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test_torch.py
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test_torch.py
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# -*- coding: utf-8 -*-
# Owner(s): ["module: tests"]
import torch
import torch.utils.data
import numpy as np
import contextlib
import gc
import io
import inspect
import itertools
import math
import random
import re
import copy
import os
import tempfile
import unittest
import warnings
import types
import pickle
import textwrap
import subprocess
import weakref
import sys
from torch.utils.dlpack import from_dlpack, to_dlpack
from torch._six import inf, nan, string_classes
from itertools import product, combinations, permutations
from functools import partial
from torch import multiprocessing as mp
from torch.testing import make_tensor
from torch.testing._internal.common_utils import (
TestCase, TEST_WITH_ROCM, run_tests,
IS_WINDOWS, IS_FILESYSTEM_UTF8_ENCODING, NO_MULTIPROCESSING_SPAWN,
IS_SANDCASTLE, IS_FBCODE, IS_REMOTE_GPU, load_tests, slowTest,
TEST_WITH_CROSSREF,
skipCUDAMemoryLeakCheckIf, BytesIOContext,
skipIfRocm, skipIfNoSciPy, TemporaryFileName, TemporaryDirectoryName,
wrapDeterministicFlagAPITest, DeterministicGuard, CudaSyncGuard,
skipIfNotRegistered, bytes_to_scalar, parametrize, skipIfMps)
from multiprocessing.reduction import ForkingPickler
from torch.testing._internal.common_device_type import (
expectedFailureMeta,
expectedFailureXLA,
instantiate_device_type_tests,
onlyCUDA, onlyCPU,
dtypes, dtypesIfCUDA, dtypesIfCPU, deviceCountAtLeast,
skipMeta,
PYTORCH_CUDA_MEMCHECK, largeTensorTest, onlyNativeDeviceTypes,
expectedAlertNondeterministic, get_all_device_types, skipXLA)
from typing import Tuple
import torch.backends.quantized
import torch.testing._internal.data
from torch.testing._internal.common_cuda import (
tf32_on_and_off, tf32_is_not_fp32, TEST_CUDNN)
from torch.testing._internal.common_dtype import (
floating_types_and, get_all_math_dtypes, all_types_and_complex_and, complex_types,
all_types_and, floating_types, floating_and_complex_types, integral_types,
)
# Protects against includes accidentally setting the default dtype
assert torch.get_default_dtype() is torch.float32
# load_tests from torch.testing._internal.common_utils is used to automatically filter tests for
# sharding on sandcastle. This line silences flake warnings
load_tests = load_tests
AMPERE_OR_ROCM = TEST_WITH_ROCM or tf32_is_not_fp32()
@contextlib.contextmanager
def torch_vital_set(value):
stash = None
if 'TORCH_VITAL' in os.environ:
stash = os.environ['TORCH_VITAL']
os.environ['TORCH_VITAL'] = value
try:
yield
finally:
if stash:
os.environ['TORCH_VITAL'] = stash
else:
del os.environ['TORCH_VITAL']
# Tests Vital Signs for Torch
# FIXME: document or deprecate whatever this is
class TestBasicVitalSigns(TestCase):
def test_basic_vitals(self):
with torch_vital_set(''):
self.assertFalse(torch.vitals_enabled())
with torch_vital_set('ON'):
self.assertTrue(torch.vitals_enabled())
def test_basic_vitals_read_write(self):
with torch_vital_set('ON'):
self.assertTrue(torch.vitals_enabled())
# This tests the code path of setting a vital
self.assertTrue(torch.set_vital('Dataloader', 'basic_unit_test', 'TEST_VALUE_STRING'))
self.assertIn('TEST_VALUE_STRING', torch.read_vitals())
self.assertIn('CUDA.used', torch.read_vitals())
def test_dataloader_vitals(self):
with torch_vital_set('ON'):
inps = torch.arange(10 * 5, dtype=torch.float32).view(10, 5)
tgts = torch.arange(10 * 5, dtype=torch.float32).view(10, 5)
dataset = torch.utils.data.TensorDataset(inps, tgts)
loader = torch.utils.data.DataLoader(dataset, batch_size=2)
self.assertIn('Dataloader.enabled\t\t True', torch.read_vitals())
# FIXME: document or deprecate whatever this is
class TestVitalSignsCuda(TestCase):
@onlyCUDA
def test_cuda_vitals_gpu_only(self, device):
with torch_vital_set('ON'):
self.assertIn('CUDA.used\t\t true', torch.read_vitals())
class TestTorchDeviceType(TestCase):
exact_dtype = True
# TODO: move all tensor creation to common ops
def _rand_shape(self, dim, min_size, max_size):
shape = []
for i in range(dim):
shape.append(random.randint(min_size, max_size))
return tuple(shape)
# Validates that mathematical constants are defined properly, as required by
# the Python Array API (https://data-apis.org/array-api/latest/API_specification/constants.html)
@onlyCPU
def test_constants(self, device):
self.assertIsInstance(torch.e, float)
self.assertEqual(torch.e, math.e, atol=0, rtol=0)
self.assertIsInstance(torch.pi, float)
self.assertEqual(torch.pi, math.pi, atol=0, rtol=0)
self.assertIsInstance(torch.nan, float)
self.assertEqual(torch.nan, math.nan, equal_nan=True)
self.assertIsInstance(torch.inf, float)
self.assertEqual(torch.inf, math.inf)
@onlyNativeDeviceTypes
@dtypes(torch.int8, torch.uint8, torch.int16, torch.int32, torch.int64,
torch.bool, torch.float32, torch.complex64, torch.float64,
torch.complex128)
def test_bytes_to_scalar(self, device, dtype):
def rand_byte():
if dtype == torch.bool:
return torch.randint(0, 2, ()).item()
else:
return torch.randint(0, 256, ()).item()
element_size = torch._utils._element_size(dtype)
for i in range(10):
bytes_list = [rand_byte() for _ in range(element_size)]
scalar = bytes_to_scalar(bytes_list, dtype, device)
self.assertEqual(scalar.storage()._untyped().tolist(), bytes_list)
@dtypes(torch.int8, torch.uint8, torch.int16, torch.int32, torch.int64,
torch.bool, torch.float32, torch.complex64, torch.float64,
torch.complex128)
def test_storage(self, device, dtype):
v = make_tensor((3, 5), dtype=dtype, device=device, low=-9, high=9)
self.assertEqual(v.storage()[0], v[0][0])
self.assertEqual(v.storage()[14], v[2][4])
v_s = v.storage()
for el_num in range(v.numel()):
dim0 = el_num // v.size(1)
dim1 = el_num % v.size(1)
self.assertEqual(
v_s[el_num],
v[dim0][dim1])
v_s_byte = v.storage()._untyped()
el_size = v.element_size()
for el_num in range(v.numel()):
start = el_num * el_size
end = start + el_size
dim0 = el_num // v.size(1)
dim1 = el_num % v.size(1)
self.assertEqual(
bytes_to_scalar(v_s_byte[start:end], dtype, device),
v[dim0][dim1])
@onlyNativeDeviceTypes
@dtypes(torch.int8, torch.uint8, torch.int16, torch.int32, torch.int64,
torch.bool, torch.float32, torch.complex64, torch.float64,
torch.complex128, torch.quint8, torch.qint8, torch.qint32,
torch.quint4x2)
def test_storage_setitem(self, device, dtype):
# Skip quantized dtypes for CUDA, since they're not supported
if torch.device(device).type == 'cuda':
if dtype in [torch.quint8, torch.qint8, torch.qint32, torch.quint4x2]:
return
storage_type_name = torch.storage._dtype_to_storage_type_map()[dtype]
if torch.device(device).type == 'cuda':
storage_type = eval('torch.cuda.' + storage_type_name)
else:
storage_type = eval('torch.' + storage_type_name)
N = 10
s = storage_type(N)
s[:] = 0
l = [0] * N
self.assertEqual(s, storage_type(l))
for i in range(N):
s[i] = i
l[i] = i
self.assertEqual(s, storage_type(l))
l[2:7] = [1] * 5
s[2:7] = 1
self.assertEqual(s, storage_type(l))
@onlyNativeDeviceTypes
@dtypes(*all_types_and_complex_and(torch.half, torch.bool, torch.bfloat16))
def test_tensor_storage_type(self, device, dtype):
a = make_tensor((10,), dtype=dtype, device=device, low=-9, high=9)
module = torch.cuda if (torch.device(device).type == 'cuda') else torch
expected_storage_type = getattr(module, torch.storage._dtype_to_storage_type_map()[dtype])
self.assertEqual(a.storage_type(), expected_storage_type)
@onlyNativeDeviceTypes
@dtypes(*all_types_and_complex_and(torch.half, torch.bool, torch.bfloat16))
def test_tensor_from_storage(self, device, dtype):
a = make_tensor((4, 5, 3), dtype=dtype, device=device, low=-9, high=9)
a_s = a.storage()
b = torch.tensor(a_s, device=device, dtype=dtype).reshape(a.size())
self.assertEqual(a, b)
c = torch.tensor(a_s._untyped(), device=device, dtype=dtype).reshape(a.size())
self.assertEqual(a, c)
for error_dtype in all_types_and_complex_and(torch.half, torch.bool, torch.bfloat16):
if error_dtype == dtype:
continue
with self.assertRaisesRegex(RuntimeError, r'Expected a Storage of type'):
error_storage = a.to(error_dtype).storage()
torch.tensor(error_storage, device=device, dtype=dtype)
@onlyNativeDeviceTypes
@dtypes(*all_types_and_complex_and(torch.half, torch.bool, torch.bfloat16))
def test_set_storage(self, device, dtype):
a = make_tensor((4, 5, 3), dtype=dtype, device=device, low=-9, high=9)
a_s = a.storage()
b = torch.tensor([], device=device, dtype=dtype).set_(a_s).reshape(a.size())
self.assertEqual(a, b)
c = torch.tensor([], device=device, dtype=dtype).set_(a_s._untyped()).reshape(a.size())
self.assertEqual(a, c)
for error_dtype in all_types_and_complex_and(torch.half, torch.bool, torch.bfloat16):
if error_dtype == dtype:
continue
with self.assertRaisesRegex(RuntimeError, r'Expected a Storage of type'):
error_storage = a.to(error_dtype).storage()
b = torch.tensor([], device=device, dtype=dtype).set_(error_storage)
@onlyCUDA
def test_module_share_memory(self):
# Test fix for issue #80733
# See https://github.com/pytorch/pytorch/issues/80733
model = torch.nn.Linear(3, 1)
model_cuda = model.to('cuda')
model.share_memory()
@dtypes(torch.float32, torch.complex64)
def test_deepcopy(self, device, dtype):
from copy import deepcopy
a = torch.randn(5, 5, dtype=dtype, device=device)
b = torch.randn(5, 5, dtype=dtype, device=device)
c = a.view(25)
q = [a, [a.storage(), b.storage()], b, c]
w = deepcopy(q)
self.assertEqual(w[0], q[0], atol=0, rtol=0)
self.assertEqual(w[1][0], q[1][0], atol=0, rtol=0)
self.assertEqual(w[1][1], q[1][1], atol=0, rtol=0)
self.assertEqual(w[1], q[1], atol=0, rtol=0)
self.assertEqual(w[2], q[2], atol=0, rtol=0)
# Check that deepcopy preserves sharing
w[0].add_(1)
for i in range(a.numel()):
self.assertEqual(w[1][0][i], q[1][0][i] + 1)
self.assertEqual(w[3], c + 1)
w[2].sub_(1)
for i in range(a.numel()):
self.assertEqual(w[1][1][i], q[1][1][i] - 1)
# Check that deepcopy preserves attributes
a.foo = 3
self.assertEqual(deepcopy(a).foo, 3)
@dtypes(torch.float32, torch.complex64)
def test_deepcopy_scalar(self, device, dtype):
from copy import deepcopy
a = torch.tensor(5, dtype=dtype, device=device)
self.assertEqual(a.size(), deepcopy(a).size())
self.assertEqual(a, deepcopy(a))
def check_internal_mem_overlap(self, inplace_op, num_inputs,
dtype, device,
expected_failure=False):
if isinstance(inplace_op, str):
inplace_op = getattr(torch.Tensor, inplace_op)
input = torch.randn(1, dtype=dtype, device=device).expand(3, 3)
inputs = [input] + [torch.randn_like(input)
for i in range(num_inputs - 1)]
if not expected_failure:
with self.assertRaisesRegex(RuntimeError, 'single memory location'):
inplace_op(*inputs)
else:
with self.assertRaises(AssertionError):
with self.assertRaisesRegex(RuntimeError, 'single memory location'):
inplace_op(*inputs)
def unary_check_input_output_mem_overlap(self, data, sz, op,
expected_failure=False):
def _test(op, output, input):
output_exp = torch.empty_like(output)
op(input, out=output_exp)
self.assertEqual(op(input, out=output), output_exp, msg=op.__name__)
# output is identical to input:
_test(op, output=data[0:sz], input=data[0:sz])
# output and input are independent:
_test(op, output=data[0:sz], input=data[sz:2 * sz])
# output partially overlaps with input:
if not expected_failure:
with self.assertRaisesRegex(RuntimeError, 'unsupported operation'):
_test(op, data[0:sz], data[1:sz + 1])
else:
with self.assertRaises(AssertionError):
with self.assertRaisesRegex(RuntimeError, 'unsupported operation'):
_test(op, data[0:sz], data[1:sz + 1])
# output is transpose of input:
length = int(math.sqrt(sz))
input = data[:length**2].view([length, length])
out = input.t()
if not expected_failure:
with self.assertRaisesRegex(RuntimeError, 'unsupported operation'):
_test(op, out, input)
else:
with self.assertRaises(AssertionError):
with self.assertRaisesRegex(RuntimeError, 'unsupported operation'):
_test(op, out, input)
def ternary_check_input_output_mem_overlap(self, op, device,
expected_failure=False):
sz = 9
data = torch.randn(2 * sz, device=device)
other1 = torch.randn(sz, device=device)
other2 = torch.randn(sz, device=device)
self.unary_check_input_output_mem_overlap(
data, sz, lambda input, out:
op(input, other1.view(input.shape), other2.view(input.shape), out=out),
expected_failure=expected_failure)
self.unary_check_input_output_mem_overlap(
data, sz, lambda input, out:
op(other1.view(input.shape), input, other2.view(input.shape), out=out),
expected_failure=expected_failure)
self.unary_check_input_output_mem_overlap(
data, sz, lambda input, out:
op(other1.view(input.shape), other2.view(input.shape), input, out=out),
expected_failure=expected_failure)
def _select_broadcastable_dims(self, dims_full=None):
# select full dimensionality
if dims_full is None:
dims_full = []
ndims = random.randint(1, 4)
dims_full = [random.randint(1, 8) for _ in range(ndims)]
else:
ndims = len(dims_full)
# select actual dimensions for ops:
# larger: full ndims, individual sizes may be reduced
# smaller: possibly reduced ndims, sizes may be reduced
smaller_ndims = random.randint(1, ndims)
dims_small = []
dims_large = []
for i in range(ndims - 1, -1, -1):
j = random.randint(1, 3)
if j == 1: # no reduced singleton dimension
ds = dims_full[i]
dl = dims_full[i]
elif j == 2: # larger may have reduced singleton dimension
ds = dims_full[i]
dl = 1 if len(dims_small) < smaller_ndims else dims_full[i]
elif j == 3: # smaller may have reduced singleton dimension
ds = 1
dl = dims_full[i]
dims_large = [dl] + dims_large
if len(dims_small) < smaller_ndims:
dims_small = [ds] + dims_small
return (dims_small, dims_large, dims_full)
# collected tests of ops that used scalar_check in Declarations.cwrap for
# correctness
def test_scalar_check(self, device):
zero_d = torch.randn((), device=device)
one_d = torch.randn((1,), device=device)
# remainder
self.assertEqual((), torch.remainder(zero_d, zero_d).shape)
self.assertEqual((), torch.remainder(zero_d, 2).shape)
self.assertEqual((1,), torch.remainder(zero_d, one_d).shape)
self.assertEqual((1,), torch.remainder(one_d, zero_d).shape)
# fmod
self.assertEqual((), torch.fmod(zero_d, zero_d).shape)
self.assertEqual((), torch.fmod(zero_d, 2).shape)
self.assertEqual((1,), torch.fmod(zero_d, one_d).shape)
self.assertEqual((1,), torch.fmod(one_d, zero_d).shape)
# exp, cos, cosh, tan, atan, tanh, erf, erfc, reciprocal
self.assertEqual((), torch.exp(zero_d).shape)
self.assertEqual((), torch.cos(zero_d).shape)
self.assertEqual((), torch.cosh(zero_d).shape)
self.assertEqual((), torch.tan(zero_d).shape)
self.assertEqual((), torch.atan(zero_d).shape)
self.assertEqual((), torch.acosh(zero_d).shape)
self.assertEqual((), torch.asinh(zero_d).shape)
self.assertEqual((), torch.atanh(zero_d).shape)
self.assertEqual((), torch.tanh(zero_d).shape)
self.assertEqual((), torch.erf(zero_d).shape)
self.assertEqual((), torch.erfc(zero_d).shape)
self.assertEqual((), torch.reciprocal(zero_d).shape)
self.assertEqual((1,), torch.exp(one_d).shape)
self.assertEqual((1,), torch.cos(one_d).shape)
self.assertEqual((1,), torch.cosh(one_d).shape)
self.assertEqual((1,), torch.tan(one_d).shape)
self.assertEqual((1,), torch.atan(one_d).shape)
self.assertEqual((1,), torch.acosh(one_d).shape)
self.assertEqual((1,), torch.asinh(one_d).shape)
self.assertEqual((1,), torch.atanh(one_d).shape)
self.assertEqual((1,), torch.tanh(one_d).shape)
self.assertEqual((1,), torch.erf(one_d).shape)
self.assertEqual((1,), torch.erfc(one_d).shape)
self.assertEqual((1,), torch.reciprocal(one_d).shape)
# clamp
self.assertEqual((), torch.clamp(zero_d, min=0, max=1).shape)
self.assertEqual((), torch.clamp(zero_d, min=0).shape)
self.assertEqual((), torch.clamp(zero_d, max=1).shape)
self.assertEqual((1,), torch.clamp(one_d, min=0, max=1).shape)
self.assertEqual((1,), torch.clamp(one_d, min=0).shape)
self.assertEqual((1,), torch.clamp(one_d, max=1).shape)
# cumsum, cumprod, cummax, cummin
self.assertEqual((), torch.logcumsumexp(zero_d, 0).shape)
self.assertEqual((), torch.cumsum(zero_d, 0).shape)
self.assertEqual((), torch.cumprod(zero_d, 0).shape)
self.assertEqual((), torch.cummax(zero_d, 0)[0].shape)
self.assertEqual((), torch.cummin(zero_d, 0)[0].shape)
# sort, topk
self.assertEqual([(), ()], [x.shape for x in torch.sort(zero_d, 0, False)])
self.assertEqual([(), ()], [x.shape for x in torch.sort(zero_d, 0, True)])
self.assertEqual([(), ()], [x.shape for x in torch.topk(zero_d, 1, 0, False)])
self.assertEqual([(), ()], [x.shape for x in torch.topk(zero_d, 1, 0, True)])
# max, min
self.assertEqual((), torch.max(zero_d, zero_d).shape)
self.assertEqual((1,), torch.max(one_d, zero_d).shape)
self.assertEqual((1,), torch.max(zero_d, one_d).shape)
self.assertEqual((), torch.min(zero_d, zero_d).shape)
self.assertEqual((1,), torch.min(one_d, zero_d).shape)
self.assertEqual((1,), torch.min(zero_d, one_d).shape)
zero_d_int = torch.tensor(1, device=device)
one_d_int = torch.tensor([1], device=device)
# lshift, rshift
self.assertEqual((), (zero_d_int >> zero_d_int).shape)
self.assertEqual((), (zero_d_int >> 1).shape)
self.assertEqual((1,), (one_d_int >> zero_d_int).shape)
self.assertEqual((1,), (zero_d_int >> one_d_int).shape)
self.assertEqual((1,), (one_d_int >> 1).shape)
self.assertEqual((), (zero_d_int << zero_d_int).shape)
self.assertEqual((), (zero_d_int << 1).shape)
self.assertEqual((1,), (one_d_int << zero_d_int).shape)
self.assertEqual((1,), (zero_d_int << one_d_int).shape)
self.assertEqual((1,), (one_d_int << 1).shape)
# or
self.assertEqual((), (zero_d_int | zero_d_int).shape)
self.assertEqual((), (zero_d_int | 1).shape)
self.assertEqual((1,), (one_d_int | zero_d_int).shape)
self.assertEqual((1,), (zero_d_int | one_d_int).shape)
self.assertEqual((1,), (one_d_int | 1).shape)
# and
self.assertEqual((), (zero_d_int & zero_d_int).shape)
self.assertEqual((), (zero_d_int & 1).shape)
self.assertEqual((1,), (one_d_int & zero_d_int).shape)
self.assertEqual((1,), (zero_d_int & one_d_int).shape)
self.assertEqual((1,), (one_d_int & 1).shape)
# clone
self.assertEqual((), zero_d.clone().shape)
zero_d_bool = torch.tensor(True, device=device)
one_d_bool = torch.tensor([True], device=device)
# masked_select
self.assertEqual((1,), torch.masked_select(zero_d_bool, zero_d_bool).shape)
self.assertEqual((1,), torch.masked_select(zero_d_bool, one_d_bool).shape)
self.assertEqual((1,), torch.masked_select(one_d_bool, zero_d_bool).shape)
zero_d_uint8 = torch.tensor(1, dtype=torch.uint8, device=device)
one_d_uint8 = torch.tensor([1], dtype=torch.uint8, device=device)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
self.assertEqual((1,), torch.masked_select(zero_d_uint8, zero_d_uint8).shape)
self.assertEqual((1,), torch.masked_select(zero_d_uint8, one_d_uint8).shape)
self.assertEqual((1,), torch.masked_select(one_d_uint8, zero_d_uint8).shape)
# mode
self.assertEqual([(), ()], [x.shape for x in torch.mode(zero_d, dim=0, keepdim=True)])
self.assertEqual([(), ()], [x.shape for x in torch.mode(zero_d, dim=0, keepdim=False)])
self.assertEqual([(1,), (1,)], [x.shape for x in torch.mode(one_d, dim=0, keepdim=True)])
self.assertEqual([(), ()], [x.shape for x in torch.mode(one_d, dim=0, keepdim=False)])
# max
self.assertEqual([(), ()], [x.shape for x in torch.max(zero_d, dim=0, keepdim=True)])
self.assertEqual([(), ()], [x.shape for x in torch.max(zero_d, dim=0, keepdim=False)])
self.assertEqual([(1,), (1,)], [x.shape for x in torch.max(one_d, dim=0, keepdim=True)])
self.assertEqual([(), ()], [x.shape for x in torch.max(one_d, dim=0, keepdim=False)])
# amax
self.assertEqual((), torch.amax(zero_d, dim=0, keepdim=True).shape)
self.assertEqual((), torch.amax(zero_d, dim=0, keepdim=False).shape)
self.assertEqual((1,), torch.amax(one_d, dim=0, keepdim=True).shape)
self.assertEqual((), torch.amax(one_d, dim=0, keepdim=False).shape)
# min
self.assertEqual([(), ()], [x.shape for x in torch.min(zero_d, dim=0, keepdim=True)])
self.assertEqual([(), ()], [x.shape for x in torch.min(zero_d, dim=0, keepdim=False)])
self.assertEqual([(1,), (1,)], [x.shape for x in torch.min(one_d, dim=0, keepdim=True)])
self.assertEqual([(), ()], [x.shape for x in torch.min(one_d, dim=0, keepdim=False)])
# amin
self.assertEqual((), torch.amin(zero_d, dim=0, keepdim=True).shape)
self.assertEqual((), torch.amin(zero_d, dim=0, keepdim=False).shape)
self.assertEqual((1,), torch.amin(one_d, dim=0, keepdim=True).shape)
self.assertEqual((), torch.amin(one_d, dim=0, keepdim=False).shape)
# set_
zero_d_clone = zero_d.clone()
one_d_clone = one_d.clone()
self.assertEqual((), zero_d_clone.set_(one_d.storage(), 0, (), ()).shape)
self.assertEqual((1,), zero_d_clone.set_(one_d.storage(), 0, (1,), (1,)).shape)
self.assertEqual((), one_d_clone.set_(one_d.storage(), 0, (), ()).shape)
self.assertEqual((1,), one_d_clone.set_(one_d.storage(), 0, (1,), (1,)).shape)
self.assertEqual((), zero_d.clone().set_(zero_d).shape)
self.assertEqual((), one_d.clone().set_(zero_d).shape)
self.assertEqual((1,), zero_d.clone().set_(one_d).shape)
self.assertEqual((1,), one_d.clone().set_(one_d).shape)
# take
self.assertEqual((), torch.randn((2, 3), device=device).take(zero_d_int).shape)
self.assertEqual((1,), torch.randn((2, 3), device=device).take(one_d_int).shape)
# gather
self.assertEqual((), torch.gather(zero_d, 0, torch.zeros((), dtype=torch.int64, device=device)).shape)
self.assertEqual((1,), torch.gather(zero_d, 0, torch.zeros((1,), dtype=torch.int64, device=device)).shape)
self.assertEqual((), torch.gather(one_d, 0, torch.zeros((), dtype=torch.int64, device=device)).shape)
self.assertEqual((1,), torch.gather(one_d, 0, torch.zeros((1,), dtype=torch.int64, device=device)).shape)
# normal
# std must be >= 0
zero_d_ge_0 = torch.rand((), device=device)
# documentation says out shape matches shape of mean
self.assertEqual((), torch.normal(zero_d, zero_d_ge_0).shape)
self.assertEqual((1,), torch.normal(one_d, zero_d_ge_0).shape)
self.assertEqual((), torch.normal(1, zero_d_ge_0).shape)
self.assertEqual((), torch.normal(zero_d, 1).shape)
self.assertEqual((1,), torch.normal(one_d, 1).shape)
# TODO: this behavior differs on CPU and GPU, see https://github.com/pytorch/pytorch/issues/30480.
# self.assertEqual((), torch.normal(zero_d, one_d).shape)
# self.assertEqual((), torch.normal(1, one_d).shape)
# convolutions. Yes, we are testing nn.functional here; seems justified
# given its similar to the other tests
w = torch.randn(2, 1, 3, 3, device=device).div_(2).requires_grad_()
self.assertRaises(RuntimeError, lambda: torch.nn.functional.conv2d(zero_d, w, groups=1))
self.assertRaises(RuntimeError, lambda: torch.nn.functional.conv2d(zero_d, w, groups=2))
# nll_loss -- verify input can't be 0-dimensional.
self.assertRaises(ValueError, lambda: torch.nn.functional.nll_loss(zero_d, zero_d, reduction='none'))
self.assertRaises(ValueError, lambda: torch.nn.functional.nll_loss(zero_d, one_d, reduction='none'))
# verify output is 0-dimensional when reduction != 'none'
for (input, target) in ((torch.randn(1, 1, device=device), torch.tensor([0], device=device)),
(torch.randn(1, 1, 1, 1, device=device), torch.tensor([[[0]]], device=device))):
self.assertEqual((), torch.nn.functional.nll_loss(input, target, reduction='mean').shape)
self.assertEqual((), torch.nn.functional.nll_loss(input, target, reduction='sum').shape)
# multilabel_margin_loss
for input in (zero_d, one_d, torch.randn(1, 1, device=device)):
for target in (torch.tensor(0, device=device), torch.tensor([0], device=device), torch.tensor([[0]], device=device)):
if (input.dim() <= 1 and target.dim() <= 1) or (input.dim() == 2 and target.dim() == 2):
output_shape = (target.shape[0],) if target.dim() == 2 else ()
self.assertEqual(output_shape,
torch.nn.functional.multilabel_margin_loss(input, target, reduction='none').shape)
self.assertEqual((), torch.nn.functional.multilabel_margin_loss(input, target, reduction='mean').shape)
self.assertEqual((), torch.nn.functional.multilabel_margin_loss(input, target, reduction='sum').shape)
else:
self.assertRaises(RuntimeError,
lambda: torch.nn.functional.multilabel_margin_loss(input, target, reduction='none'))
self.assertRaises(RuntimeError,
lambda: torch.nn.functional.multilabel_margin_loss(input, target, reduction='mean'))
self.assertRaises(RuntimeError,
lambda: torch.nn.functional.multilabel_margin_loss(input, target, reduction='sum'))
# multi_margin_loss
for input in (zero_d, one_d, torch.randn(1, 1, device=device)):
for target in (torch.tensor(0, device=device), torch.tensor([0], device=device)):
self.assertEqual(target.shape, torch.nn.functional.multi_margin_loss(input, target, reduction='none').shape)
self.assertEqual((), torch.nn.functional.multi_margin_loss(input, target, reduction='mean').shape)
self.assertEqual((), torch.nn.functional.multi_margin_loss(input, target, reduction='sum').shape)
# Uses mismatched arange out size to trigger a warning
@unittest.skipIf(TEST_WITH_CROSSREF, "crossref perturbs line numbering")
def test_cpp_warnings_have_python_context(self, device):
# Creates long string in advance to avoid a too-long Python line
s = ".+Triggered internally at.+RangeFactories.+"
def cpp_warn_fn():
out = torch.empty((5,))
torch.arange(0, 3, out=out)
return out
# Checks eager-mode cpp warning
with warnings.catch_warnings(record=True) as w:
cpp_warn_fn()
frameinfo = inspect.getframeinfo(inspect.currentframe())
warning = w[0]
# Checks for cpp context in the warning message
escaped_warning_message = str(warning.message).encode('unicode_escape')
self.assertTrue(re.search(s, str(escaped_warning_message), re.IGNORECASE) is not None)
# Checks the Python features of the warning
# Note: the eager mode warning refers to the line in the function
# that throws the warning.
self.assertEqual(frameinfo.lineno - 6, warning.lineno)
self.assertEqual(len(w), 1)
# Checks jitted cpp warning
with warnings.catch_warnings(record=True) as w:
scripted_cpp_warn_fn = torch.jit.script(cpp_warn_fn)
scripted_cpp_warn_fn()
warning = w[0]
# Checks for cpp context in the warning message
escaped_warning_message = str(warning.message).encode('unicode_escape')
self.assertTrue(re.search(s, str(escaped_warning_message), re.IGNORECASE) is not None)
# Checks the Python features of the warning
# Note: the jitted warning's lineno refers to the call to the jitted
# function, which in our test suite has a layer of indirection
# that makes checking the Python lineno fragile
self.assertEqual(len(w), 1)
# Checks jitted Python warning
def warn_fn():
warnings.warn("Warning!")
# The jit mimics an eager-mode Python warning in this case
with warnings.catch_warnings(record=True) as w:
scripted_warn_fn = torch.jit.script(warn_fn)
scripted_warn_fn()
frameinfo = inspect.getframeinfo(inspect.currentframe())
warning = w[0]
self.assertTrue(re.search('Warning!', str(warning.message)) is not None)
# Checks the Python features of the warning
self.assertEqual(frameinfo.lineno - 6, warning.lineno)
self.assertEqual(len(w), 1)
# FIXME: move to test_testing
@onlyCPU
def test_warn_always_caught(self, device):
# Check that we can catch a TORCH_WARN_ONCE warning twice
# since assertWarnsOnceRegex uses set_warn_always(True) which changes
# TORCH_WARN_ONCE to TORCH_WARN
a = np.arange(10)
a.flags.writeable = False
with self.assertWarnsOnceRegex(UserWarning, '.*non-writable.*'):
torch.from_numpy(a)
# OK, got it once, now try again
with self.assertWarnsOnceRegex(UserWarning, '.*non-writable.*'):
torch.from_numpy(a)
# Make sure emitting two warnings will pass the assertWarnsOnceRegex
# context manager
with self.assertWarnsOnceRegex(UserWarning, '.*non-writable.*'):
torch.from_numpy(a)
torch.from_numpy(a)
@onlyNativeDeviceTypes
def test_complex_half_experimental_warning(self, device):
msg = 'ComplexHalf support is experimental'
with self.assertWarnsOnceRegex(UserWarning, msg):
t = torch.randn(3, dtype=torch.chalf, device=device)
with self.assertWarnsOnceRegex(UserWarning, msg):
torch.rand(3, dtype=torch.chalf, device=device)
with self.assertWarnsOnceRegex(UserWarning, msg):
torch.empty(3, dtype=torch.chalf, device=device)
with self.assertWarnsOnceRegex(UserWarning, msg):
torch.ones(3, dtype=torch.chalf, device=device)
with self.assertWarnsOnceRegex(UserWarning, msg):
torch.zeros(3, dtype=torch.chalf, device=device)
with self.assertWarnsOnceRegex(UserWarning, msg):
torch.randn_like(t)
with self.assertWarnsOnceRegex(UserWarning, msg):
torch.rand_like(t)
with self.assertWarnsOnceRegex(UserWarning, msg):
torch.empty_like(t)
with self.assertWarnsOnceRegex(UserWarning, msg):
torch.ones_like(t)
with self.assertWarnsOnceRegex(UserWarning, msg):
torch.zeros_like(t)
with self.assertWarnsOnceRegex(UserWarning, msg):
# t + 1 allocates a new tensor for result using empty
t + 1
# TODO: this test should be in test_nn.py
def test_conv_transposed_backward_agnostic_to_memory_format(self, device):
in_channels = 64
out_channels = 128
scale_factor = 8
batch_size = 8
length = 16
conv = torch.nn.ConvTranspose1d(
in_channels, out_channels, kernel_size=scale_factor * 2, stride=scale_factor).to(device)
layer_norm = torch.nn.LayerNorm(out_channels).to(device)
input_ = torch.randn(batch_size, in_channels, length).to(device).contiguous()
input_ = conv(input_).contiguous()
input_ = layer_norm(input_.transpose(1, 2).contiguous()).contiguous()
input_.sum().backward()
# 3d
conv = torch.nn.ConvTranspose3d(3, 3, kernel_size=3).to(device)
input = torch.randn(batch_size, 3, length, length, length, device=device)
out = conv(input)
out.backward(torch.ones_like(out).transpose(-2, -1))
# TODO: this test should be in test_nn.py
@onlyCUDA
@largeTensorTest('12GB')
def test_conv_transposed_large(self, device):
# ConvTranspose3d works for large input tensors (gh-32866)
in_channels = 64
out_channels = 128
kernel_size = 5
conv = torch.nn.ConvTranspose3d(
in_channels, out_channels, kernel_size=kernel_size,
stride=2, padding=2, output_padding=1).to(device)
x = torch.rand([1, 64, 8, 128, 172]).to(device)
y = conv(x)
def test_is_set_to(self, device):
t1 = torch.empty(3, 4, 9, 10, device=device)
t2 = torch.empty(3, 4, 9, 10, device=device)
t3 = torch.tensor([], device=device).set_(t1)
t4 = t3.clone().resize_(12, 90)
self.assertFalse(t1.is_set_to(t2))
self.assertTrue(t1.is_set_to(t3))
self.assertTrue(t3.is_set_to(t1), "is_set_to should be symmetric")
self.assertFalse(t1.is_set_to(t4))
self.assertFalse(torch.tensor([]).is_set_to(torch.tensor([])),
"Tensors with no storages should not appear to be set "
"to each other")
t1 = torch.tensor([True, True], dtype=torch.bool, device=device)
t2 = torch.tensor([0], dtype=torch.bool, device=device).set_(t1)
self.assertTrue(t1.is_set_to(t2))
# test that sizes must match
t1 = torch.empty([2, 3, 4], device=device)
t2 = t1.view(4, 3, 2)
self.assertFalse(t1.is_set_to(t2))
self.assertFalse(t2.is_set_to(t1))
# test that legacy empty size behavior used to be respected (i.e. all
# empty tensors were logically collapsed to size [0]).
t1 = torch.empty([2, 5, 0], device=device)
t2 = t1.view([0])
self.assertFalse(t1.is_set_to(t2))
self.assertFalse(t2.is_set_to(t1))
# See https://github.com/pytorch/pytorch/issues/72650
@skipIfMps
@skipMeta
@parametrize(
"fn",
[
"dist", "atan2", "pow", "lerp", "add", "sub", "mul", "div", "fmod", "remainder", "eq", "ge", "gt", "le",
"lt", "max", "min", "ne", "addcdiv", "addcmul", "masked_scatter", "masked_select", "masked_fill", "map",
"map2", "copy",
],
)
def test_broadcast(self, fn, device):
# functions with three tensor arguments
fns_3_args = {"map2"}
fns_value_kwarg = {"addcdiv", "addcmul"}
(dims_small, dims_large, dims_full) = self._select_broadcastable_dims()
full1d = torch.randn(*dims_full, device=device).flatten().float()
small = torch.randn(*dims_small, device=device).float()
large = torch.randn(*dims_large, device=device).float()
small_expanded = small.expand(*dims_full)
large_expanded = large.expand(*dims_full)
small2 = None
small2_expanded = None
if fn in fns_3_args or fn in fns_value_kwarg:
# create another smaller tensor
(dims_small2, _, _) = self._select_broadcastable_dims(dims_full)
small2 = torch.randn(*dims_small2, device=device).float()
small2_expanded = small2.expand(*dims_full)
if small.is_cuda and fn in ['map', 'map2']:
# map and map2 are not implementd on CUDA tensors
return
if hasattr(large_expanded, fn):
# run through tensor versions of functions
# and verify fully expanded inputs give same results
expanded = {large: large_expanded, small: small_expanded, small2: small2_expanded}
def tensorfn(myfn, t1, t2):
if fn == "lerp":
return myfn(t1, 0.5)
elif fn == "masked_select":
return myfn(t1 < 0)
elif fn == "masked_scatter":
return myfn(t1 < 0.5, full1d)
elif fn == "masked_fill":
return myfn(t1 < 0.5, 1.0)
elif fn in fns_3_args:
return myfn(1, t1, t2)
elif fn in fns_value_kwarg:
return myfn(t1, t2, value=1)
else:
return myfn(t1)
# test various orders
for first, second, third in [(large, small, small2), (small, large, small2),
(small2, small, large), (small2, large, small)]:
if first is None:
break # ignore last iter when small2 is None
method_expanded = getattr(expanded[first], fn)
method = getattr(first, fn)
r1 = tensorfn(method_expanded, expanded[second], expanded[third])
r2 = tensorfn(method, second, third)
self.assertEqual(r1, r2)
# now for torch. versions of functions
if hasattr(torch, fn):
fntorch = getattr(torch, fn)
expanded = {large: large_expanded, small: small_expanded, small2: small2_expanded}
def torchfn(t1, t2, t3):
if fn == "lerp":
return fntorch(t1, t2, 0.5)
elif fn == "masked_select":
return fntorch(t1, t2 < 0)
elif fn == "masked_scatter":
return fntorch(t1, t2 < 0.5, full1d)
elif fn == "masked_fill":
return fntorch(t1, t2 < 0.5, 1.0)
elif fn in fns_3_args:
return fntorch(t1, 1.0, t2, t3)
elif fn in fns_value_kwarg:
return fntorch(t1, t2, t3, value=1.0)
else:
return fntorch(t1, t2)
# test various orders
for first, second, third in [(large, small, small2), (small, large, small2),
(small2, small, large), (small2, large, small)]:
if first is None:
break # ignore last iter when small2 is None
r1 = torchfn(expanded[first], expanded[second], expanded[third])
r2 = torchfn(first, second, third)
self.assertEqual(r1, r2)
# now for in place functions
# in-place tensor is not broadcastable; test only guaranteed
# to work by broadcasting other argument(s)
if not hasattr(large_expanded, fn + "_"):
return
# need to clone largeExpanded so we can reuse, since functions are in-place
large_expanded_clone = large_expanded.clone()
def tensorfn_inplace(t0, t1, t2=None):
t0_fn = getattr(t0, fn + "_")
if fn == "lerp":
return t0_fn(t1, 0.5)
elif fn == "masked_scatter":
return t0_fn(t1 < 0.5, full1d)
elif fn == "masked_fill":
return t0_fn(t1 < 0.5, 1.0)
elif fn == "map":
return t0_fn(t1, lambda x, y: x + y)
elif fn == "map2":
return t0_fn(t1, t2, lambda x, y, z: x + y + z)
elif fn in fns_3_args:
return t0_fn(1.0, t1, t2)
elif fn in fns_value_kwarg:
return t0_fn(t1, t2, value=1.0)
else:
return t0_fn(t1)
# in-place pointwise operations don't actually work if the in-place
# tensor is 0-strided (numpy has the same issue)
if (0 not in large_expanded.stride() and 0 not in large_expanded_clone.stride()):
r1 = tensorfn_inplace(large_expanded, small_expanded, small2_expanded)
r2 = tensorfn_inplace(large_expanded_clone, small, small2)
self.assertEqual(r1, r2)
def broadcastable(t0, t1, t2=None):
try:
t1.expand_as(t0)
if t2 is not None:
t2.expand_as(t0)
except RuntimeError:
return False
return True
def _test_in_place_broadcastable(t0, t1, t2=None):
if not broadcastable(t0, t1, t2):
same_size = t0.numel() == t1.numel() and (t0.numel() == t2.numel() if t2 is not None else True)
if not same_size:
self.assertRaises(RuntimeError, lambda: tensorfn_inplace(t0, t1, t2))
else:
tensorfn_inplace(t0, t1, t2)
if fn not in fns_3_args and fn not in fns_value_kwarg:
_test_in_place_broadcastable(small, large_expanded)
_test_in_place_broadcastable(small, large)
else:
_test_in_place_broadcastable(small2, small_expanded, large_expanded)
_test_in_place_broadcastable(small2, small, large)
@unittest.skipIf(IS_FBCODE and IS_REMOTE_GPU, "cublas runtime error")
@onlyCUDA
@wrapDeterministicFlagAPITest
def test_cublas_config_nondeterministic_alert(self, device):
test_cases = [
# (function, (tensor sizes))
('mm', ((2, 2), (2, 2),)),
('mv', ((2, 2), (2,),)),
('bmm', ((1, 2, 2), (1, 2, 2),))]
test_configs = [
# (CuBLAS workspace config, is deterministic)
('garbage', False),
(None, False),
(':4096:8', True),
(':16:8', True)]
cublas_var_name = 'CUBLAS_WORKSPACE_CONFIG'
is_cuda10_2_or_higher = (
(torch.version.cuda is not None)
and ([int(x) for x in torch.version.cuda.split(".")] >= [10, 2]))