device='cuda'))
def test_large_trilu_indices(self):
for test_args in tri_large_tests_args:
_compare_large_trilu_indices(self, *test_args, device='cuda')
def test_triu_tril(self):
_TestTorchMixin._test_triu_tril(self, lambda t: t.cuda())
def test_cuda_round(self):
# test half-to-even
a = [-5.8, -3.5, -2.3, -1.5, -0.5, 0.5, 1.5, 2.3, 3.5, 5.8]
res = [-6., -4., -2., -2., 0., 0., 2., 2., 4., 6.]
self.assertEqual(
torch.HalfTensor(a).cuda().round().cpu(),
torch.HalfTensor(res).cpu())
self.assertEqual(
torch.FloatTensor(a).cuda().round().cpu(),
torch.FloatTensor(res).cpu())
self.assertEqual(
torch.DoubleTensor(a).cuda().round().cpu(),
torch.DoubleTensor(res).cpu())
def load_ignore_file():
from os.path import join, dirname
global ignores
path = join(dirname(__file__), 'data', 'test_cuda_ignores.txt')
with open(path, 'r') as f:
ignores = {l for l in f.read().splitlines() if not l.startswith('#')}
def generate_tests():
for decl in tests:
for t in types:
tensor = t()
# Default values
desc = ''
type_subset = types
no_inplace = False
decorator = None
if len(decl) == 3:
name, constr, arg_constr = decl
elif len(decl) == 4:
name, constr, arg_constr, desc = decl
elif len(decl) == 5:
name, constr, arg_constr, desc, type_subset = decl
elif len(decl) == 6:
name, constr, arg_constr, desc, type_subset, no_inplace = decl
elif len(decl) == 7:
name, constr, arg_constr, desc, type_subset, no_inplace, decorator = decl
if t not in type_subset:
continue
if TEST_WITH_ROCM and decorator is not None:
if (isinstance(decorator, str)):
tensor_type_name = str(t.__name__)
decorator_list = decorator.split(":")
skip_type_list = decorator_list[1].split(",")
if (("ByteTensor" in skip_type_list) and tensor_type_name == "ByteTensor") \
or (("CharTensor" in skip_type_list) and tensor_type_name == "CharTensor") \
or (("DoubleTensor" in skip_type_list) and tensor_type_name == "DoubleTensor") \
or (("FloatTensor" in skip_type_list) and tensor_type_name == "FloatTensor") \
or (("HalfTensor" in skip_type_list) and tensor_type_name == "HalfTensor") \
or (("IntTensor" in skip_type_list) and tensor_type_name == "IntTensor") \
or (("LongTensor" in skip_type_list) and tensor_type_name == "LongTensor") \
or (("ShortTensor" in skip_type_list) and tensor_type_name == "ShortTensor"):
decorator = skipIfRocm
else:
decorator = None
elif ((not TEST_WITH_ROCM) and (decorator is not None)):
if (isinstance(decorator, str)):
decorator = None
precision = custom_precision.get(name, TestCuda.precision)
if is_half(t):
precision = custom_half_precision.get(name, precision)
for inplace in (True, False):
if inplace and no_inplace:
continue
if inplace:
name_inner = name + '_'
else:
name_inner = name
if t != torch.HalfTensor and not hasattr(tensor, name_inner):
# torch.HalfTensor doesn't support most operations,
# but we use torch.FloatTensor as cpu baseline
continue
full_name = '{}.{}'.format(tensor.type(), name_inner)
if full_name in ignores:
continue
test_name = 'test_' + t.__name__ + '_' + name_inner
if desc:
test_name += '_' + desc
assert not hasattr(TestCuda, test_name), "Duplicated test name: " + test_name
test_fn = compare_cpu_gpu(constr, arg_constr, name_inner, t, precision)
if decorator is not None:
test_fn = decorator(test_fn)
setattr(TestCuda, test_name, test_fn)
if __name__ == '__main__':
if TEST_CUDA:
load_ignore_file()
generate_tests()
run_tests()