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test_cuda.py
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test_cuda.py
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import io
import tempfile
import unittest
import sys
from itertools import repeat
import os
from contextlib import contextmanager
import threading
import 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
# 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 >= 9e9
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
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):
x = torch.randn(5, 5).cuda()
y = torch.zeros(5, 5)
y.copy_(x, non_blocking=True)
self.assertEqual(x, y)
x = torch.randn(5, 5)
y = torch.zeros(5, 5).cuda()
y.copy_(x, non_blocking=True)
self.assertEqual(x, y)
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))