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pytorch/test/test_nn.py

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# Owner(s): ["module: nn"]
import contextlib
import math
import random
import unittest
import io
import itertools
import warnings
import pickle
from copy import deepcopy
from itertools import product
from functools import partial
from collections import OrderedDict
from tempfile import NamedTemporaryFile
from unittest import SkipTest
import torch
from torch import inf, nan
import torch.autograd.forward_ad as fwAD
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.utils.rnn as rnn_utils
from torch.nn.utils import clip_grad_norm_, clip_grad_value_
from torch.nn.utils import parameters_to_vector, vector_to_parameters
from torch.nn.utils.fusion import fuse_conv_bn_weights
from torch.nn.utils.fusion import fuse_linear_bn_weights
from torch.nn import Parameter
from torch.nn.parallel._functions import Broadcast
from torch.testing._internal.common_dtype import integral_types, get_all_math_dtypes, floating_types
from torch.testing._internal.common_utils import freeze_rng_state, run_tests, TestCase, skipIfNoLapack, skipIfRocm, \
TEST_NUMPY, TEST_SCIPY, TEST_WITH_CROSSREF, TEST_WITH_ROCM, \
download_file, get_function_arglist, load_tests, skipIfMps,\
IS_PPC, \
parametrize as parametrize_test, subtest, instantiate_parametrized_tests, \
skipIfTorchDynamo, IS_WINDOWS, gcIfJetson, set_default_dtype
from torch.testing._internal.common_cuda import TEST_CUDA, TEST_MULTIGPU, TEST_CUDNN, TEST_CUDNN_VERSION
from torch.testing._internal.common_nn import NNTestCase, NewModuleTest, CriterionTest, \
module_tests, criterion_tests, loss_reference_fns, _create_basic_net, \
ctcloss_reference, new_module_tests, single_batch_reference_fn, _test_bfloat16_ops, _test_module_empty_input
from torch.testing._internal.common_device_type import instantiate_device_type_tests, dtypes, \
dtypesIfCUDA, precisionOverride, skipCUDAIfCudnnVersionLessThan, onlyCUDA, onlyCPU, \
skipCUDAIfRocm, skipCUDAIf, skipCUDAIfNotRocm, \
onlyNativeDeviceTypes, deviceCountAtLeast, largeTensorTest, expectedFailureMeta, skipMeta, get_all_device_types
from hypothesis import given
import torch.testing._internal.hypothesis_utils as hu
from torch.testing._internal.common_utils import _assertGradAndGradgradChecks, gradcheck, gradgradcheck, \
GRADCHECK_NONDET_TOL
from torch.testing._internal.common_utils import dtype2prec_DONTUSE
from torch.testing._internal.common_cuda import tf32_on_and_off, tf32_is_not_fp32, tf32_off, tf32_on
from torch.types import _TensorOrTensors
AMPERE_OR_ROCM = TEST_WITH_ROCM or tf32_is_not_fp32()
# load_tests from common_utils is used to automatically filter tests for
# sharding on sandcastle. This line silences flake warnings
load_tests = load_tests
if TEST_SCIPY:
import scipy.signal
import scipy.ndimage
if TEST_NUMPY:
import numpy as np
# WARNING: If you add a new top-level test case to this file, you MUST
# update test/run_test.py to list it, otherwise it will NOT be run in
# CI.
class TestNN(NNTestCase):
_do_cuda_memory_leak_check = True
_do_cuda_non_default_stream = True
def _forward(self, module, input: _TensorOrTensors):
with freeze_rng_state():
if isinstance(input, tuple):
return module(*input)
else:
return module(input)
def _backward(self, module, input: _TensorOrTensors, output, grad_output, create_graph=False):
output.backward(grad_output, retain_graph=True, create_graph=create_graph)
if isinstance(input, tuple):
return tuple(i.grad.data if i.grad is not None else None for i in input)
else:
return input.grad.data if input.grad is not None else None
def _forward_criterion(self, criterion, input, target, extra_args=None):
if extra_args is None:
extra_args = tuple()
if isinstance(input, tuple):
args = input + (target,) + extra_args
output = criterion(*args)
else:
output = criterion(input, target, *extra_args)
return output
def _backward_criterion(self, criterion, input, output, target, gradOutput=None, extra_args=None):
if extra_args is None:
extra_args = tuple()
input_tuple = input if isinstance(input, tuple) else (input,)
output_tuple = output if isinstance(output, tuple) else (output,)
for i in input_tuple:
if i.grad is not None:
i.grad.data.zero_()
args = input_tuple + (target,) + extra_args
if gradOutput is None:
gradOutput = torch.ones(())
criterion(*args).backward(gradOutput.to(output_tuple[0]))
if isinstance(input, tuple):
return tuple(i.grad.data for i in input)
else:
return input.grad.data
def _zero_grad_parameters(self, module):
for p in module.parameters():
if p.grad is not None:
with torch.no_grad():
p.grad.zero_()
p.grad.detach_()
def _get_parameters(self, module):
params = []
d_params = []
for p in module.parameters():
params.append(p)
d_params.append(p.grad)
return params, d_params
def test_parse_to(self):
# Test for buggy use of THPMemoryFormat_New
self.assertEqual(
repr(torch._C._nn._parse_to(memory_format=torch.contiguous_format)[3]),
"torch.contiguous_format"
)
def test_requires_grad_(self):
m = _create_basic_net()[-1]
assert len(list(m.buffers())) > 0, 'invalid test'
assert all(not b.requires_grad for b in m.buffers()) > 0, 'invalid test'
assert len(list(m.parameters())) > 0, 'invalid test'
assert all(p.requires_grad for p in m.parameters()) > 0, 'invalid test'
for requires_grad in (False, True):
self.assertIs(m.requires_grad_(requires_grad), m)
for p in m.parameters():
self.assertEqual(p.requires_grad, requires_grad)
for b in m.buffers():
self.assertFalse(b.requires_grad)
def test_module_backcompat(self):
from torch.serialization import SourceChangeWarning
path = download_file('https://download.pytorch.org/test_data/linear.pt')
with warnings.catch_warnings():
warnings.simplefilter('ignore', SourceChangeWarning)
m = torch.load(path)
input = torch.randn(2, 3, dtype=torch.float)
self.assertEqual(m(input).size(), (2, 5))
def test_module_super_init(self):
class MyMixin:
def __init__(self, *a, **kw):
super().__init__(*a, **kw)
self.mixin_init = True
class MyModuleWithMixinBefore(MyMixin, nn.Module):
pass
class MyModuleWithMixinAfter(nn.Module, MyMixin):
pass
self.assertTrue(hasattr(MyModuleWithMixinBefore(), 'mixin_init'))
self.assertFalse(hasattr(MyModuleWithMixinAfter(), 'mixin_init'))
nn.Module.call_super_init = True
self.assertTrue(hasattr(MyModuleWithMixinBefore(), 'mixin_init'))
self.assertTrue(hasattr(MyModuleWithMixinAfter(), 'mixin_init'))
nn.Module.call_super_init = False
MyModuleWithMixinBefore.call_super_init = True
MyModuleWithMixinAfter.call_super_init = True
self.assertTrue(hasattr(MyModuleWithMixinBefore(), 'mixin_init'))
self.assertTrue(hasattr(MyModuleWithMixinAfter(), 'mixin_init'))
MyModuleWithMixinBefore.call_super_init = False
MyModuleWithMixinAfter.call_super_init = False
def test_share_memory(self):
class Net(nn.Module):
def __init__(self):
super().__init__()
self.p = nn.Parameter(torch.eye(5))
self.par = nn.ParameterList()
self.par.append(nn.Parameter(torch.randn(10)))
def forward(self, inp):
# NB: dead code
return inp.clone()
net = Net()
for p in net.parameters():
self.assertFalse(p.storage().is_shared())
for b in net.buffers():
self.assertFalse(b.storage().is_shared())
net.share_memory()
for p in net.parameters():
self.assertTrue(p.storage().is_shared())
for b in net.buffers():
self.assertTrue(b.storage().is_shared())
def test_to(self):
m = nn.Linear(3, 5)
self.assertIs(m, m.to('cpu'))
self.assertIs(m, m.to('cpu', dtype=torch.float32))
self.assertEqual(m.double(), m.to(torch.float64))
self.assertRaises(RuntimeError, lambda: m.to('cpu', copy=True))
if torch.cuda.is_available():
for cuda in ['cuda', 'cuda:0' if torch.cuda.device_count() == 1 else 'cuda:1']:
m2 = m.cuda(device=cuda)
self.assertIs(m2, m2.to(cuda))
self.assertEqual(m, m2.to('cpu'))
self.assertEqual(m2, m.to(cuda))
self.assertIs(m2, m2.to(dtype=torch.float32))
self.assertEqual(m2.double(), m2.to(dtype=torch.float64))
def test_zero_grad(self):
i = torch.randn(2, 5, requires_grad=True)
module = nn.Linear(5, 5)
for p in module.parameters():
p.requires_grad = False
module.zero_grad()
module.weight.requires_grad = True
module.zero_grad()
self.assertIsNone(module.weight.grad) # uninitialized grad
module(i).sum().backward()
self.assertIsNotNone(module.weight.grad)
self.assertGreater(module.weight.grad.data.abs().sum(), 0)
module.zero_grad()
self.assertIsNone(module.weight.grad)
module.bias.requires_grad = True
module.zero_grad()
self.assertIsNone(module.weight.grad)
self.assertIsNone(module.bias.grad)
module(i).sum().backward()
self.assertIsNotNone(module.weight.grad)
self.assertIsNotNone(module.bias.grad)
self.assertGreater(module.weight.grad.data.abs().sum(), 0)
self.assertGreater(module.bias.grad.data.abs().sum(), 0)
module.zero_grad(set_to_none=False) # Force set to zeros.
self.assertEqual(module.weight.grad.data, module.weight.data.clone().zero_())
self.assertEqual(module.bias.grad.data, module.bias.data.clone().zero_())
module.zero_grad()
self.assertIsNone(module.weight.grad)
self.assertIsNone(module.bias.grad)
def test_no_grad(self):
for dtype in [torch.bfloat16, torch.float, torch.double]:
module = nn.Conv2d(2, 5, kernel_size=3, padding=1).to(dtype)
input = torch.randn(1, 2, 10, 10).to(dtype)
x = input
y = input.clone()
output = module(x)
self.assertTrue(output.requires_grad)
output.backward(torch.ones(1, 5, 10, 10))
with torch.no_grad():
output2 = module(y)
self.assertFalse(output2.requires_grad)
self.assertRaises(RuntimeError, lambda: output2.backward(torch.ones(1, 5, 10, 10)))
def test_parameters_and_named_parameters(self):
def names(named_parameters):
return [k for k, _ in named_parameters]
l, n, s = _create_basic_net()
self.assertEqual(len(list(l.parameters())), 1)
self.assertEqual(
names(l.named_parameters()),
['layer_dummy_param'])
self.assertEqual(len(list(n.parameters())), 2)
self.assertEqual(
names(n.named_parameters()),
['dummy_param', 'l1.layer_dummy_param'])
self.assertEqual(len(list(n.parameters(recurse=False))), 1)
self.assertEqual(
names(n.named_parameters(recurse=False)),
['dummy_param'])
self.assertEqual(len(list(s.parameters())), 2)
self.assertEqual(
names(s.named_parameters()),
['0.dummy_param', '0.l1.layer_dummy_param'])
def test_named_parameters_remove_duplicate(self):
def names(named_parameters):
return [k for k, _ in named_parameters]
class M1(nn.Module):
def __init__(self):
super().__init__()
self.param1 = nn.Parameter(torch.empty(3, 3))
self.param2 = self.param1
m1 = M1()
self.assertEqual(names(m1.named_parameters()),
["param1"])
self.assertEqual(names(m1.named_parameters(remove_duplicate=False)),
["param1", "param2"])
class M2(nn.Module):
def __init__(self):
super().__init__()
self.mod1 = nn.Linear(3, 4, bias=False)
self.mod2 = self.mod1
m2 = M2()
self.assertEqual(names(m2.named_parameters()),
["mod1.weight"])
self.assertEqual(names(m2.named_parameters(remove_duplicate=False)),
["mod1.weight", "mod2.weight"])
def test_buffers_and_named_buffers(self):
def names(named_buffers):
return [k for k, _ in named_buffers]
l, n, s = _create_basic_net()
self.assertEqual(len(list(l.buffers())), 1)
self.assertEqual(
names(l.named_buffers()),
['layer_dummy_buf'])
self.assertEqual(len(list(n.buffers())), 2)
self.assertEqual(
names(n.named_buffers()),
['dummy_buf', 'l1.layer_dummy_buf'])
self.assertEqual(len(list(n.buffers(recurse=False))), 1)
self.assertEqual(
names(n.named_buffers(recurse=False)),
['dummy_buf'])
self.assertEqual(len(list(s.buffers())), 2)
self.assertEqual(
names(s.named_buffers()),
['0.dummy_buf', '0.l1.layer_dummy_buf'])
# test remove_duplicate
class M(nn.Module):
def __init__(self):
super().__init__()
self.register_buffer("buffer1", torch.empty(3, 5))
self.register_buffer("buffer2", self.buffer1)
m = M()
self.assertEqual(names(m.named_buffers()),
["buffer1"])
self.assertEqual(names(m.named_buffers(remove_duplicate=False)),
["buffer1", "buffer2"])
def test_call_supports_python_dict_output(self):
class Net(nn.Module):
def __init__(self):
super().__init__()
self.l1 = nn.Linear(10, 20)
self.register_backward_hook(self.hook)
self.check_backward_hook_flag = False
def hook(self, module, grad_out, grad_in):
self.check_backward_hook_flag = True
def forward(self, inputs):
return {"output": self.l1(inputs).sum()}
net = Net()
model_output = net(torch.randn([5, 10]))
model_output["output"].backward()
self.assertTrue(net.check_backward_hook_flag)
def test_children(self):
l1 = nn.Linear(2, 2)
l2 = nn.Linear(2, 2)
l3 = nn.Linear(2, 2)
l4 = nn.Linear(2, 2)
subnet = nn.Sequential(l3, l4)
s = nn.Sequential(l1, l2, l1, l2, subnet)
self.assertEqual(list(s.children()), [l1, l2, subnet])
def test_train_errors_for_invalid_mode(self):
class SubclassNet(nn.Module):
def __init__(self):
super().__init__()
self.l1 = nn.Linear(2, 2)
def forward(self, inputs):
return self.l1(inputs)
subclass_net = SubclassNet()
sequential_net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2))
error_modes = ["invalid_str", torch.device('cpu')]
modules_to_check = [subclass_net, sequential_net]
for error_mode, module in itertools.product(error_modes, modules_to_check):
with self.assertRaises(ValueError):
module.train(error_mode)
def test_dir(self):
linear = nn.Linear(2, 2)
linear._test_submodule = nn.Linear(2, 2)
linear._test_parameter = Parameter(torch.empty(2, 2))
linear.register_buffer('_test_buffer', torch.empty(2, 2))
keys = dir(linear)
self.assertIn('_test_submodule', keys)
self.assertIn('_test_parameter', keys)
self.assertIn('_test_buffer', keys)
for key in keys:
self.assertTrue(hasattr(linear, key))
def test_repr(self):
# no extra information or sub-modules
empty_sequential = nn.Sequential()
expected_repr_empty = 'Sequential()'
self.assertEqual(repr(empty_sequential), expected_repr_empty)
# one liner extra information
linear = nn.Linear(1, 1)
expected_repr_linear = 'Linear(in_features=1, out_features=1, bias=True)'
self.assertEqual(repr(linear), expected_repr_linear)
# sub-modules repr
sequential = nn.Sequential(linear)
expected_repr_sequential = 'Sequential(\n' \
' (0): Linear(in_features=1, out_features=1, bias=True)\n' \
')'
self.assertEqual(repr(sequential), expected_repr_sequential)
def test_dir_digit(self):
model = nn.Sequential(nn.Linear(2, 2))
keys = dir(model)
self.assertNotIn('0', keys)
def test_named_children(self):
l1 = nn.Linear(2, 2)
l2 = nn.Linear(2, 2)
l3 = nn.Linear(2, 2)
l4 = nn.Linear(2, 2)
subnet = nn.Sequential(l3, l4)
s = nn.Sequential()
with self.assertRaises(KeyError):
s.add_module('', l1)
with self.assertRaises(KeyError):
s.add_module('name.with.dot', l1)
s.add_module('layer1', l1)
s.add_module('layer2', l2)
s.add_module('layer3', l1)
s.add_module('layer4', l2)
s.add_module('subnet', subnet)
self.assertEqual(list(s.named_children()), [('layer1', l1), ('layer2', l2), ('subnet', subnet)])
def test_modules(self):
class Net(nn.Module):
def __init__(self):
super().__init__()
self.l1 = l
self.l2 = l
self.param = torch.empty(3, 5)
l = nn.Linear(10, 20)
n = Net()
s = nn.Sequential(n, n, n, n)
self.assertEqual(list(s.modules()), [s, n, l])
def test_named_modules(self):
class Net(nn.Module):
def __init__(self):
super().__init__()
self.l1 = l
self.l2 = l
self.param = torch.empty(3, 5)
self.block = block
l = nn.Linear(10, 20)
l1 = nn.Linear(10, 20)
l2 = nn.Linear(10, 20)
block = nn.Sequential()
block.add_module('linear1', l1)
block.add_module('linear2', l2)
n = Net()
s = nn.Sequential(n, n)
self.assertEqual(list(s.named_modules()), [('', s), ('0', n), ('0.l1', l),
('0.block', block), ('0.block.linear1', l1),
('0.block.linear2', l2)])
# test the option to not remove duplicate module instances
self.assertEqual(list(s.named_modules(remove_duplicate=False)), [
('', s), ('0', n), ('0.l1', l), ('0.l2', l),
('0.block', block), ('0.block.linear1', l1),
('0.block.linear2', l2),
('1', n), ('1.l1', l), ('1.l2', l),
('1.block', block), ('1.block.linear1', l1),
('1.block.linear2', l2)])
def test_register_buffer_raises_error_if_name_is_not_string(self):
m = nn.Module()
expected_error = 'buffer name should be a string. Got '
with self.assertRaisesRegex(TypeError, expected_error + 'int'):
m.register_buffer(1, torch.rand(5))
with self.assertRaisesRegex(TypeError, expected_error + 'NoneType'):
m.register_buffer(None, torch.rand(5))
def test_register_buffer_raises_error_if_attr_exists(self):
m = nn.Module()
m.attribute_name = 5
with self.assertRaises(KeyError):
m.register_buffer('attribute_name', torch.rand(5))
del m.attribute_name
m.register_parameter('attribute_name', nn.Parameter())
with self.assertRaises(KeyError):
m.register_buffer('attribute_name', torch.rand(5))
del m.attribute_name
m.add_module('attribute_name', nn.Module())
with self.assertRaises(KeyError):
m.register_buffer('attribute_name', torch.rand(5))
def test_register_buffer_raises_error_if_not_tensor(self):
m = nn.Module()
with self.assertRaises(TypeError):
m.register_buffer('attribute_name', 5)
def test_register_buffer_allows_overwriting_with_same_name(self):
m = nn.Module()
buffer1 = torch.rand(5)
buffer2 = buffer1 + 5
buffer3 = None
m.register_buffer('buffer_name', buffer1)
self.assertEqual(m.buffer_name, buffer1)
m.register_buffer('buffer_name', buffer2)
self.assertEqual(m.buffer_name, buffer2)
m.register_buffer('buffer_name', buffer3)
self.assertEqual(m.buffer_name, buffer3)
def test_get_buffer(self):
m = nn.Module()
buffer1 = torch.randn(2, 3)
buffer2 = torch.randn(4, 5)
m.register_buffer('foo', buffer1)
m.register_buffer('bar', buffer2)
self.assertEqual(buffer1, m.get_buffer('foo'))
self.assertEqual(buffer2, m.get_buffer('bar'))
def test_get_buffer_from_submodules(self):
class MyModule(nn.Module):
def __init__(self, foo, bar):
super().__init__()
self.sub = Sub(foo, bar)
class Sub(nn.Module):
def __init__(self, foo, bar):
super().__init__()
self.register_buffer('foo', foo)
self.subsub = SubSub(bar)
class SubSub(nn.Module):
def __init__(self, bar):
super().__init__()
self.register_buffer('bar', bar)
foo = torch.randn(2, 3)
bar = torch.randn(4, 5)
m = MyModule(foo, bar)
self.assertEqual(foo, m.get_buffer('sub.foo'))
self.assertEqual(bar, m.get_buffer('sub.subsub.bar'))
def test_buffer_not_persistent(self):
m = nn.Module()
m.register_buffer('buf', torch.rand(5), persistent=False)
self.assertTrue(len(list(m.buffers())) == 1)
self.assertTrue(len(m.state_dict()) == 0)
def test_buffer_not_persistent_del(self):
m = nn.Module()
m.register_buffer('buf', torch.rand(5), persistent=False)
del m.buf
self.assertTrue(len(list(m.buffers())) == 0)
def test_buffer_not_persistent_overwrite(self):
m = nn.Module()
m.register_buffer('buf', torch.rand(5), persistent=False)
m.register_buffer('buf', torch.rand(5))
# can we overwrite a non-persistent buffer with a persistent one?
self.assertTrue(len(list(m.buffers())) == 1)
self.assertTrue(len(m.state_dict()) == 1)
# can we overwrite a persistent buffer with a non-persistent one?
m.register_buffer('buf', torch.rand(5), persistent=False)
self.assertTrue(len(list(m.buffers())) == 1)
self.assertTrue(len(m.state_dict()) == 0)
def test_buffer_not_persistent_assign(self):
m = nn.Module()
m.register_buffer('buf', torch.rand(5), persistent=False)
# Assigning None removes the buffer but if we then assign a new Tensor
# to the same property, it should still be marked as a buffer.
m.buf = None
self.assertTrue(len(list(m.buffers())) == 0)
self.assertTrue(len(m.state_dict()) == 0)
m.buf = torch.rand(5)
self.assertTrue(len(list(m.buffers())) == 1)
self.assertTrue(len(m.state_dict()) == 0)
# Assigning a Parameter removes the buffer.
m.buf = nn.Parameter(torch.rand(5))
self.assertTrue(len(list(m.buffers())) == 0)
self.assertTrue(len(m.state_dict()) == 1)
@unittest.skipIf(not TEST_NUMPY, "numpy not found")
def test_load_state_dict_invalid(self):
m = torch.nn.Linear(2, 2, bias=False)
state_dict = {'weight': np.random.randn(2, 2)}
with self.assertRaisesRegex(RuntimeError,
"expected torch.Tensor or Tensor-like object from checkpoint but received"):
m.load_state_dict(state_dict)
state_dict = {'weight': ((1., 1.), (2., 2.))}
with self.assertRaisesRegex(RuntimeError,
"expected torch.Tensor or Tensor-like object from checkpoint but received"):
m.load_state_dict(state_dict)
def test_load_state_dict_type(self):
m = nn.Module()
with self.assertRaisesRegex(TypeError,
"Expected state_dict to be dict-like, got"):
m.load_state_dict("")
with self.assertRaisesRegex(TypeError,
"Expected state_dict to be dict-like, got"):
m.load_state_dict(2)
def test_buffer_not_persistent_load(self):
m = nn.Module()
m.register_buffer('buf', torch.rand(5), persistent=False)
m.load_state_dict({})
def test_register_parameter_raises_error_if_name_is_not_string(self):
m = nn.Module()
expected_error = 'parameter name should be a string. Got '
with self.assertRaisesRegex(TypeError, expected_error + 'int'):
m.register_parameter(1, nn.Parameter())
with self.assertRaisesRegex(TypeError, expected_error + 'NoneType'):
m.register_parameter(None, nn.Parameter())
def test_register_parameter_raises_error_if_attr_exists(self):
m = nn.Module()
m.attribute_name = 5
with self.assertRaises(KeyError):
m.register_parameter('attribute_name', nn.Parameter())
del m.attribute_name
m.register_buffer('attribute_name', torch.rand(5))
with self.assertRaises(KeyError):
m.register_parameter('attribute_name', nn.Parameter())
del m.attribute_name
m.add_module('attribute_name', nn.Module())
with self.assertRaises(KeyError):
m.register_parameter('attribute_name', nn.Parameter())
def test_register_parameter_allows_overwriting_with_same_name(self):
m = nn.Module()
param1 = nn.Parameter(torch.rand(5))
param2 = nn.Parameter(param1.data + 5)
param3 = None
m.register_parameter('param_name', param1)
self.assertEqual(m.param_name, param1)
m.register_parameter('param_name', param2)
self.assertEqual(m.param_name, param2)
m.register_parameter('param_name', param3)
self.assertEqual(m.param_name, param3)
def test_add_module_raises_error_if_attr_exists(self):
methods_to_test = ['add_module', 'register_module']
for fn in methods_to_test:
m = nn.Module()
m.attribute_name = 5
with self.assertRaises(KeyError):
getattr(m, fn)('attribute_name', nn.Module())
del m.attribute_name
m.register_buffer('attribute_name', torch.rand(5))
with self.assertRaises(KeyError):
getattr(m, fn)('attribute_name', nn.Module())
del m.attribute_name
m.register_parameter('attribute_name', nn.Parameter())
with self.assertRaises(KeyError):
getattr(m, fn)('attribute_name', nn.Module())
@unittest.expectedFailure
def test_getattr_with_property(self):
class Model(nn.Module):
@property
def some_property(self):
return self.something_that_doesnt_exist
model = Model()
with self.assertRaisesRegex(
AttributeError,
r"'Model' object has no attribute 'something_that_doesnt_exist'"):
model.some_property
def test_Sequential_getitem(self):
l1 = nn.Linear(10, 20)
l2 = nn.Linear(20, 30)
l3 = nn.Linear(30, 40)
l4 = nn.Linear(40, 50)
n = nn.Sequential(l1, l2, l3, l4)
self.assertIs(n[0], l1)
self.assertIs(n[1], l2)
self.assertIs(n[2], l3)
self.assertIs(n[3], l4)
self.assertIs(n[torch.tensor(3, dtype=torch.int64)], l4)
self.assertEqual(n[1:], nn.Sequential(l2, l3, l4))
self.assertEqual(n[3:], nn.Sequential(l4))
self.assertEqual(n[:-1], nn.Sequential(l1, l2, l3))
self.assertEqual(n[:-3], nn.Sequential(l1))
self.assertEqual(n[::-1], nn.Sequential(l4, l3, l2, l1))
def test_Sequential_setitem(self):
l1 = nn.Linear(10, 20)
l2 = nn.Linear(20, 30)
l3 = nn.Linear(30, 40)
l4 = nn.Linear(40, 50)
n = nn.Sequential(l1, l2, l3)
n[0] = l4
n[-1] = l4
n[torch.tensor(1, dtype=torch.int16)] = l1
self.assertIs(n[0], l4)
self.assertIs(n[1], l1)
self.assertIs(n[2], l4)
def test_Sequential_setitem_named(self):
l1 = nn.Linear(10, 20)
l2 = nn.Linear(20, 30)
l3 = nn.Linear(30, 40)
l4 = nn.Linear(40, 50)
n = nn.Sequential(OrderedDict([
('linear1', l1),
('linear2', l2),
('linear3', l3),
]))
n[0] = l4
n[-1] = l4
self.assertEqual(n.linear1, l4)
self.assertEqual(n.linear3, l4)
def test_Sequential_delitem(self):
l1 = nn.Linear(10, 20)
l2 = nn.Linear(20, 30)
l3 = nn.Linear(30, 40)
l4 = nn.Linear(40, 50)
n = nn.Sequential(l1, l2, l3, l4)
del n[-1]
self.assertEqual(n, nn.Sequential(l1, l2, l3))
del n[1::2]
self.assertEqual(n, nn.Sequential(l1, l3))
def test_Sequential_add(self):
l1 = nn.Linear(1, 2)
l2 = nn.Linear(2, 3)
l3 = nn.Linear(3, 4)
l4 = nn.Linear(4, 5)
n = nn.Sequential(l1, l2)
other = nn.Sequential(l3, l4)
self.assertEqual(n + other, nn.Sequential(l1, l2, l3, l4))
def test_Sequential_iadd(self):
l1 = nn.Linear(10, 20)
l2 = nn.Linear(20, 30)
l3 = nn.Linear(30, 40)
l4 = nn.Linear(40, 50)
n = nn.Sequential(l1, l2, l3)
n2 = nn.Sequential(l4)
n += n2
n2 += n
self.assertEqual(n, nn.Sequential(l1, l2, l3, l4))
self.assertEqual(n2, nn.Sequential(l4, l1, l2, l3, l4))
def test_Sequential_mul(self):
l1 = nn.Linear(10, 20)
l2 = nn.Linear(20, 30)
l3 = nn.Linear(30, 40)
l4 = nn.Linear(40, 50)
n = nn.Sequential(l1, l2, l3, l4)
n2 = n * 2
self.assertEqual(n2, nn.Sequential(l1, l2, l3, l4, l1, l2, l3, l4))
def test_Sequential_rmul(self):
l1 = nn.Linear(10, 20)
l2 = nn.Linear(20, 30)
l3 = nn.Linear(30, 40)
l4 = nn.Linear(40, 50)
n = nn.Sequential(l1, l2, l3, l4)
n2 = 2 * n
self.assertEqual(n2, nn.Sequential(l1, l2, l3, l4, l1, l2, l3, l4))
def test_Sequential_imul(self):
l1 = nn.Linear(10, 20)
l2 = nn.Linear(20, 30)
l3 = nn.Linear(30, 40)
l4 = nn.Linear(40, 50)
n = nn.Sequential(l1, l2, l3, l4)
n *= 2
self.assertEqual(n, nn.Sequential(l1, l2, l3, l4, l1, l2, l3, l4))
n *= 2
self.assertEqual(
n,
nn.Sequential(l1, l2, l3, l4, l1, l2, l3, l4, l1, l2, l3, l4, l1, l2, l3, l4)
)
def test_Sequential_append(self):
l1 = nn.Linear(10, 20)
l2 = nn.Linear(20, 30)
l3 = nn.Linear(30, 40)
l4 = nn.Linear(40, 50)
n = nn.Sequential(l1, l2, l3)
n2 = n.append(l4)
self.assertEqual(n, nn.Sequential(l1, l2, l3, l4))
self.assertEqual(n2, nn.Sequential(l1, l2, l3, l4))
self.assertEqual(nn.Sequential(l1).append(l2).append(l4), nn.Sequential(l1, l2, l4))
def test_Sequential_pop(self):
l1 = nn.Linear(1, 2)
l2 = nn.Linear(2, 3)
l3 = nn.Linear(3, 4)
l4 = nn.Linear(4, 5)
n1 = nn.Sequential(l1, l2, l3, l4)
self.assertEqual(l4, n1.pop(3))
n2 = nn.Sequential(l1, l2, l3)
self.assertEqual(n1, n2)
# check order of the index
for k, mod in zip(range(len(n1)), n1):
self.assertIs(n1[k], mod)
def test_Sequential_insert(self):
l1 = nn.Linear(1, 2)
l2 = nn.Linear(2, 3)
l3 = nn.Linear(3, 4)
n1 = nn.Sequential(l1, l2, l3)
module_1 = nn.Linear(4, 5)
n2 = nn.Sequential(l1, module_1, l2, l3)
self.assertEqual(n1.insert(1, module_1), n2)
# test for negative support
n3 = nn.Sequential(l1, l2, l3)
module_2 = nn.Linear(5, 6)
n4 = nn.Sequential(l1, module_2, l2, l3)
self.assertEqual(n3.insert(-2, module_2), n4)
def test_Sequential_insert_fail_case(self):
l1 = nn.Linear(1, 2)
l2 = nn.Linear(2, 3)
l3 = nn.Linear(3, 4)
module = nn.Linear(5, 6)
# test for error case
n1 = nn.Sequential(l1, l2, l3)
with self.assertRaises(IndexError):
n1.insert(-5, module)
with self.assertRaises(AssertionError):
n1.insert(1, [nn.Linear(6, 7)])
def test_Sequential_extend(self):
l1 = nn.Linear(10, 20)
l2 = nn.Linear(20, 30)
l3 = nn.Linear(30, 40)
l4 = nn.Linear(40, 50)
n1 = nn.Sequential(l1, l2)
n2 = nn.Sequential(l3, l4)
n3 = nn.Sequential(l1, l2)
for l in n2:
n1.append(l)
n3.extend(n2)
self.assertEqual(n3, n1)
def test_ModuleList(self):
modules = [nn.ReLU(), nn.Linear(5, 5)]
module_list = nn.ModuleList(modules)
def check():
self.assertEqual(len(module_list), len(modules))
for m1, m2 in zip(modules, module_list):
self.assertIs(m1, m2)
for m1, m2 in zip(modules, module_list.children()):
self.assertIs(m1, m2)
for i in range(len(modules)):
self.assertIs(module_list[i], modules[i])
check()
modules += [nn.Conv2d(3, 4, 3)]
module_list += [modules[-1]]
check()
modules = modules + [nn.Conv2d(3, 4, 3, bias=False), nn.GELU()]
module_list = module_list + nn.ModuleList(modules[-2:])
check()
modules.insert(1, nn.Linear(3, 2))
module_list.insert(1, modules[1])
check()
modules.append(nn.Tanh())
module_list.append(modules[-1])
check()
next_modules = [nn.Linear(5, 5), nn.Sigmoid()]
modules.extend(next_modules)
module_list.extend(next_modules)
check()
modules[2] = nn.Conv2d(5, 3, 2)
module_list[2] = modules[2]
check()
modules[-1] = nn.Conv2d(5, 2, 1)
module_list[-1] = modules[-1]
check()
idx = torch.tensor(2, dtype=torch.int32)
modules[2] = nn.Conv2d(5, 3, 2)
module_list[idx] = modules[2]
self.assertIs(module_list[idx], modules[2])
check()
self.assertEqual(module_list[1:], nn.ModuleList(modules[1:]))
self.assertEqual(module_list[3:], nn.ModuleList(modules[3:]))
self.assertEqual(module_list[:-1], nn.ModuleList(modules[:-1]))
self.assertEqual(module_list[:-3], nn.ModuleList(modules[:-3]))
self.assertEqual(module_list[::-1], nn.ModuleList(modules[::-1]))
del module_list[-1]
self.assertEqual(module_list, nn.ModuleList(modules[:-1]))
del module_list[1::2]
self.assertEqual(module_list, nn.ModuleList(modules[:-1][0::2]))
with self.assertRaises(TypeError):
module_list += nn.ReLU()
with self.assertRaises(TypeError):
module_list.extend(nn.ReLU())
l1 = nn.Linear(1, 2)
l2 = nn.Linear(2, 3)
l3 = nn.Linear(3, 2)
l4 = nn.Linear(2, 3)
subnet = nn.Sequential(l3, l4)
s = nn.Sequential(
OrderedDict([
("layer1", l1),
("layer2", l2),
("layer3", l3),
("layer4", l4),
("subnet_layer", subnet)
])
)
modules = list(s.modules())
module_list = nn.ModuleList()
module_list.extend(s.modules())
check()
modules = [nn.ReLU(), nn.Linear(5, 5), nn.Conv2d(3, 4, 3)]
module_list = nn.ModuleList(modules)
self.assertEqual(modules.pop(1), module_list.pop(1))
self.assertEqual(modules, module_list)
# check order of the index
for k, mod in zip(range(len(module_list)), module_list):
self.assertIs(module_list[k], mod)
# verify the right exception is thrown when trying to "forward" through a ModuleList
self.assertRaises(NotImplementedError, module_list)
self.assertRaises(NotImplementedError, module_list, torch.rand(1, 3))
def test_ModuleDict(self):
modules = OrderedDict([
('act', nn.ReLU()),
('conv', nn.Conv2d(10, 10, 5)),
('fc', nn.Linear(5, 5)),
])
module_dict = nn.ModuleDict(modules)
def check():
self.assertEqual(len(module_dict), len(modules))
for k1, m2 in zip(modules, module_dict.children()):
self.assertIs(modules[k1], m2)
for k1, k2 in zip(modules, module_dict):
self.assertIs(modules[k1], module_dict[k2])
for k in module_dict:
self.assertIs(module_dict[k], modules[k])
for k in module_dict.keys():
self.assertIs(module_dict[k], modules[k])
for k, v in module_dict.items():
self.assertIs(modules[k], v)
for k1, m2 in zip(modules, module_dict.values()):
self.assertIs(modules[k1], m2)
for k in modules.keys():
self.assertTrue(k in module_dict)
check()
modules['conv'] = nn.Conv2d(3, 4, 3)
module_dict['conv'] = modules['conv']
check()
next_modules = [
('fc2', nn.Linear(5, 5)),
('act', nn.Sigmoid()),
]
modules.update(next_modules)
module_dict.update(next_modules)
check()
next_modules = OrderedDict([
('fc3', nn.Linear(5, 5)),
('act2', nn.Sigmoid()),
])
modules.update(next_modules)
module_dict.update(next_modules)
check()
next_modules = {
'fc4': nn.Linear(5, 5),
'act3': nn.Sigmoid()
}
modules.update(next_modules.items())
module_dict.update(next_modules)
check()
next_modules = nn.ModuleDict([
('fc5', nn.Linear(5, 5)),
('act4', nn.Sigmoid()),
])
modules.update(next_modules)
module_dict.update(next_modules)
check()
del module_dict['fc']
del modules['fc']
check()
with self.assertRaises(TypeError):
module_dict.update(nn.ReLU())
with self.assertRaises(TypeError):
module_dict.update([nn.ReLU()])
with self.assertRaises(ValueError):
module_dict.update([[nn.ReLU()]])
with self.assertRaises(TypeError):
module_dict[1] = nn.ReLU()
s = nn.Sequential(modules)
module_dict = nn.ModuleDict(s.named_children())
check()
c = module_dict.pop('conv')
self.assertIs(c, modules['conv'])
modules.pop('conv')
check()
module_dict.clear()
self.assertEqual(len(module_dict), 0)
modules.clear()
check()
# verify the right exception is thrown when trying to "forward" through a ModuleDict
self.assertRaises(NotImplementedError, module_dict)
self.assertRaises(NotImplementedError, module_dict, torch.rand(1, 3))
def test_ParameterList(self):
def make_param():
return Parameter(torch.randn(2, 2))
parameters = [make_param(), make_param()]
param_list = nn.ParameterList(parameters)
def check():
self.assertEqual(len(parameters), len(param_list))
for p1, p2 in zip(parameters, param_list):
self.assertIs(p1, p2)
for p1, p2 in zip(filter(lambda x: isinstance(x, Parameter), parameters), param_list.parameters()):
self.assertIs(p1, p2)
for i in range(len(parameters)):
self.assertIs(parameters[i], param_list[i])
check()
parameters += [make_param()]
param_list += [parameters[-1]]
check()
parameters.append(make_param())
param_list.append(parameters[-1])
check()
next_params = [make_param(), make_param()]
parameters.extend(next_params)
param_list.extend(next_params)
check()
parameters[2] = make_param()
param_list[2] = parameters[2]
check()
parameters[-1] = make_param()
param_list[-1] = parameters[-1]
check()
idx = torch.tensor(2, dtype=torch.int32)
parameters[2] = make_param()
param_list[idx] = parameters[2]
self.assertIs(param_list[idx], parameters[2])
check()
self.assertEqual(param_list[1:], nn.ParameterList(parameters[1:]))
self.assertEqual(param_list[3:], nn.ParameterList(parameters[3:]))
self.assertEqual(param_list[:-1], nn.ParameterList(parameters[:-1]))
self.assertEqual(param_list[:-3], nn.ParameterList(parameters[:-3]))
self.assertEqual(param_list[::-1], nn.ParameterList(parameters[::-1]))
with self.assertRaises(TypeError):
param_list += make_param()
with self.assertRaises(TypeError):
param_list.extend(make_param())
l1 = nn.Linear(1, 2)
l2 = nn.Linear(2, 3)
l3 = nn.Linear(3, 2)
l4 = nn.Linear(2, 3)
subnet = nn.Sequential(l3, l4)
s = nn.Sequential(
OrderedDict([
("layer1", l1),
("layer2", l2),
("layer3", l3),
("layer4", l4),
("subnet_layer", subnet)
])
)
parameters = list(s.parameters())
param_list = nn.ParameterList()
param_list.extend(s.parameters())
check()
param_list.append(torch.rand(2, 2))
self.assertIsInstance(param_list[-1], Parameter)
parameters.append(param_list[-1])
param_list.extend([torch.rand(2, 2), "foo"])
self.assertIsInstance(param_list[-2], Parameter)
self.assertIsInstance(param_list[-1], str)
parameters.extend(param_list[-2:])
param_list += ["bar", torch.rand(2, 2)]
self.assertIsInstance(param_list[-2], str)
self.assertIsInstance(param_list[-1], Parameter)
parameters += param_list[-2:]
check()
def test_ParameterList_meta(self):
p = torch.nn.Parameter(torch.empty(1, device='meta'))
self.assertExpectedInline(str(p), """\
Parameter containing:
tensor(..., device='meta', size=(1,), requires_grad=True)""")
pl = torch.nn.ParameterList([p])
self.assertExpectedInline(str(pl), """ParameterList( (0): Parameter containing: [torch.float32 of size 1])""")
def test_ParameterList_replication(self):
# The actual replication code from DP cannot be used on CPU so doing it manually here
def make_param():
return Parameter(torch.randn(2, 2))
parameters = [make_param(), make_param()]
param_list = nn.ParameterList(parameters)
new_param_list = param_list._replicate_for_data_parallel()
for n, p in param_list.named_parameters():
# Do a view here so that we can check the base later
setattr(new_param_list, n, p.view_as(p))
for p, p2 in zip(param_list, new_param_list):
self.assertEqual(p, p2)
self.assertIsNotNone(p2.grad_fn)
self.assertIs(p2._base, p)
def test_ParameterDict(self):
parameters = OrderedDict([
('p1', Parameter(torch.randn(10, 10))),
('p2', Parameter(torch.randn(10, 10))),
('p3', Parameter(torch.randn(10, 10))),
])
parameter_dict = nn.ParameterDict(parameters)
def check():
self.assertEqual(len(parameter_dict), len(parameters))
for i, (k1, (k2, m2)) in enumerate(zip(parameters, parameter_dict.named_parameters())):
self.assertEqual(k1, k2)
self.assertIs(parameters[k1], m2)
for k1, k2 in zip(parameters, parameter_dict):
self.assertIs(parameters[k1], parameter_dict[k2])
for k in parameter_dict:
self.assertIs(parameter_dict[k], parameters[k])
for k in parameter_dict.keys():
self.assertIs(parameter_dict[k], parameters[k])
for k, v in parameter_dict.items():
self.assertIs(v, parameters[k])
for k1, m2 in zip(parameters, parameter_dict.values()):
self.assertIs(parameters[k1], m2)
for k in parameters.keys():
self.assertTrue(k in parameter_dict)
check()
parameters['p4'] = Parameter(torch.randn(10, 10))
parameter_dict['p4'] = parameters['p4']
check()
next_parameters = [
('p5', Parameter(torch.randn(10, 10))),
('p2', Parameter(torch.randn(10, 10))),
]
parameters.update(next_parameters)
parameter_dict.update(next_parameters)
check()
next_parameters = OrderedDict([
('p6', Parameter(torch.randn(10, 10))),
('p5', Parameter(torch.randn(10, 10))),
])
parameters.update(next_parameters)
parameter_dict.update(next_parameters)
check()
next_parameters = {
'p8': Parameter(torch.randn(10, 10)),
'p7': Parameter(torch.randn(10, 10))
}
parameters.update(sorted(next_parameters.items()))
parameter_dict.update(next_parameters)
check()
next_parameters = nn.ParameterDict([
('p10', Parameter(torch.randn(10, 10))),
('p9', Parameter(torch.randn(10, 10))),
])
parameters.update(next_parameters)
parameter_dict.update(next_parameters)
check()
del parameter_dict['p3']
del parameters['p3']
check()
with self.assertRaises(TypeError):
parameter_dict.update(1)
with self.assertRaises(TypeError):
parameter_dict.update([1])
with self.assertRaises(ValueError):
parameter_dict.update(Parameter(torch.randn(10, 10)))
p_pop = parameter_dict.pop('p4')
self.assertIs(p_pop, parameters['p4'])
parameters.pop('p4')
check()
# Check reverse works
forward = list(iter(parameter_dict))
backward = list(reversed(parameter_dict))
self.assertEqual(len(forward), len(backward))
n = len(forward)
for i in range(n):
self.assertIs(forward[i], backward[n - i - 1])
check()
# Check copy works
copy = parameter_dict.copy()
# Check all keys are present and have shallow copied values
for key in parameter_dict:
self.assertTrue(key in copy)
self.assertEqual(parameter_dict[key], copy[key])
self.assertIs(parameter_dict[key], copy[key])
check()
parameter_dict["p20"] = Parameter(torch.randn(10, 10))
copy["p21"] = Parameter(torch.randn(9, 10))
self.assertTrue("p20" in parameter_dict)
self.assertFalse("p20" in copy)
self.assertFalse("p21" in parameter_dict)
self.assertTrue("p21" in copy)
parameter_dict.pop("p20")
check()
p = Parameter(torch.randn(10, 10))
parameter_dict['p12'] = p
p_popitem = parameter_dict.popitem()
self.assertEqual(p_popitem[0], 'p12')
self.assertIs(p_popitem[1], p)
check()
# Unit test for set_default
# 1. Ensure parameter is correctly inserted when
# the key is not present in `ParameterDict`
assert 'p11' not in parameter_dict
assert 'p11' not in parameters
parameters['p11'] = Parameter(torch.randn(10, 10))
p_setdefault = parameter_dict.setdefault('p11', parameters['p11'])
self.assertIs(p_setdefault, parameters['p11'])
self.assertIs(p_setdefault, parameter_dict['p11'])
check()
# 2. Ensure parameter is NOT inserted when the
# key is already present in `ParameterDict`
p = Parameter(torch.randn(10, 10))
self.assertFalse(parameter_dict.setdefault('p11', p) is p)
check()
# 3. Ensure `None` is inserted when the key is not
# present in `Parameter` and parameter is not specified
self.assertIs(parameter_dict.setdefault('p26'), None)
del parameter_dict['p26']
check()
parameters2 = OrderedDict([
('p13', Parameter(torch.randn(10, 10))),
('p2', Parameter(torch.randn(10, 10))),
('p3', Parameter(torch.randn(10, 10))),
])
parameter_dict2 = nn.ParameterDict(parameters2)
parameters.update(parameters2)
parameter_dict |= parameter_dict2
check()
parameters2 = OrderedDict()
parameter_dict2 = nn.ParameterDict(parameters2)
parameters.update(parameters2)
parameter_dict |= parameter_dict2
check()
parameters2 = OrderedDict([
('p14', Parameter(torch.randn(10, 10))),
('p15', Parameter(torch.randn(10, 10))),
('p13', Parameter(torch.randn(10, 10))),
])
parameter_dict2 = nn.ParameterDict(parameters2)
parameters.update(parameters2)
parameter_dict |= parameter_dict2
check()
# Check __or__ and __ror__ works
parameters2 = OrderedDict([
('p20', Parameter(torch.randn(10, 10))),
('p21', Parameter(torch.randn(10, 10))),
('p22', Parameter(torch.randn(10, 10))),
])
parameter_dict2 = nn.ParameterDict(parameters2)
parameters.update(parameters2)
parameter_dict = parameter_dict | parameter_dict2
check()
parameters2 = OrderedDict([
('p23', Parameter(torch.randn(10, 10))),
('p24', Parameter(torch.randn(10, 10))),
('p25', Parameter(torch.randn(10, 10))),
])
parameter_dict2 = nn.ParameterDict(parameters2)
parameters2.update(parameters)
parameters = parameters2
parameter_dict = parameter_dict2 | parameter_dict
check()
parameters['p17'] = Parameter(torch.randn(10, 10))
parameter_dict['p17'] = parameters['p17']
self.assertIs(parameters['p17'], parameter_dict.get('p17'))
temp_param = Parameter(torch.randn(10, 10))
self.assertIs(parameters['p17'], parameter_dict.get('p17', temp_param))
self.assertIs(None, parameter_dict.get('p18'))
self.assertIs(temp_param, parameter_dict.get('p18', temp_param))
check()
parameter_dict.clear()
self.assertEqual(len(parameter_dict), 0)
parameters.clear()
check()
parameter_dict2 = parameter_dict.fromkeys(['p19', 'p20'])
self.assertEqual({'p19': None, 'p20': None}, parameter_dict2)
check()
parameter_dict2 = parameter_dict.fromkeys(['p19', 'p20'], temp_param)
self.assertEqual({'p19': temp_param, 'p20': temp_param}, parameter_dict2)
check()
parameter_dict['p21'] = torch.rand(2, 2)
self.assertIsInstance(parameter_dict['p21'], Parameter)
parameters['p21'] = parameter_dict['p21']
parameter_dict.update({'p22': torch.rand(2, 2), 'foo': 'bar'})
self.assertIsInstance(parameter_dict['p22'], Parameter)
self.assertIsInstance(parameter_dict['foo'], str)
parameters['p22'] = parameter_dict['p22']
parameters['foo'] = parameter_dict['foo']
def test_ParameterDict_replication(self):
# The actual replication code from DP cannot be used on CPU so doing it manually here
def make_param():
return Parameter(torch.randn(2, 2))
parameters = {"foo": make_param(), "bar": make_param()}
param_dict = nn.ParameterDict(parameters)
new_param_dict = param_dict._replicate_for_data_parallel()
for n, p in param_dict.named_parameters():
# Do a view here so that we can check the base later
setattr(new_param_dict, n, p.view_as(p))
for (k, p), (k2, p2) in zip(param_dict.items(), new_param_dict.items()):
self.assertEqual(k, k2)
self.assertEqual(p, p2)
self.assertIsNotNone(p2.grad_fn)
self.assertIs(p2._base, p)
self.assertEqual(param_dict["foo"], new_param_dict["foo"])
def test_add_module(self):
methods_to_test = ['add_module', 'register_module']
for fn in methods_to_test:
l = nn.Linear(10, 20)
net = nn.Module()
net.l = l
net.l2 = l
getattr(net, fn)('empty', None)
self.assertEqual(net.l, l)
self.assertEqual(net.l2, l)
self.assertEqual(net.empty, None)
getattr(net, fn)('l3', l)
self.assertEqual(net.l3, l)
l3 = nn.Linear(20, 10)
getattr(net, fn)('l', l3)
self.assertEqual(net.l, l3)
self.assertRaises(TypeError, lambda: getattr(net, fn)('x', 'non-module'))
self.assertRaisesRegex(TypeError, 'module name should be a string. Got int',
lambda: getattr(net, fn)(1, l))
self.assertRaisesRegex(TypeError, 'module name should be a string. Got NoneType',
lambda: getattr(net, fn)(None, l))
def test_module_to_argparse(self):
net = nn.Sequential(nn.Linear(3, 3))
cpu = torch.device('cpu')
with self.assertRaises(TypeError):
net.to(cpu, True)
with self.assertRaises(TypeError):
net.to(torch.long)
with self.assertRaises(TypeError):
net.to(None, True)
with self.assertRaises(TypeError):
net.to(cpu, torch.long, True)
with self.assertRaises(TypeError):
net.to(cpu, dtype=torch.long, non_blocking=True)
with self.assertRaises(TypeError):
net.to([])
with self.assertRaises(TypeError):
net.to({}, non_blocking=True)
with self.assertRaises(TypeError):
net.to(torch.tensor(3, dtype=torch.long), non_blocking=True)
with self.assertRaises(TypeError):
net.to(cpu, torch.tensor(3, dtype=torch.long), non_blocking=True)
def test_RNN_nonlinearity(self):
rnn = torch.nn.RNN(1, 10)
self.assertEqual(rnn.nonlinearity, 'tanh')
rnn = torch.nn.RNN(1, 10, nonlinearity='relu')
self.assertEqual(rnn.nonlinearity, 'relu')
with self.assertRaisesRegex(ValueError, 'Unknown nonlinearity'):
rnn = torch.nn.RNN(1, 10, nonlinearity='garbage')
def test_module_apply_inplace_op(self):
def add_one_inplace(t):
return t.add_(1.0)
# Test that applying an in-place operation to a module would bump
# the module's parameters' version counter.
m = nn.Linear(20, 10)
pvm = m.weight.mul(m.weight)
m_weight_version_saved = m.weight._version
m = m._apply(add_one_inplace)
self.assertGreater(m.weight._version, m_weight_version_saved)
with self.assertRaisesRegex(RuntimeError, "modified by an inplace operation"):
pvm.backward(torch.randn(10, 20))
# Test that applying an in-place operation to a module would bump
# the module's parameters' gradients' version counter.
m = nn.Linear(20, 10)
m.weight.grad = torch.randn(10, 20).requires_grad_()
pgm = m.weight.grad.mul(m.weight.grad)
m_weight_grad_version_saved = m.weight.grad._version
m = m._apply(add_one_inplace)
self.assertGreater(m.weight.grad._version, m_weight_grad_version_saved)
with self.assertRaisesRegex(RuntimeError, "modified by an inplace operation"):
pgm.backward(torch.randn(10, 20))
def test_overwrite_module_params_on_conversion(self):
# Test that if the conversion function passed to `module._apply()`
# changes the TensorImpl type of `module`'s parameters, the `module`'s
# parameters are always overwritten, regardless of the value of
# `torch.__future__.get_overwrite_module_params_on_conversion()`.
m = nn.Linear(20, 10)
m.weight.grad = torch.randn(10, 20)
weight_ref = m.weight
weight_grad_ref = m.weight.grad
m = m._apply(lambda t: torch.sparse_coo_tensor(torch.zeros([2, 1]), torch.ones([1]), torch.Size([10, 20])))
self.assertNotEqual(weight_ref.layout, m.weight.layout)
self.assertNotEqual(weight_grad_ref.layout, m.weight.grad.layout)
# Test that under the current default settings
# (`torch.__future__.get_overwrite_module_params_on_conversion() == False`),
# a view to a module's parameters is not pointing to the same storage as
# its base variable after converting the module to a different dtype.
m = nn.Linear(20, 10).float()
mw = m.weight[:]
m.double()
with torch.no_grad():
mw[0][0] = 5
self.assertTrue(mw[0][0].dtype == torch.float)
self.assertTrue(mw._base[0][0].dtype == torch.double)
try:
torch.__future__.set_overwrite_module_params_on_conversion(True)
# Test that if `torch.__future__.get_overwrite_module_params_on_conversion() == True`,
# a view to a module's parameters is still pointing to the same storage as
# its base variable after converting the module to a different dtype.
m = nn.Linear(20, 10).float()
mw = m.weight[:]
m.double()
with torch.no_grad():
mw[0][0] = 5
self.assertTrue(mw[0][0] == mw._base[0][0])
# Test that if `torch.__future__.get_overwrite_module_params_on_conversion() == True`,
# `float_module.double()` doesn't preserve previous references to
# `float_module`'s parameters or gradients.
m = nn.Linear(20, 10).float()
m.weight.grad = torch.randn(10, 20).float()
weight_ref = m.weight
weight_grad_ref = m.weight.grad
m.double()
self.assertNotEqual(weight_ref.dtype, m.weight.dtype)
self.assertNotEqual(weight_grad_ref.dtype, m.weight.grad.dtype)
def add_one_inplace(t):
return t.add_(1.0)
# Test that if `torch.__future__.get_overwrite_module_params_on_conversion() == True`,
# applying an in-place operation to a module would bump the module's
# original parameters' version counter.
m = nn.Linear(20, 10)
pvm = m.weight.mul(m.weight)
weight_ref = m.weight
m_weight_version_saved = weight_ref._version
m = m._apply(add_one_inplace)
# Test that the in-place operation bumps the original parameter's version counter
self.assertGreater(weight_ref._version, m_weight_version_saved)
with self.assertRaisesRegex(RuntimeError, "modified by an inplace operation"):
pvm.backward(torch.randn(10, 20))
# Test that if `torch.__future__.get_overwrite_module_params_on_conversion() == True`,
# applying an in-place operation to a module would bump the module's
# original parameters' gradients' version counter.
m = nn.Linear(20, 10)
m.weight.grad = torch.randn(10, 20).requires_grad_()
pgm = m.weight.grad.mul(m.weight.grad)
weight_grad_ref = m.weight.grad
m_weight_grad_version_saved = weight_grad_ref._version
m = m._apply(add_one_inplace)
self.assertGreater(weight_grad_ref._version, m_weight_grad_version_saved)
with self.assertRaisesRegex(RuntimeError, "modified by an inplace operation"):
pgm.backward(torch.randn(10, 20))
# Test that if `torch.__future__.get_overwrite_module_params_on_conversion() == True`,
# applying an out-of-place operation to a module doesn't bump
# the module's original parameters' version counter.
m = nn.Linear(20, 10)
weight_ref = m.weight
m_weight_version_saved = weight_ref._version
m = m._apply(lambda t: torch.randn(t.shape))
self.assertEqual(weight_ref._version, m_weight_version_saved)
# Test that if `torch.__future__.get_overwrite_module_params_on_conversion() == True`,
# applying an out-of-place operation to a module doesn't bump
# the module's original parameters' gradients' version counter.
m = nn.Linear(20, 10)
m.weight.grad = torch.randn(10, 20).requires_grad_()
weight_grad_ref = m.weight.grad
m_weight_grad_version_saved = weight_grad_ref._version
m = m._apply(lambda t: torch.randn(t.shape))
self.assertEqual(weight_grad_ref._version, m_weight_grad_version_saved)
finally:
torch.__future__.set_overwrite_module_params_on_conversion(False)
def test_type(self):
l = nn.Linear(10, 20)
net = nn.Module()
net.l = l
net.l2 = l
net.add_module('empty', None)
net.register_buffer('indices', torch.LongTensor(1))
net.float()
self.assertIsInstance(l.weight.data, torch.FloatTensor)
self.assertIsInstance(l.bias.data, torch.FloatTensor)
self.assertIsInstance(net.indices, torch.LongTensor)
net.double()
self.assertIsInstance(l.weight.data, torch.DoubleTensor)
self.assertIsInstance(l.bias.data, torch.DoubleTensor)
self.assertIsInstance(net.indices, torch.LongTensor)
net.to(torch.half)
self.assertIsInstance(l.weight.data, torch.HalfTensor)
self.assertIsInstance(l.bias.data, torch.HalfTensor)
self.assertIsInstance(net.indices, torch.LongTensor)
if TEST_CUDA:
net.float().cuda()
self.assertIsInstance(l.weight.data, torch.cuda.FloatTensor)
self.assertIsInstance(l.bias.data, torch.cuda.FloatTensor)
self.assertIsInstance(net.indices, torch.cuda.LongTensor)
net.cpu()
self.assertIsInstance(l.weight.data, torch.FloatTensor)
self.assertIsInstance(l.bias.data, torch.FloatTensor)
self.assertIsInstance(net.indices, torch.LongTensor)
net.to("cuda", torch.double, True)
self.assertIsInstance(l.weight.data, torch.cuda.DoubleTensor)
self.assertIsInstance(l.bias.data, torch.cuda.DoubleTensor)
self.assertIsInstance(net.indices, torch.cuda.LongTensor)
net.to(torch.empty(1, device="cuda:0", dtype=torch.half))
self.assertIsInstance(l.weight.data, torch.cuda.HalfTensor)
self.assertIsInstance(l.bias.data, torch.cuda.HalfTensor)
self.assertIsInstance(net.indices, torch.cuda.LongTensor)
net.to(torch.device("cpu"), non_blocking=True)
self.assertIsInstance(l.weight.data, torch.HalfTensor)
self.assertIsInstance(l.bias.data, torch.HalfTensor)
self.assertIsInstance(net.indices, torch.LongTensor)
net.to(torch.float)
self.assertIsInstance(l.weight.data, torch.FloatTensor)
self.assertIsInstance(l.bias.data, torch.FloatTensor)
net.to(torch.DoubleTensor(1))
self.assertIsInstance(l.weight.data, torch.DoubleTensor)
self.assertIsInstance(l.bias.data, torch.DoubleTensor)
if TEST_CUDA:
net.to(device='cuda', dtype=torch.float)
self.assertIsInstance(l.weight.data, torch.cuda.FloatTensor)
self.assertIsInstance(l.bias.data, torch.cuda.FloatTensor)
def test_non_leaf_parameters(self):
l1 = nn.Linear(10, 10)
l2 = nn.Linear(10, 10)
def assign_weight():
l2.weight = l1.weight + 2
self.assertRaises(TypeError, assign_weight)
# This should work though
l2.weight = Parameter(torch.randn(10, 10))
def test_parameters_to_vector(self):
conv1 = nn.Conv2d(3, 10, 5)
fc1 = nn.Linear(10, 20)
model = nn.Sequential(conv1, fc1)
vec = parameters_to_vector(model.parameters())
self.assertEqual(vec.size(0), 980)
def test_vector_to_parameters(self):
conv1 = nn.Conv2d(3, 10, 5)
fc1 = nn.Linear(10, 20)
model = nn.Sequential(conv1, fc1)
vec = torch.arange(0., 980)
vector_to_parameters(vec, model.parameters())
sample = next(model.parameters())[0, 0, 0]
self.assertTrue(torch.equal(sample.data, vec.data[:5]))
def test_rnn_weight_norm(self):
def check_weight_norm(l, name, num_params):
# This Module has 4 or 5 parameters called:
# 'weight_ih_l0', 'weight_hh_l0', 'bias_ih_l0', 'bias_hh_l0', weight_hr_l0
# Applying weight norm on one of them causes it to become a tensor
l = torch.nn.utils.weight_norm(l, name=name)
self.assertEqual(
sum([isinstance(p, torch.nn.Parameter) for p in l._flat_weights]),
num_params - 1,
)
# Removing the weight norm reparametrization restores the Parameter
l = torch.nn.utils.remove_weight_norm(l, name=name)
self.assertEqual(
sum([isinstance(p, torch.nn.Parameter) for p in l._flat_weights]),
num_params,
)
# Make sure that, upon removal of the reparametrization, the
# `._parameters` and `.named_parameters` contain the right params.
# Specifically, the original weight ('weight_ih_l0') should be placed
# back in the parameters, while the reparametrization components
# ('weight_ih_l0_v' and 'weight_ih_l0_g') should be removed.
self.assertTrue(name in l._parameters)
self.assertIsNotNone(l._parameters[name])
self.assertTrue(name + '_v' not in l._parameters)
self.assertTrue(name + '_g' not in l._parameters)
self.assertTrue(name in dict(l.named_parameters()))
self.assertIsNotNone(dict(l.named_parameters())[name])
self.assertTrue(name + '_v' not in dict(l.named_parameters()))
self.assertTrue(name + '_g' not in dict(l.named_parameters()))
check_weight_norm(torch.nn.LSTM(32, 32), 'weight_ih_l0', 4)
check_weight_norm(torch.nn.LSTM(32, 32, proj_size=16), 'weight_hr_l0', 5)
def test_weight_norm(self):
for dtype in [torch.float, torch.bfloat16]:
input = torch.randn(3, 4, dtype=dtype)
m = nn.Linear(4, 5).to(dtype=dtype)
expected_output = m(input)
# add weight normalization
m = torch.nn.utils.weight_norm(m)
self.assertEqual(m.weight_v.size(), m.weight.size())
self.assertEqual(m.weight_g.size(), (5, 1))
self.assertEqual(m(input), expected_output, atol=dtype2prec_DONTUSE[dtype], rtol=0)
# remove weight norm
m = torch.nn.utils.remove_weight_norm(m)
self.assertFalse(hasattr(m, 'weight_g'))
self.assertFalse(hasattr(m, 'weight_v'))
self.assertEqual(m(input), expected_output, atol=dtype2prec_DONTUSE[dtype], rtol=0)
# test with dim=1
m = torch.nn.utils.weight_norm(m, dim=1)
self.assertEqual(m.weight_v.size(), m.weight.size())
self.assertEqual(m.weight_g.size(), (1, 4))
self.assertEqual(m(input), expected_output, atol=dtype2prec_DONTUSE[dtype], rtol=0)
# test with dim=None
m = nn.Linear(4, 5).to(dtype=dtype)
expected_output = m(input)
m = torch.nn.utils.weight_norm(m, dim=None)
self.assertEqual(m(input), expected_output)
with self.assertRaisesRegex(RuntimeError, 'register two weight_norm hooks'):
m = torch.nn.utils.weight_norm(m)
m = torch.nn.utils.weight_norm(m)
# For float16, the forward of the Module doesn't work but we must still be able
# to register the weight norm as this is often done before sending the Module to
# CUDA.
m = nn.Linear(4, 5, dtype=torch.float16)
m = torch.nn.utils.weight_norm(m)
def test_parameterlistdict_setting_attributes(self):
with warnings.catch_warnings(record=True) as w:
mod = nn.ParameterList(map(nn.Parameter, [torch.rand(2), torch.rand(2)]))
self.assertTrue(len(w) == 0)
with warnings.catch_warnings(record=True) as w:
mod.train()
mod.eval()
self.assertTrue(len(w) == 0)
with warnings.catch_warnings(record=True) as w:
mod = nn.ParameterDict({"a": nn.Parameter(torch.rand(2)), "b": nn.Parameter(torch.rand(2))})
self.assertTrue(len(w) == 0)
with warnings.catch_warnings(record=True) as w:
mod.train()
mod.eval()
self.assertTrue(len(w) == 0)
def test_parameterlistdict_pickle(self):
m = nn.ParameterList(map(nn.Parameter, [torch.rand(2), torch.rand(2)]))
with warnings.catch_warnings(record=True) as w:
m = pickle.loads(pickle.dumps(m))
self.assertTrue(len(w) == 0)
# Test whether loading from older checkpoints works without triggering warnings
m = nn.ParameterList(map(nn.Parameter, [torch.rand(2), torch.rand(2)]))
del m._forward_pre_hooks, m._state_dict_hooks, m._load_state_dict_pre_hooks, m._non_persistent_buffers_set
with warnings.catch_warnings(record=True) as w:
m = pickle.loads(pickle.dumps(m))
self.assertTrue(len(w) == 0)
m = nn.ParameterDict({"a": nn.Parameter(torch.rand(2)), "b": nn.Parameter(torch.rand(2))})
with warnings.catch_warnings(record=True) as w:
m = pickle.loads(pickle.dumps(m))
self.assertTrue(len(w) == 0)
# Test whether loading from older checkpoints works without triggering warnings
m = nn.ParameterDict({"a": nn.Parameter(torch.rand(2)), "b": nn.Parameter(torch.rand(2))})
del m._forward_pre_hooks, m._state_dict_hooks, m._load_state_dict_pre_hooks, m._non_persistent_buffers_set
with warnings.catch_warnings(record=True) as w:
m = pickle.loads(pickle.dumps(m))
self.assertTrue(len(w) == 0)
def test_weight_norm_pickle(self):
m = torch.nn.utils.weight_norm(nn.Linear(5, 7))
m = pickle.loads(pickle.dumps(m))
self.assertIsInstance(m, nn.Linear)
@skipIfTorchDynamo("TorchDynamo fails here for unknown reasons")
@set_default_dtype(torch.double)
def test_spectral_norm(self):
input = torch.randn(3, 5)
m = nn.Linear(5, 7)
m = torch.nn.utils.spectral_norm(m)
self.assertEqual(m.weight_u.size(), torch.Size([m.weight.size(0)]))
# weight_orig should be trainable
self.assertTrue(hasattr(m, 'weight_orig'))
self.assertTrue('weight_orig' in m._parameters)
# weight_u should be just a reused buffer
self.assertTrue(hasattr(m, 'weight_u'))
self.assertTrue('weight_u' in m._buffers)
self.assertTrue('weight_v' in m._buffers)
# weight should be a plain attribute, not counted as a buffer or a param
self.assertFalse('weight' in m._buffers)
self.assertFalse('weight' in m._parameters)
# it should also be sharing storage as `weight_orig`
self.assertEqual(m.weight_orig.storage(), m.weight.storage())
self.assertEqual(m.weight_orig.size(), m.weight.size())
self.assertEqual(m.weight_orig.stride(), m.weight.stride())
m = torch.nn.utils.remove_spectral_norm(m)
self.assertFalse(hasattr(m, 'weight_orig'))
self.assertFalse(hasattr(m, 'weight_u'))
# weight should be converted back as a parameter
self.assertTrue(hasattr(m, 'weight'))
self.assertTrue('weight' in m._parameters)
with self.assertRaisesRegex(RuntimeError, 'register two spectral_norm hooks'):
m = torch.nn.utils.spectral_norm(m)
m = torch.nn.utils.spectral_norm(m)
# test correctness in training/eval modes and cpu/multi-gpu settings
for apply_dp in (True, False):
if apply_dp:
if not TEST_MULTIGPU:
continue
device = torch.device('cuda:0')
def maybe_wrap(m):
return torch.nn.DataParallel(m, [0, 1])
else:
device = torch.device('cpu')
def maybe_wrap(m):
return m
for requires_grad in (True, False):
m = nn.Linear(3, 4).to(device)
m.weight.requires_grad_(requires_grad)
m = torch.nn.utils.spectral_norm(m)
wrapped_m = maybe_wrap(m)
self.assertTrue(hasattr(m, 'weight_u'))
u0 = m.weight_u.clone()
v0 = m.weight_v.clone()
# TEST TRAINING BEHAVIOR
# assert that u and v are updated
input = torch.randn(2, 3, device=device)
out = wrapped_m(input)
self.assertNotEqual(u0, m.weight_u)
self.assertNotEqual(v0, m.weight_v)
# assert that backprop reaches weight_orig
# can't use gradcheck because the function changes as we
# activate through it in training mode
if requires_grad:
torch.autograd.grad(out.sum(), m.weight_orig)
# test backward works with multiple forwards
# it uses training mode so we need to reset `u` and `v` vectors
# to same value at beginning for finite difference test to pass
saved_u = m.weight_u.clone()
saved_v = m.weight_v.clone()
def fn(input):
m.weight_u.data.copy_(saved_u)
m.weight_v.data.copy_(saved_v)
out0 = wrapped_m(input)
out1 = wrapped_m(input)
return out0 + out1
gradcheck(fn, (input.clone().requires_grad_(),), check_batched_grad=False)
# test removing
pre_remove_out = wrapped_m(input)
m = torch.nn.utils.remove_spectral_norm(m)
self.assertEqual(wrapped_m(input), pre_remove_out)
m = torch.nn.utils.spectral_norm(m)
for _ in range(3):
pre_remove_out = wrapped_m(input)
m = torch.nn.utils.remove_spectral_norm(m)
self.assertEqual(wrapped_m(input), pre_remove_out)
# TEST EVAL BEHAVIOR
m = torch.nn.utils.spectral_norm(m)
wrapped_m(input)
last_train_out = wrapped_m(input)
last_train_u = m.weight_u.clone()
last_train_v = m.weight_v.clone()
wrapped_m.zero_grad()
wrapped_m.eval()
eval_out0 = wrapped_m(input)
# assert eval gives same result as last training iteration
self.assertEqual(eval_out0, last_train_out)
# assert doing more iteartion in eval don't change things
self.assertEqual(eval_out0, wrapped_m(input))
self.assertEqual(last_train_u, m.weight_u)
self.assertEqual(last_train_v, m.weight_v)
# FIXME: the code below is flaky when executed with DataParallel
# see https://github.com/pytorch/pytorch/issues/13818
if apply_dp:
continue
# test backward works with multiple forwards in mixed training
# and eval modes
# it uses training mode so we need to reset `u` and `v` vectors
# to same value at beginning for finite difference test to pass
saved_u = m.weight_u.clone()
saved_v = m.weight_v.clone()
def fn(input):
m.weight_u.data.copy_(saved_u)
m.weight_v.data.copy_(saved_v)
wrapped_m.train()
out0 = wrapped_m(input)
wrapped_m.eval()
out1 = wrapped_m(input)
wrapped_m.train()
out2 = wrapped_m(input)
wrapped_m.eval()
out3 = wrapped_m(input)
return out0 + out1 + out2 + out3
gradcheck(fn, (input.clone().requires_grad_(),))
# assert that backprop reaches weight_orig in eval
if requires_grad:
def fn(weight):
return wrapped_m(input)
gradcheck(fn, (m.weight_orig,))
@skipIfNoLapack
def test_spectral_norm_load_state_dict(self):
inp = torch.randn(2, 3)
for activate_times in (0, 3):
# Test backward compatibility
# At version None -> 1: weight becomes not a buffer and v vector becomes a buffer
m = nn.Linear(3, 5)
snm = torch.nn.utils.spectral_norm(m)
snm.train()
for _ in range(activate_times):
snm(inp)
version_latest_ref_state_dict = deepcopy(snm.state_dict())
self.assertEqual({'weight_orig', 'bias', 'weight_u', 'weight_v'}, set(version_latest_ref_state_dict.keys()))
# test that non-strict loading works
non_strict_state_dict = deepcopy(version_latest_ref_state_dict)
non_strict_state_dict['nonsense'] = 'nonsense'
with self.assertRaisesRegex(RuntimeError, r'Unexpected key\(s\) in state_dict: "nonsense"'):
snm.load_state_dict(non_strict_state_dict, strict=True)
snm.load_state_dict(non_strict_state_dict, strict=False)
del non_strict_state_dict['weight_orig']
snm.load_state_dict(non_strict_state_dict, strict=False)
del non_strict_state_dict['weight_u']
snm.load_state_dict(non_strict_state_dict, strict=False)
del non_strict_state_dict['weight_v']
snm.load_state_dict(non_strict_state_dict, strict=False)
non_strict_state_dict['weight'] = snm.weight.detach().clone() # set W as a buffer
snm.load_state_dict(non_strict_state_dict, strict=False)
del non_strict_state_dict._metadata['']['spectral_norm'] # remove metadata info
snm.load_state_dict(non_strict_state_dict, strict=False)
del non_strict_state_dict['weight'] # remove W buffer
snm.load_state_dict(non_strict_state_dict, strict=False)
del non_strict_state_dict['bias']
snm.load_state_dict(non_strict_state_dict, strict=False)
# craft a version None state_dict
version_none_state_dict = deepcopy(version_latest_ref_state_dict)
self.assertIn('spectral_norm', version_none_state_dict._metadata[''])
del version_none_state_dict._metadata['']['spectral_norm'] # remove metadata info
del version_none_state_dict['weight_v'] # remove v vector
version_none_state_dict['weight'] = snm.weight.detach().clone() # set W as a buffer
# normal state_dict
for version_latest_with_metadata in [True, False]:
version_latest_state_dict = deepcopy(version_latest_ref_state_dict)
if not version_latest_with_metadata:
# We want to still load a user-crafted state_dict, one without metadata
del version_latest_state_dict._metadata['']['spectral_norm']
# test that re-wrapping does not matter
m = torch.nn.utils.remove_spectral_norm(snm)
snm = torch.nn.utils.spectral_norm(m)
snm.load_state_dict(version_latest_ref_state_dict)
with torch.no_grad():
snm.eval()
out0_eval = snm(inp)
snm.train()
out1_train = snm(inp)
out2_train = snm(inp)
snm.eval()
out3_eval = snm(inp)
# test that re-wrapping does not matter
m = torch.nn.utils.remove_spectral_norm(snm)
snm = torch.nn.utils.spectral_norm(m)
snm.load_state_dict(version_none_state_dict)
if activate_times > 0:
# since in loading version None state dict, we assume that the
# values in the state dict have gone through at lease one
# forward, we only test for equivalence when activate_times > 0.
with torch.no_grad():
snm.eval()
self.assertEqual(out0_eval, snm(inp))
snm.train()
self.assertEqual(out1_train, snm(inp))
self.assertEqual(out2_train, snm(inp))
snm.eval()
self.assertEqual(out3_eval, snm(inp))
# test that re-wrapping does not matter
m = torch.nn.utils.remove_spectral_norm(snm)
snm = torch.nn.utils.spectral_norm(m)
# Test normal loading
snm.load_state_dict(version_latest_state_dict)
with torch.no_grad():
snm.eval()
self.assertEqual(out0_eval, snm(inp))
snm.train()
self.assertEqual(out1_train, snm(inp))
self.assertEqual(out2_train, snm(inp))
snm.eval()
self.assertEqual(out3_eval, snm(inp))
def test_spectral_norm_dim(self):
inp = torch.randn(2, 3, 10, 12)
m = nn.ConvTranspose2d(3, 4, (5, 6))
m = torch.nn.utils.spectral_norm(m)
# this should not run into incompatible shapes
x = m(inp)
# check that u refers to the same dimension
self.assertEqual(m.weight_u.shape, m.weight_orig[0, :, 0, 0].shape)
def test_spectral_norm_forward(self):
input = torch.randn(3, 5)
m = nn.Linear(5, 7)
m = torch.nn.utils.spectral_norm(m)
# naive forward
_weight, _bias, _u = m.weight_orig, m.bias, m.weight_u
_weight_mat = _weight.view(_weight.size(0), -1)
_v = torch.mv(_weight_mat.t(), _u)
_v = F.normalize(_v, dim=0, eps=1e-12)
_u = torch.mv(_weight_mat, _v)
_u = F.normalize(_u, dim=0, eps=1e-12)
_weight.data /= torch.dot(_u, torch.matmul(_weight_mat, _v))
out_hat = torch.nn.functional.linear(input, _weight, _bias)
expect_out = m(input)
self.assertEqual(expect_out, out_hat)
def test_spectral_norm_pickle(self):
m = torch.nn.utils.spectral_norm(nn.Linear(5, 7))
m = pickle.loads(pickle.dumps(m))
self.assertIsInstance(m, nn.Linear)
def test_threshold_int(self):
x = torch.tensor([-3, -2, -1, 0, 1, 2, 3])
expected = torch.tensor([99, 99, 99, 99, 1, 2, 3])
self.assertEqual(F.threshold(x, 0, 99), expected)
def test_threshold_bfloat16_half(self):
x = torch.randn(100)
for dtype in [torch.bfloat16, torch.half]:
for threshold in [0, -0.5, 0.5, float('inf'), float('-inf'), float('nan')]:
expected = F.threshold(x, threshold, 0).to(dtype=dtype).float()
res_bf16 = F.threshold(x.to(dtype=dtype), threshold, 0).float()
self.assertEqual(res_bf16, expected)
@unittest.skipUnless('fbgemm' in torch.backends.quantized.supported_engines,
'Linear_FP16_weight requires FBGEMM. FBGEMM is only optimized for CPUs'
' with instruction set support avx2 or newer.')
def test_fb_fc_packed(self):
X = np.random.rand(16, 16).astype(np.float32) - 0.5
W = np.random.rand(16, 16).astype(np.float32) - 0.5
b = np.random.rand(16).astype(np.float32) - 0.5
def fc_op(X, W, b):
return np.dot(X, W.T) + b
x_tensor = torch.tensor(X)
w_tensor = torch.tensor(W)
b_tensor = torch.tensor(b)
packed_w_tensor = torch.fbgemm_pack_gemm_matrix_fp16(w_tensor)
actual_output = torch.fbgemm_linear_fp16_weight(x_tensor, packed_w_tensor, b_tensor)
expected_output = fc_op(X, W, b)
torch.testing.assert_close(torch.from_numpy(expected_output), actual_output.cpu(), atol=1e-3, rtol=1e-3)
def test_pad_scalar_error(self):
inputs = torch.tensor(0., requires_grad=True)
self.assertRaises(RuntimeError, lambda: F.pad(inputs, (1, 1)))
self.assertRaises(RuntimeError, lambda: F.pad(inputs, (1,)))
def test_nested_tensor_from_mask(self):
N, L, D = 10, 12, 14
input = torch.rand(N, L, D)
mask = torch.ones(N, L, dtype=torch.bool)
# Leave first row be all True to maintain the nt's size unchanged
for i in range(1, N):
end = torch.randint(1, L, size=()).item()
mask[i, end:] = False
nt = torch._nested_tensor_from_mask(input, mask)
input_convert = nt.to_padded_tensor(0.)
input.masked_fill_(mask.reshape(N, L, 1).logical_not(), 0.)
self.assertEqual(input, input_convert)
def test_nested_tensor_from_mask_error(self):
N, L, D = 10, 12, 14
input = torch.rand(N, L, D)
# Mask is not bool
mask = torch.zeros(N, L, dtype=torch.float)
self.assertRaises(RuntimeError, lambda: torch._nested_tensor_from_mask(input, mask))
# Mask size is not 2
mask = torch.zeros(N, L, D, dtype=torch.bool)
self.assertRaises(RuntimeError, lambda: torch._nested_tensor_from_mask(input, mask))
# Input size is not 3
mask = torch.zeros(N, L, dtype=torch.bool)
input = torch.rand(N, L)
self.assertRaises(RuntimeError, lambda: torch._nested_tensor_from_mask(input, mask))
# Mask size does not match input
mask = torch.zeros(N + 1, L + 1, dtype=torch.bool)
input = torch.rand(N, L, D)
self.assertRaises(RuntimeError, lambda: torch._nested_tensor_from_mask(input, mask))
# Mask is not padding format
mask = torch.ones(N, L, dtype=torch.bool)
mask[0, 0] = False
mask[0, 2] = False
self.assertRaises(RuntimeError, lambda: torch._nested_tensor_from_mask(input, mask))
def test_normalize(self):
inputs = torch.randn(1, 3, 4, 4, requires_grad=True, dtype=torch.double)
self.assertTrue(gradcheck(lambda x: F.normalize(x, p=1, dim=-1), (inputs,)))
self.assertTrue(gradcheck(lambda x: F.normalize(x, p=2, dim=-2), (inputs,)))
inputs = torch.randn((), requires_grad=True)
self.assertTrue(gradcheck(lambda x: F.normalize(x, p=1, dim=-1), (inputs,)))
@unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
# Skip the test for ROCm as per https://github.com/pytorch/pytorch/issues/53190
@skipIfRocm
def test_broadcast_double_backwards_gpu(self):
tensors = (torch.randn(4, 4, device='cuda', requires_grad=True, dtype=torch.double),
torch.randn(4, 4, device='cuda', requires_grad=True, dtype=torch.double),
torch.randn(4, 4, device='cuda', requires_grad=True, dtype=torch.double))
# TODO(#50743): the following segfaults with check_batched_grad=True
_assertGradAndGradgradChecks(self, lambda *i: Broadcast.apply((0, 1), *i), tensors,
check_batched_grad=False)
@unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
def test_broadcast_not_requiring_grad(self):
variables = [
torch.randn(1, 2, device='cuda', requires_grad=True),
torch.randn(1, 2, device='cuda', requires_grad=False),
torch.randn(1, 2, device='cuda', requires_grad=False),
torch.randn(1, 2, device='cuda', requires_grad=True),
torch.randn(1, 2, device='cuda', requires_grad=True),
]
broadcasted_variables = Broadcast.apply((0, 1), *variables)
for output_idx, broadcasted_var in enumerate(broadcasted_variables):
input_var = variables[output_idx % len(variables)]
self.assertEqual(input_var.requires_grad, broadcasted_var.requires_grad)
@unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
def test_broadcast_no_grad(self):
x = torch.randn(1, 2, dtype=torch.float32, requires_grad=True, device='cuda')
with torch.no_grad():
broadcasted = Broadcast.apply((0, 1), x)
self.assertTrue(x.requires_grad)
for output in broadcasted:
self.assertFalse(output.requires_grad)
def test_state_dict(self):
l = nn.Linear(5, 5)
block = nn.Module()
block.conv = nn.Conv2d(3, 3, 3, bias=False)
net = nn.Module()
net.linear1 = l
net.linear2 = l
net.bn = nn.BatchNorm2d(2)
net.block = block
net.add_module('empty', None)
state_dict = net.state_dict()
self.assertEqual(len(state_dict), 10)
self.assertEqual(len(state_dict._metadata), 6)
self.assertIn('', state_dict._metadata)
self.assertIn('linear1', state_dict._metadata)
self.assertIn('linear1.weight', state_dict)
self.assertIn('linear1.bias', state_dict)
self.assertIn('linear2', state_dict._metadata)
self.assertIn('linear2.weight', state_dict)
self.assertIn('linear2.bias', state_dict)
self.assertIn('block', state_dict._metadata)
self.assertIn('block.conv', state_dict._metadata)
self.assertIn('block.conv.weight', state_dict)
self.assertIn('block.conv.weight', state_dict)
self.assertNotIn('block.conv.bias', state_dict)
self.assertIn('bn', state_dict._metadata)
self.assertIn('bn.weight', state_dict)
self.assertIn('bn.bias', state_dict)
self.assertIn('bn.running_var', state_dict)
self.assertIn('bn.running_mean', state_dict)
self.assertIn('bn.num_batches_tracked', state_dict)
self.assertFalse(any(k.startswith('empty') for k in state_dict.keys()))
for k, v in state_dict.items():
param = net
for component in k.split('.'):
param = getattr(param, component)
if isinstance(param, Parameter):
param = param.data
self.assertEqual(v.data_ptr(), param.data_ptr())
l = nn.Linear(5, 5)
state_dict = l.state_dict()
self.assertEqual(len(state_dict), 2)
self.assertEqual(len(state_dict._metadata), 1)
self.assertIn('', state_dict._metadata)
self.assertTrue(state_dict._metadata['']['version'] >= 0)
self.assertEqual(state_dict['weight'].data_ptr(), l.weight.data_ptr())
self.assertEqual(state_dict['bias'].data_ptr(), l.bias.data_ptr())
# Reference https://github.com/pytorch/pytorch/pull/75507#issuecomment-1110291545
self.assertNotWarn(lambda: l.state_dict(destination=dict()), "Should not warn kwarg destination w/o _metadata")
def test_load_state_dict(self):
l = nn.Linear(5, 5)
block = nn.Module()
block.conv1 = nn.Conv2d(3, 3, 3, bias=True)
block.conv2 = nn.Conv2d(3, 3, 3, bias=False)
net = nn.Module()
net.linear1 = l
net.linear2 = l
net.bn = nn.BatchNorm2d(2)
net.block = block
net.add_module('empty', None)
conv1_bias_dtype = block.conv1.bias.dtype
state_dict = net.state_dict()
state_dict.update({
'linear1.weight': torch.ones(5, 5),
'block.conv1.bias': torch.arange(1, 4, dtype=conv1_bias_dtype),
'bn.running_mean': torch.randn(2),
})
# Also test if a DDP state_dict can be loaded from a local model.
ddp_state_dict = net.state_dict()
ddp_state_dict.update({
'module.linear1.weight': torch.ones(5, 5),
'module.block.conv1.bias': torch.arange(1, 4, dtype=conv1_bias_dtype),
'module.bn.running_mean': torch.randn(2),
})
torch.nn.modules.utils.consume_prefix_in_state_dict_if_present(ddp_state_dict, 'module.')
for sd in [state_dict, ddp_state_dict]:
incompatible_keys = net.load_state_dict(sd)
self.assertEqual(len(incompatible_keys.missing_keys), 0)
self.assertEqual(len(incompatible_keys.unexpected_keys), 0)
self.assertNotIn('Incompatible', str(incompatible_keys))
self.assertEqual(net.linear1.weight, sd['linear1.weight'])
self.assertEqual(net.block.conv1.bias, sd['block.conv1.bias'])
self.assertEqual(net.bn.running_mean, sd['bn.running_mean'])
state_dict = net.state_dict()
state_dict.update({'extra': torch.ones(5)})
self.assertRaises(RuntimeError, lambda: net.load_state_dict(state_dict))
incompatible_keys = net.load_state_dict(state_dict, strict=False)
self.assertEqual(len(incompatible_keys.missing_keys), 0)
self.assertEqual(len(incompatible_keys.unexpected_keys), 1)
self.assertIn('extra', incompatible_keys.unexpected_keys)
self.assertIn('Incompatible', str(incompatible_keys))
state_dict = net.state_dict()
state_dict.update({'extra.param': torch.ones(5)})
self.assertRaises(RuntimeError, lambda: net.load_state_dict(state_dict))
incompatible_keys = net.load_state_dict(state_dict, strict=False)
self.assertEqual(len(incompatible_keys.missing_keys), 0)
self.assertEqual(len(incompatible_keys.unexpected_keys), 1)
self.assertIn('extra.param', incompatible_keys.unexpected_keys)
state_dict = net.state_dict()
del state_dict['linear1.weight']
self.assertRaises(RuntimeError, lambda: net.load_state_dict(state_dict))
incompatible_keys = net.load_state_dict(state_dict, strict=False)
self.assertEqual(len(incompatible_keys.missing_keys), 1)
self.assertEqual(len(incompatible_keys.unexpected_keys), 0)
self.assertIn('linear1.weight', incompatible_keys.missing_keys)
state_dict.update({'extra.param': torch.ones(5)})
self.assertRaises(RuntimeError, lambda: net.load_state_dict(state_dict))
incompatible_keys = net.load_state_dict(state_dict, strict=False)
self.assertEqual(len(incompatible_keys.missing_keys), 1)
self.assertEqual(len(incompatible_keys.unexpected_keys), 1)
self.assertIn('linear1.weight', incompatible_keys.missing_keys)
self.assertIn('extra.param', incompatible_keys.unexpected_keys)
state_dict = net.state_dict()
state_dict.update({'bn.running_mean': torch.rand(14, 4)}) # wrong size
self.assertRaises(RuntimeError, lambda: net.load_state_dict(state_dict))
self.assertRaises(RuntimeError, lambda: net.load_state_dict(state_dict, strict=False))
state_dict = net.state_dict()
old_state_dict = deepcopy(state_dict)
state_dict = {
'linear1.weight': torch.ones(5, 5),
'block.conv1.bias': torch.arange(1, 4, dtype=conv1_bias_dtype),
'bn.running_mean': torch.randn(2),
'nonexistent_key': torch.rand(3)
}
net.load_state_dict(state_dict, strict=False)
self.assertEqual(net.linear1.weight, state_dict['linear1.weight'])
self.assertEqual(net.block.conv1.bias, state_dict['block.conv1.bias'])
self.assertEqual(net.bn.running_mean, state_dict['bn.running_mean'])
new_state_dict = net.state_dict()
del old_state_dict['linear1.weight']
del old_state_dict['block.conv1.bias']
del old_state_dict['bn.running_mean']
for k, v, in old_state_dict.items():
self.assertTrue(v.equal(new_state_dict[k]))
def test_load_state_dict_BC(self):
# BatchNormNd
# Added num_batches_tracked buffer at version 2. For state dict with
# earlier versions or no versions, it should provide default value of 0.
bn = nn.BatchNorm2d(3)
state_dict = bn.state_dict()
del state_dict['num_batches_tracked']
state_dict._metadata['']['version'] = 1 # version 1
bn.load_state_dict(state_dict)
self.assertEqual(bn.num_batches_tracked.dtype, torch.long)
self.assertEqual(bn.num_batches_tracked.item(), 0)
del state_dict._metadata['']['version'] # no version
bn.load_state_dict(state_dict)
self.assertEqual(bn.num_batches_tracked.dtype, torch.long)
self.assertEqual(bn.num_batches_tracked.item(), 0)
def test_load_state_dict_child(self):
base_module = nn.Linear(1, 1)
model = base_module
for _ in range(3):
model = nn.Sequential(*[deepcopy(model) for _ in range(10)])
def hook_fn(module, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
module_state_dict = module.state_dict()
self.assertEqual(len(module_state_dict.keys()), len(state_dict.keys()))
model[0][0]._register_load_state_dict_pre_hook(hook_fn, with_module=True)
model.load_state_dict(model.state_dict(), strict=True)
@unittest.skipIf(IS_WINDOWS, "Tempfile permission issue on windows")
def test_register_state_dict_pre_hook_backward_compat(self):
called = False
def my_state_dict_pre_hook(*args, **kwargs):
nonlocal called
called = True
m = nn.Linear(1, 1)
self.assertTrue(hasattr(m, '_state_dict_pre_hooks'))
delattr(m, '_state_dict_pre_hooks')
# Save and load, ensure we can still call state_dict
# without running into issues.
with NamedTemporaryFile() as f:
# Note that torch.save / torch.load is not recommended
# to save / load modules.
torch.save(m, f.name)
m = torch.load(f.name)
# Ensure we can run state_dict without issues
_ = m.state_dict()
self.assertFalse(called)
m.register_state_dict_pre_hook(my_state_dict_pre_hook)
_ = m.state_dict()
self.assertTrue(called)
def _test_register_state_dict_pre_hook(self, model, submodule):
_state_dict_prefix = "foo."
state_dict_pre_hook_count = 0
keep_var_setting = False
def my_state_dict_pre_hook(module, prefix, keep_vars):
self.assertEqual(keep_vars, keep_var_setting)
nonlocal state_dict_pre_hook_count
state_dict_pre_hook_count += 1
self.assertTrue(prefix.startswith(_state_dict_prefix))
model.register_state_dict_pre_hook(my_state_dict_pre_hook)
# Test to ensure submodules run the hook as well.
submodule.register_state_dict_pre_hook(my_state_dict_pre_hook)
def check_results(model):
nonlocal state_dict_pre_hook_count, keep_var_setting
for keep_var_setting in [True, False]:
_ = model.state_dict(prefix=_state_dict_prefix, keep_vars=keep_var_setting)
self.assertEqual(2, state_dict_pre_hook_count)
state_dict_pre_hook_count = 0
# Test state dict works as expected after model construction
check_results(model)
# Test state dict works as expected after forward
model(torch.ones(10, 3))
check_results(model)
def test_register_state_dict_pre_hook(self):
class MyModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.a = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3), nn.Linear(3, 3))
def forward(self, x):
return self.a(x)
mod = MyModule()
self._test_register_state_dict_pre_hook(mod, mod.a)
def test_register_state_dict_pre_hook_lazy_module(self):
class MyLazyModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.layer1 = nn.LazyLinear(8)
self.layer2 = nn.LazyLinear(5)
def forward(self, x):
return self.layer2(self.layer1(x))
mod = MyLazyModule()
self._test_register_state_dict_pre_hook(mod, mod.layer1)
@skipIfTorchDynamo("TorchDynamo fails here for unknown reasons")
def test_load_state_dict_ref_cycle(self):
# load_state_dict shouldn't cause a reference cycle involving Tensors
import gc
m = torch.nn.LSTM(16, 16, bidirectional=True)
gc.collect()
m.load_state_dict(deepcopy(m).state_dict())
refcycles = gc.collect()
self.assertEqual(refcycles, 0)
def test_load_state_dict_custom(self):
class CustomState(nn.Module):
def __init__(self):
super().__init__()
self.param = torch.nn.Parameter(torch.ones(1))
self.sub = torch.nn.Linear(5, 5)
def _save_to_state_dict(self, destination, prefix, keep_vars):
destination[prefix + "serialized"] = self.param.data + 1
def _load_from_state_dict(self, state_dict, prefix, local_metadata,
strict, missing_keys, unexpected_keys,
error_msgs):
# skip some of the error handling
self.param.data.copy_(state_dict[prefix + "serialized"] - 1)
# use sequential to verify nesting
m = nn.Sequential(CustomState())
with torch.no_grad():
m[0].param[0] = 10
m[0].sub.weight[0, 0] = 555
state_dict = m.state_dict()
self.assertEqual(state_dict["0.serialized"].item(), 11)
self.assertIn("0.sub.weight", state_dict)
self.assertNotIn("0.param", state_dict)
del m
mm = nn.Sequential(CustomState())
self.assertEqual(mm[0].param[0].item(), 1)
mm.load_state_dict(state_dict)
self.assertEqual(mm[0].param[0].item(), 10)
self.assertEqual(mm[0].sub.weight[0, 0].item(), 555)
def test_extra_state(self):
class SubModule(torch.nn.Module):
def __init__(self, foo):
super().__init__()
self.foo = foo
def get_extra_state(self):
return {
'foo': self.foo
}
def set_extra_state(self, state):
self.foo = state['foo']
class MyModule(torch.nn.Module):
def __init__(self, foo, bar):
super().__init__()
self.sub = SubModule(foo)
self.bar = bar
def get_extra_state(self):
return {
'bar': self.bar
}
def set_extra_state(self, state):
self.bar = state['bar']
# Ensure state_dict contains the extra state by loading it into another module.
m = MyModule(3, 'something')
m2 = MyModule(5, 'something else')
m2.load_state_dict(m.state_dict())
self.assertEqual(m.state_dict(), m2.state_dict())
self.assertEqual(m2.bar, m.bar)
self.assertEqual(m2.sub.foo, m.sub.foo)
def test_extra_state_non_dict(self):
class MyModule(torch.nn.Module):
def __init__(self, foo):
super().__init__()
self.foo = foo
def get_extra_state(self):
return self.foo
def set_extra_state(self, state):
self.foo = state
# Test various types of extra state.
for state in ('something', 5, MyModule(3)):
m = MyModule(state)
m2 = MyModule('something else')
m2.load_state_dict(m.state_dict())
self.assertEqual(m.state_dict(), m2.state_dict())
self.assertEqual(m.foo, m2.foo)
def test_load_state_dict_assign_meta(self):
class MyModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(3, 5)
self.bn = nn.BatchNorm1d(5)
def forward(self, input):
return self.bn(self.fc1(input))
net = MyModule()
state_dict = net.state_dict(keep_vars=True)
with torch.device('meta'):
net_meta = MyModule()
net_meta.load_state_dict(state_dict, assign=True)
# Make sure parameters and persistent buffers were assigned
net_meta_state_dict = net_meta.state_dict(keep_vars=True)
for key in state_dict.keys():
if isinstance(state_dict[key], torch.nn.Parameter):
self.assertTrue(state_dict[key] is net_meta_state_dict[key])
# Make sure that ordering of parameters and buffers is preserved
net_named_parameters = net.named_parameters()
net_named_buffers = net.named_buffers()
net_meta_named_parameters = net_meta.named_parameters()
net_meta_named_buffers = net_meta.named_buffers()
for p1, p2 in zip(net_named_parameters, net_meta_named_parameters):
n1, _ = p1
n2, _ = p2
self.assertEqual(n1, n2)
for p1, p2 in zip(net_named_buffers, net_meta_named_buffers):
n1, _ = p1
n2, _ = p2
self.assertEqual(n1, n2)
# Make sure outputs are the same
t = torch.randn(4, 3)
out_net = net(t)
out_net_meta = net_meta(t.clone())
self.assertEqual(out_net, out_net_meta)
def test_load_state_dict_assign_with_optimizer(self):
class MyModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(3, 5)
self.bn = nn.BatchNorm1d(5)
def forward(self, input):
return self.bn(self.fc1(input))
net = MyModule()
opt = torch.optim.Adam(net.parameters(), lr=1000)
x = torch.randn(4, 3)
num_iters = 3
for i in range(num_iters):
opt.zero_grad()
out = net(x)
out.sum().backward()
opt.step()
opt_state_dict = deepcopy(opt.state_dict())
net_state_dict = deepcopy(net.state_dict())
with torch.device('meta'):
net_meta = MyModule()
net_meta.load_state_dict(net_state_dict, assign=True)
# must create optimizer only after loading state_dict when assign=True
opt2 = torch.optim.Adam(net_meta.parameters(), lr=1000)
opt2.load_state_dict(opt_state_dict)
y = x.clone()
for i in range(num_iters):
opt.zero_grad()
out = net(x)
out.sum().backward()
opt.step()
opt2.zero_grad()
out2 = net_meta(y)
out2.sum().backward()
opt2.step()
self.assertEqual(opt.state_dict(), opt2.state_dict())
self.assertEqual(net.state_dict(), net_meta.state_dict())
def test_load_state_dict_assign_shape_stride(self):
# Assigned tensor is allowed to have different properties than initial
# tensor except for shape
class MyModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(3, 5)
self.bn = nn.BatchNorm1d(5)
def forward(self, input):
return self.bn(self.fc1(input))
net = MyModule()
state_dict = net.state_dict()
# loading should be ok if stride is different
state_dict['fc1.weight'] = torch.randn(3, 5).transpose(0, 1)
net2 = MyModule()
net2.load_state_dict(state_dict, strict=False, assign=True)
state_dict['fc1.weight'] = torch.randn(2, 4)
with self.assertRaisesRegex(RuntimeError, "size mismatch for fc1.weight: copying a param with shape"):
net2.load_state_dict(state_dict, strict=False, assign=True)
def test_load_state_dict_warn_assign(self):
with torch.device('meta'):
m = torch.nn.Linear(3, 5)
state_dict = m.state_dict()
state_dict['weight'] = torch.empty_like(state_dict['weight'], device='cpu')
with self.assertWarnsRegex(UserWarning, "for weight: copying from a non-meta parameter in the checkpoint to a meta"):
m.load_state_dict(state_dict)
def test_extra_state_missing_set_extra_state(self):
class MyModule(torch.nn.Module):
def get_extra_state(self):
return {
'foo': 5
}
m = MyModule()
with self.assertRaisesRegex(RuntimeError, 'Unexpected key'):
m.load_state_dict(m.state_dict())
def test_extra_state_missing_get_extra_state(self):
class MyModule(torch.nn.Module):
def set_extra_state(self):
pass
m = MyModule()
with self.assertRaisesRegex(RuntimeError, 'Missing key'):
m.load_state_dict(m.state_dict())
@skipIfTorchDynamo("TorchDynamo fails here for unknown reasons")
def test_parameter_assignment(self):
l = nn.Linear(5, 5)
def num_params():
return len(list(l.parameters()))
self.assertEqual(num_params(), 2)
new_param = Parameter(torch.randn(5, 5))
l.param_name = new_param
self.assertEqual(num_params(), 3)
self.assertObjectIn(new_param, l.parameters())
var = torch.randn(5, 5)
l.var_name = var
self.assertEqual(num_params(), 3)
self.assertNotIn(id(var), map(id, l.parameters()))
# Make sure Variables are not saved as parameters
l.variable_attr = torch.empty(5, 5)
self.assertEqual(num_params(), 3)
l.param_attr = Parameter(torch.empty(5, 5))
self.assertEqual(num_params(), 4)
# It shouldn't be possible to replace a parameter with a Variable
def assign_var():
l.param_attr = torch.empty(5, 5)
self.assertRaises(TypeError, assign_var)
# But replacing it with None should be fine
l.param_attr = None
self.assertEqual(num_params(), 3)
def test_assignment(self):
l = nn.Module()
a = nn.Parameter(torch.randn(2))
b = nn.Parameter(torch.randn(3))
c = nn.Parameter(torch.randn(4))
q = nn.Linear(4, 4)
r = nn.Linear(5, 5)
w = nn.Linear(6, 6)
def test_assignments(get_list, a, b, c):
# Check that None can be shadowed
l.a = None
self.assertIsNone(l.a)
self.assertIn('a', l.__dict__)
l.a = a
self.assertIs(l.a, a)
self.assertEqual(get_list(), [a])
self.assertNotIn('a', l.__dict__)
# Assign second object
l.b = None
self.assertIsNone(l.b)
self.assertIn('b', l.__dict__)
l.b = b
self.assertIs(l.b, b)
self.assertEqual(get_list(), [a, b])
self.assertNotIn('b', l.__dict__)
# Remove and add the object back. Order should be unchanged.
l.a = None
self.assertIsNone(l.a)
self.assertEqual(get_list(), [b])
l.a = a
self.assertIs(l.a, a)
self.assertEqual(get_list(), [a, b])
# Replace object with another one. Order should be unchanged.
l.a = c
self.assertIs(l.a, c)
self.assertEqual(get_list(), [c, b])
# Remove and reassign an attribute. It should appear at the end of the list now.
del l.a
self.assertFalse(hasattr(l, 'a'))
l.a = a
self.assertIs(l.a, a)
self.assertEqual(get_list(), [b, a])
test_assignments(lambda: list(l.parameters()), a, b, c)
del l.a, l.b
self.assertEqual(list(l.parameters()), [])
test_assignments(lambda: list(l.children()), q, r, w)
del l.a, l.b
self.assertEqual(list(l.children()), [])
buf = torch.randn(10)
l.register_buffer('buf', buf)
self.assertIs(l.buf, buf)
l.buf = None
self.assertIs(l.buf, None)
self.assertNotIn('buf', l.__dict__) # should be stored in l._buffers
l.buf = buf
self.assertIn('buf', l.state_dict())
self.assertEqual(l.state_dict()['buf'], buf)
def test_container_copy(self):
class Model(nn.Module):
def __init__(self):
super().__init__()
self.linear = nn.Linear(4, 5)
def forward(self, input):
return self.linear(input)
input = torch.randn(2, 4)
model = Model()
model_cp = deepcopy(model)
self.assertEqual(model(input).data, model_cp(input).data)
model_cp.linear.weight.data[:] = 2
self.assertNotEqual(model(input).data, model_cp(input).data)
def test_RNN_cell(self):
# this is just a smoke test; these modules are implemented through
# autograd so no Jacobian test is needed
for module in (nn.RNNCell, nn.GRUCell):
for bias in (True, False):
input = torch.randn(3, 10)
hx = torch.randn(3, 20)
cell = module(10, 20, bias=bias)
for _ in range(6):
hx = cell(input, hx)
hx.sum().backward()
def test_RNN_cell_forward_zero_hidden_size(self):
input = torch.randn(3, 10)
hx = torch.randn(3, 0)
cell_shared_param = (10, 0)
for cell in (nn.RNNCell(*cell_shared_param, nonlinearity="relu"),
nn.RNNCell(*cell_shared_param, nonlinearity="tanh"),
nn.GRUCell(*cell_shared_param)):
self.assertEqual(cell(input, hx).shape, torch.Size([3, 0]))
def _test_loss_equal_input_target_shape(self, cast):
# Tests losses whose inputs should have the same size.
losses = {
'mse_loss': lambda x, y: F.mse_loss(x, y),
'l1_loss': lambda x, y: F.l1_loss(x, y),
'smooth_l1_loss': lambda x, y: F.smooth_l1_loss(x, y),
'huber_loss': lambda x, y: F.huber_loss(x, y),
'kl_div': lambda x, y: F.kl_div(x, y),
'poisson_nll_loss': lambda x, y: F.poisson_nll_loss(x, y),
}
input = cast(torch.randn(3, 5))
target = cast(torch.randn(5, 3))
for fn in losses.values():
self.assertRaises(Exception, lambda: fn(input, target))
def test_loss_equal_input_target_shape(self):
self._test_loss_equal_input_target_shape(lambda x: x)
def test_mse_loss_size_warning(self):
i = torch.randn((10, 1), requires_grad=True)
t = torch.randn((10,))
with warnings.catch_warnings(record=True) as w:
# Ensure warnings are being shown
warnings.simplefilter("always")
# Trigger Warning
F.mse_loss(i, t)
# Check warning occurs
self.assertEqual(len(w), 1)
self.assertIn('Please ensure they have the same size.', str(w[0]))
def test_gaussian_nll_loss_broadcasting(self):
input = torch.tensor([[0.5, 1.5, 2.5], [2., 4., 6.]])
target_full = torch.tensor([[1., 2., 3.], [1., 2., 3.]])
target_part = torch.tensor([[1., 2., 3.]])
var_full = torch.tensor([[0.5, 0.5, 0.5], [1.5, 1.5, 1.5]])
var_part1 = torch.tensor([[0.5], [1.5]])
var_part2 = torch.tensor([0.5, 1.5])
component_wise_loss = 0.5 * (torch.log(var_full) + (input - target_full)**2 / var_full)
self.assertEqual(component_wise_loss,
F.gaussian_nll_loss(input, target_part, var_full, reduction='none'))
self.assertEqual(component_wise_loss,
F.gaussian_nll_loss(input, target_full, var_part1, reduction='none'))
self.assertEqual(component_wise_loss,
F.gaussian_nll_loss(input, target_full, var_part2, reduction='none'))
self.assertEqual(component_wise_loss,
F.gaussian_nll_loss(input, target_part, var_part1, reduction='none'))
self.assertEqual(component_wise_loss,
F.gaussian_nll_loss(input, target_part, var_part2, reduction='none'))
def test_gaussian_nll_loss_args(self):
input = torch.randn(3, 5)
with self.assertRaisesRegex(ValueError, 'var is of incorrect size'):
target = torch.randn(3, 5)
var = torch.ones(3, 3)
torch.nn.functional.gaussian_nll_loss(input, target, var)
with self.assertRaisesRegex(ValueError, 'var has negative entry/entries'):
var = -1 * torch.ones(3, 5)
torch.nn.functional.gaussian_nll_loss(input, target, var)
def test_KLDivLoss_batch_mean(self):
input_shape = (2, 5)
log_prob1 = F.log_softmax(torch.randn(input_shape), 1)
prob2 = F.softmax(torch.randn(input_shape), 1)
loss = nn.KLDivLoss(reduction='batchmean')
l = loss(log_prob1, prob2)
loss_none_reduce = nn.KLDivLoss(reduction='sum')(log_prob1, prob2)
expected = loss_none_reduce / input_shape[0]
self.assertEqual(l, expected)
def test_KLDivLoss_batch_mean_log_target(self):
input_shape = (2, 5)
log_prob1 = F.log_softmax(torch.randn(input_shape), 1)
log_prob2 = F.log_softmax(torch.randn(input_shape), 1)
loss = nn.KLDivLoss(reduction='batchmean', log_target=True)
l = loss(log_prob1, log_prob2)
loss_none_reduce = nn.KLDivLoss(reduction='sum', log_target=True)(log_prob1, log_prob2)
expected = loss_none_reduce / input_shape[0]
self.assertEqual(l, expected)
def test_CTCLoss_typechecks(self):
target_lengths = torch.tensor([30, 25, 20])
input_lengths = torch.tensor([50, 50, 50])
targets = torch.randint(1, 15, (sum(target_lengths),), dtype=torch.int)
log_probs = torch.randn(50, 3, 15, dtype=torch.float).log_softmax(2)
with self.assertRaises(RuntimeError):
_input_lengths = input_lengths.to(dtype=torch.float)
torch.nn.functional.ctc_loss(log_probs, targets, _input_lengths, target_lengths)
with self.assertRaises(RuntimeError):
target_lengths = target_lengths.to(dtype=torch.float)
torch.nn.functional.ctc_loss(log_probs, targets, input_lengths, target_lengths)
@unittest.skipIf(not TEST_CUDA, 'CUDA not available')
def test_CTCLoss_lengthchecks_cuda(self):
target_lengths = [30, 25, 20]
input_lengths = [50, 50, 50]
targets = torch.randint(1, 15, (3, 29), dtype=torch.long, device='cuda')
log_probs = torch.randn(50, 3, 15, dtype=torch.float, device='cuda').log_softmax(2)
with self.assertRaises(RuntimeError):
torch.nn.functional.ctc_loss(log_probs, targets, input_lengths, target_lengths)
def test_CTCLoss_lengthchecks_cpu(self):
target_lengths = [30, 25, 20]
input_lengths = [50, 50, 50]
targets = torch.randint(1, 15, (3, 29), dtype=torch.int)
log_probs = torch.randn(50, 3, 15, dtype=torch.float).log_softmax(2)
with self.assertRaises(RuntimeError):
torch.nn.functional.ctc_loss(log_probs, targets, input_lengths, target_lengths)
@unittest.skipIf(not TEST_CUDA, 'CUDA not available')
def test_CTCLoss_long_targets(self):
input_length = 4000
vocab_size = 3
batch_size = 4
target_length = 1200
log_probs = torch.randn(input_length, batch_size, vocab_size, dtype=torch.double).log_softmax(2).requires_grad_()
targets = torch.randint(low=1, high=vocab_size - 1, size=(batch_size, target_length), dtype=torch.long)
input_lengths = batch_size * [input_length]
target_lengths = batch_size * [target_length]
res_cpu = torch.nn.functional.ctc_loss(log_probs, targets, input_lengths, target_lengths,
reduction='sum', zero_infinity=True)
grad_out = torch.randn_like(res_cpu)
grad_cpu, = torch.autograd.grad(res_cpu, log_probs, grad_out)
with torch.backends.cudnn.flags(enabled=False):
res_gpu = torch.nn.functional.ctc_loss(log_probs.cuda(), targets.cuda(), input_lengths, target_lengths,
reduction='sum', zero_infinity=True)
grad_gpu, = torch.autograd.grad(res_gpu, log_probs, grad_out.cuda())
self.assertEqual(res_cpu, res_gpu, atol=1e-4, rtol=0)
self.assertEqual(grad_cpu, grad_gpu, atol=1e-4, rtol=0)
@unittest.skipIf(not TEST_CUDA, 'CUDA not available')
def test_CTCLoss_critical_target_len(self):
# cudnn has an unexpected problem with target length 256, see issue #53505
N = 1
S = 256
C = 10
T = 500
target = torch.randint(low=1, high=C, size=(S,), dtype=torch.int)
input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.int)
target_lengths = torch.tensor(S, dtype=torch.int)
inp = torch.randn(T, N, C, dtype=torch.float, device='cuda').log_softmax(2).requires_grad_()
with cudnn.flags(enabled=True):
res_gpu = torch.nn.functional.ctc_loss(inp, target, input_lengths, target_lengths, reduction='none')
res_cpu = torch.nn.functional.ctc_loss(inp.cpu(), target, input_lengths, target_lengths, reduction='none')
self.assertEqual(res_cpu, res_gpu, atol=1e-3, rtol=0)
@unittest.skipIf(not TEST_CUDA, 'CUDA not available')
def test_CTCLoss_zero_infinity(self):
target_lengths = [60, 25, 20]
input_lengths = [50, 50, 50]
targets = torch.randint(1, 15, (sum(target_lengths),), dtype=torch.int, device='cuda')
log_probs = torch.randn(50, 3, 15, dtype=torch.float, device='cuda').log_softmax(2).requires_grad_()
res = torch.nn.functional.ctc_loss(log_probs, targets, input_lengths, target_lengths,
reduction='sum', zero_infinity=True)
with torch.backends.cudnn.flags(enabled=False):
res2 = torch.nn.functional.ctc_loss(log_probs, targets.cuda().long(), input_lengths, target_lengths,
reduction='sum', zero_infinity=True)
res_cpu = torch.nn.functional.ctc_loss(log_probs.cpu(), targets.cpu(), input_lengths, target_lengths,
reduction='sum', zero_infinity=True)
self.assertEqual(res2, res, atol=1e-4, rtol=0)
self.assertEqual(res_cpu, res.cpu(), atol=1e-4, rtol=0)
g1, = torch.autograd.grad(res, log_probs)
g2, = torch.autograd.grad(res2, log_probs)
g3, = torch.autograd.grad(res_cpu, log_probs)
self.assertEqual(g2, g3, atol=1e-4, rtol=0)
self.assertEqual(g1, g2, atol=1e-4, rtol=0)
self.assertTrue((g1 == g1).all().item()) # check that we don't have NaN
def test_RNN_cell_no_broadcasting(self):
def test(cell_module, input, hx, input_size, hidden_size):
cell = cell_module(input_size, hidden_size)
self.assertRaises(RuntimeError, lambda: cell(input, hx))
def test_all(hidden_size, bad_hx, good_hx, input_size, input):
test(nn.RNNCell, input, bad_hx, input_size, hidden_size)
test(nn.GRUCell, input, bad_hx, input_size, hidden_size)
test(nn.LSTMCell, input, (bad_hx, good_hx), input_size, hidden_size)
test(nn.LSTMCell, input, (good_hx, bad_hx), input_size, hidden_size)
hidden_size = 20
input_size = 10
input = torch.randn(3, input_size)
bad_hx = torch.randn(1, hidden_size)
good_hx = torch.randn(3, hidden_size)
# Test hidden/input batch size broadcasting
test_all(hidden_size, bad_hx, good_hx, input_size, input)
# Test hx's hidden_size vs module's hidden_size broadcasting
bad_hx = torch.randn(3, 1)
test_all(hidden_size, bad_hx, good_hx, input_size, input)
# Test input's input_size vs module's input_size broadcasting
bad_input = torch.randn(3, 1)
test_all(hidden_size, good_hx, good_hx, input_size, bad_input)
def test_LSTM_cell(self):
# this is just a smoke test; these modules are implemented through
# autograd so no Jacobian test is needed
for bias in (True, False):
input = torch.randn(3, 10)
hx = torch.randn(3, 20)
cx = torch.randn(3, 20)
lstm = nn.LSTMCell(10, 20, bias=bias)
for _ in range(6):
hx, cx = lstm(input, (hx, cx))
(hx + cx).sum().backward()
def test_LSTM_cell_forward_input_size(self):
input = torch.randn(3, 11)
hx = torch.randn(3, 20)
cx = torch.randn(3, 20)
lstm = nn.LSTMCell(10, 20)
self.assertRaises(Exception, lambda: lstm(input, (hx, cx)))
def test_LSTM_cell_forward_hidden_size(self):
input = torch.randn(3, 10)
hx = torch.randn(3, 21)
cx = torch.randn(3, 20)
lstm = nn.LSTMCell(10, 20)
self.assertRaises(Exception, lambda: lstm(input, (hx, cx)))
self.assertRaises(Exception, lambda: lstm(input, (cx, hx)))
@unittest.skipIf(not TEST_CUDA, 'CUDA not available')
def test_pack_sequence_batch_sizes_throw(self):
with self.assertRaisesRegex(ValueError, r"batch_sizes should always be on CPU"):
m = nn.LSTM(3, 4, bidirectional=True, num_layers=2).to('cuda')
a = torch.rand(5, 3, device='cuda')
b = torch.tensor([1, 1, 1, 1, 1], device='cuda')
input = nn.utils.rnn.PackedSequence(a, b)
def test_Transformer_cell(self):
# this is just a smoke test; these modules are implemented through
# autograd so no Jacobian test is needed
d_model = 512
nhead = 16
num_encoder_layers = 4
num_decoder_layers = 3
dim_feedforward = 256
dropout = 0.3
bsz = 8
seq_length = 35
tgt_length = 15
for batch_first, src_size, tgt_size in zip((True, False),
[(bsz, seq_length, d_model),
(seq_length, bsz, d_model)],
[(bsz, tgt_length, d_model),
(tgt_length, bsz, d_model)]):
transformer = nn.Transformer(d_model, nhead, num_encoder_layers, num_decoder_layers,
dim_feedforward, dropout, batch_first=batch_first,
dtype=torch.double)
src = torch.randn(src_size, dtype=torch.double)
src_mask = transformer.generate_square_subsequent_mask(seq_length).double()
tgt = torch.randn(tgt_size, dtype=torch.double)
tgt_mask = transformer.generate_square_subsequent_mask(tgt_length).double()
memory_mask = torch.randn(tgt_length, seq_length).double()
src_key_padding_mask = torch.rand(bsz, seq_length) >= 0.5
tgt_key_padding_mask = torch.rand(bsz, tgt_length) >= 0.5
memory_key_padding_mask = torch.rand(bsz, seq_length) >= 0.5
output = transformer(src, tgt,
src_mask=src_mask,
tgt_mask=tgt_mask,
memory_mask=memory_mask,
src_key_padding_mask=src_key_padding_mask,
tgt_key_padding_mask=tgt_key_padding_mask,
memory_key_padding_mask=memory_key_padding_mask)
output.sum().backward()
def test_transformerdecoderlayer(self):
# this is a deterministic test for TransformerDecoderLayer
d_model = 4
nhead = 2
dim_feedforward = 16
dropout = 0.0
bsz = 2
seq_length = 5
tgt_length = 3
for batch_first in (False, True):
def perm_fn(x):
return x.transpose(1, 0) if batch_first else x
model = nn.TransformerDecoderLayer(d_model, nhead, dim_feedforward, dropout,
batch_first=batch_first)
# set constant weights of the model
for idx, p in enumerate(model.parameters()):
x = p.data
sz = x.view(-1).size(0)
shape = x.shape
x = torch.cos(torch.arange(0, sz).float().view(shape))
p.data.copy_(x)
# deterministic input
decoder_input = torch.tensor([[[20., 30., 40., 50.]]])
memory_input = torch.tensor([[[60., 70., 80., 90.]]])
result = model(decoder_input, memory_input)
ref_output = torch.tensor([[[2.314351, 0.094805, -0.671322, 0.101977]]])
result = result.detach().numpy()
ref_output = ref_output.detach().numpy()
self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
np.testing.assert_allclose(result, ref_output, atol=1e-5)
# deterministic input
decoder_input = perm_fn(torch.tensor([[[9., 10., 11., 12.]],
[[11., 12., 13., 14.]]]))
memory_input = torch.tensor([[[1., 2., 3., 4.]]])
result = model(decoder_input, memory_input)
result = result.detach().numpy()
ref_output = perm_fn(torch.tensor([[[2.422245, 0.051716, -0.606338, -0.024756]],
[[2.422245, 0.051716, -0.606338, -0.024756]]]))
ref_output = ref_output.detach().numpy()
self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
np.testing.assert_allclose(result, ref_output, atol=1e-5)
# deterministic input
decoder_input = perm_fn(torch.tensor([[[1., 2., 3., 4.]],
[[5., 6., 7., 8.]]]))
memory_input = perm_fn(torch.tensor([[[9., 10., 11., 12.]],
[[11., 12., 13., 14.]]]))
result = model(decoder_input, memory_input)
ref_output = perm_fn(torch.tensor([[[2.343536, 0.085561, -0.654954, 0.074991]],
[[2.343536, 0.085561, -0.654954, 0.074991]]]))
result = result.detach().numpy()
ref_output = ref_output.detach().numpy()
self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
np.testing.assert_allclose(result, ref_output, atol=1e-5)
# deterministic input
decoder_input = perm_fn(torch.tensor([[[0.4517, 0.6793, 0.5313, 0.0034],
[0.2678, 0.3677, 0.4459, 0.7166]],
[[0.8100, 0.3716, 0.4096, 0.1976],
[0.6958, 0.8844, 0.6081, 0.8315]],
[[0.0494, 0.9343, 0.5955, 0.3830],
[0.5404, 0.3464, 0.9378, 0.6200]]]))
memory_input = perm_fn(torch.tensor([[[0.7462, 0.6653, 0.5679, 0.4891],
[0.5387, 0.1655, 0.3565, 0.0471]],
[[0.8335, 0.2799, 0.5031, 0.2947],
[0.1402, 0.0318, 0.7636, 0.1346]],
[[0.6333, 0.9344, 0.1376, 0.9938],
[0.8924, 0.2872, 0.6692, 0.2944]],
[[0.9897, 0.6915, 0.3154, 0.1733],
[0.8645, 0.3513, 0.3064, 0.0767]],
[[0.8117, 0.2366, 0.4838, 0.7881],
[0.3718, 0.4945, 0.9511, 0.0864]]]))
result = model(decoder_input, memory_input)
ref_output = perm_fn(torch.tensor([[[2.430065, 0.027862, -0.601136, -0.073096],
[2.431935, 0.028907, -0.599809, -0.072488]],
[[2.428457, 0.027053, -0.602275, -0.073462],
[2.431970, 0.029387, -0.599789, -0.071621]],
[[2.431934, 0.028196, -0.599802, -0.073809],
[2.432306, 0.028858, -0.599542, -0.072846]]]))
result = result.detach().numpy()
ref_output = ref_output.detach().numpy()
self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
np.testing.assert_allclose(result, ref_output, atol=1e-5)
# key_padding_mask
key_padding_mask = torch.zeros(2, 3) == 1
result = model(decoder_input, memory_input, tgt_key_padding_mask=key_padding_mask)
ref_output = perm_fn(torch.tensor([[[2.430065, 0.027862, -0.601136, -0.073096],
[2.431935, 0.028907, -0.599809, -0.072488]],
[[2.428457, 0.027053, -0.602275, -0.073462],
[2.431970, 0.029387, -0.599789, -0.071621]],
[[2.431934, 0.028196, -0.599802, -0.073809],
[2.432306, 0.028858, -0.599542, -0.072846]]]))
result = result.detach().numpy()
ref_output = ref_output.detach().numpy()
self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
np.testing.assert_allclose(result, ref_output, atol=1e-5)
# key_padding_mask
key_padding_mask[0, 2] = 1
key_padding_mask[1, 1] = 1
key_padding_mask[1, 2] = 1
result = model(decoder_input, memory_input, tgt_key_padding_mask=key_padding_mask)
ref_output = perm_fn(torch.tensor([[[2.430025, 0.027643, -0.601164, -0.073476],
[2.4323, 0.029375, -0.599553, -0.071881]],
[[2.428523, 0.026838, -0.602226, -0.07391],
[2.432634, 0.029842, -0.599318, -0.071253]],
[[2.432278, 0.028152, -0.599555, -0.074139],
[2.432659, 0.029244, -0.599294, -0.072382]]]))
result = result.detach().numpy()
ref_output = ref_output.detach().numpy()
self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
np.testing.assert_allclose(result, ref_output, atol=1e-5)
# memory_key_padding_mask
key_padding_mask = torch.zeros(2, 5) == 1
result = model(decoder_input, memory_input, memory_key_padding_mask=key_padding_mask)
ref_output = perm_fn(torch.tensor([[[2.430065, 0.027862, -0.601136, -0.073096],
[2.431935, 0.028907, -0.599809, -0.072488]],
[[2.428457, 0.027053, -0.602275, -0.073462],
[2.431970, 0.029387, -0.599789, -0.071621]],
[[2.431934, 0.028196, -0.599802, -0.073809],
[2.432306, 0.028858, -0.599542, -0.072846]]]))
result = result.detach().numpy()
ref_output = ref_output.detach().numpy()
self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
np.testing.assert_allclose(result, ref_output, atol=1e-5)
# memory_key_padding_mask
key_padding_mask[0, 4] = 1
key_padding_mask[1, 3] = 1
key_padding_mask[1, 4] = 1
result = model(decoder_input, memory_input, memory_key_padding_mask=key_padding_mask)
ref_output = perm_fn(torch.tensor([[[2.429757, 0.027358, -0.601351, -0.073816],
[2.432692, 0.028583, -0.599263, -0.073634]],
[[2.428247, 0.02662, -0.602419, -0.074123],
[2.432657, 0.029055, -0.599293, -0.072732]],
[[2.431515, 0.027687, -0.600096, -0.074459],
[2.433075, 0.028543, -0.598987, -0.073985]]]))
result = result.detach().numpy()
ref_output = ref_output.detach().numpy()
self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
np.testing.assert_allclose(result, ref_output, atol=1e-5)
@set_default_dtype(torch.double)
def test_transformerdecoderlayer_gelu(self):
# this is a deterministic test for TransformerDecoderLayer with gelu activation
d_model = 4
nhead = 2
dim_feedforward = 16
dropout = 0.0
bsz = 2
seq_length = 5
tgt_length = 3
for activation, batch_first in product(('gelu', F.gelu, nn.GELU()), (True, False)):
def perm_fn(x):
return x.transpose(1, 0) if batch_first else x
model = nn.TransformerDecoderLayer(d_model, nhead, dim_feedforward, dropout,
activation, batch_first=batch_first)
# set constant weights of the model
for idx, p in enumerate(model.parameters()):
x = p.data
sz = x.view(-1).size(0)
shape = x.shape
x = torch.cos(torch.arange(0, sz).float().view(shape))
p.data.copy_(x)
# deterministic input
decoder_input = torch.tensor([[[20., 30., 40., 50.]]])
memory_input = torch.tensor([[[60., 70., 80., 90.]]])
result = model(decoder_input, memory_input)
ref_output = torch.tensor([[[2.306435, 0.095946, -0.675796, 0.10687]]])
torch.testing.assert_close(result, ref_output, rtol=1e-5, atol=0)
# deterministic input
decoder_input = perm_fn(torch.tensor([[[9., 10., 11., 12.]],
[[11., 12., 13., 14.]]]))
memory_input = perm_fn(torch.tensor([[[1., 2., 3., 4.]]]))
result = model(decoder_input, memory_input)
ref_output = perm_fn(torch.tensor([[[2.415448, 0.054389, -0.610932, -0.0156613]],
[[2.415448, 0.054389, -0.610932, -0.0156613]]]))
torch.testing.assert_close(result, ref_output, rtol=1e-5, atol=0)
# deterministic input
decoder_input = perm_fn(torch.tensor([[[1., 2., 3., 4.]],
[[5., 6., 7., 8.]]]))
memory_input = perm_fn(torch.tensor([[[9., 10., 11., 12.]],
[[11., 12., 13., 14.]]]))
result = model(decoder_input, memory_input)
ref_output = perm_fn(torch.tensor([[[2.338531, 0.087709, -0.65776, 0.080646]],
[[2.338531, 0.087709, -0.65776, 0.080646]]]))
torch.testing.assert_close(result, ref_output, rtol=1e-5, atol=0)
# deterministic input
decoder_input = perm_fn(torch.tensor([[[0.4517, 0.6793, 0.5313, 0.0034],
[0.2678, 0.3677, 0.4459, 0.7166]],
[[0.8100, 0.3716, 0.4096, 0.1976],
[0.6958, 0.8844, 0.6081, 0.8315]],
[[0.0494, 0.9343, 0.5955, 0.3830],
[0.5404, 0.3464, 0.9378, 0.6200]]]))
memory_input = perm_fn(torch.tensor([[[0.7462, 0.6653, 0.5679, 0.4891],
[0.5387, 0.1655, 0.3565, 0.0471]],
[[0.8335, 0.2799, 0.5031, 0.2947],
[0.1402, 0.0318, 0.7636, 0.1346]],
[[0.6333, 0.9344, 0.1376, 0.9938],
[0.8924, 0.2872, 0.6692, 0.2944]],
[[0.9897, 0.6915, 0.3154, 0.1733],
[0.8645, 0.3513, 0.3064, 0.0767]],
[[0.8117, 0.2366, 0.4838, 0.7881],
[0.3718, 0.4945, 0.9511, 0.0864]]]))
result = model(decoder_input, memory_input)
ref_output = perm_fn(torch.tensor([[[2.42049104, 0.03443088, -0.60793706, -0.05436271],
[2.42210631, 0.03546578, -0.60679895, -0.05357488]],
[[2.41907674, 0.0336104, -0.60892977, -0.05490462],
[2.42216881, 0.03586554, -0.6067524, -0.05289126]],
[[2.42205716, 0.03488046, -0.60683681, -0.05460596],
[2.42240309, 0.0354595, -0.60659063, -0.05378816]]]))
torch.testing.assert_close(result, ref_output, rtol=1e-5, atol=0)
def test_transformerdecoder(self):
def get_a_test_layer(use_cuda, activation, batch_first=False):
d_model = 4
nhead = 2
dim_feedforward = 16
dropout = 0.0
device = torch.device("cuda" if use_cuda else "cpu")
layer = nn.TransformerDecoderLayer(
d_model,
nhead,
dim_feedforward=dim_feedforward,
dropout=dropout,
activation=activation,
batch_first=batch_first).to(device)
with torch.no_grad():
# set constant weights of the model
for idx, p in enumerate(layer.parameters()):
x = p.data
sz = x.view(-1).size(0)
shape = x.shape
x = torch.cos(torch.arange(0, sz).float().view(shape))
p.data.copy_(x)
return layer
# this is a deterministic test for TransformerDecoder
for batch_first in (False, True):
def perm_fn(x):
return x.transpose(1, 0) if batch_first else x
activation = F.relu
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
decoder_layer = get_a_test_layer(use_cuda=use_cuda, activation=activation,
batch_first=batch_first)
model = nn.TransformerDecoder(decoder_layer, 1).to(device)
# deterministic input
decoder_input = torch.tensor([[[20., 30., 40., 50.]]]).to(device)
memory_input = torch.tensor([[[60., 70., 80., 90.]]]).to(device)
result = model(decoder_input, memory_input)
ref_output = torch.tensor(
[[[2.314351, 0.094805, -0.671322, 0.101977]]]).to(device)
self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
torch.testing.assert_close(result, ref_output, rtol=1e-7, atol=1e-3)
# deterministic input
decoder_input = perm_fn(torch.tensor([[[9., 10., 11., 12.]],
[[11., 12., 13., 14.]]])).to(device)
memory_input = perm_fn(torch.tensor([[[1., 2., 3., 4.]]])).to(device)
result = model(decoder_input, memory_input)
ref_output = perm_fn(torch.tensor([[[2.422245, 0.051716, -0.606338, -0.024756]],
[[2.422245, 0.051716, -0.606338, -0.024756]]]
)).to(device)
self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
torch.testing.assert_close(result, ref_output, rtol=1e-7, atol=1e-4)
# deterministic input
decoder_input = perm_fn(torch.tensor([[[1., 2., 3., 4.]],
[[5., 6., 7., 8.]]])).to(device)
memory_input = perm_fn(torch.tensor([[[9., 10., 11., 12.]],
[[11., 12., 13., 14.]]])).to(device)
result = model(decoder_input, memory_input)
ref_output = perm_fn(torch.tensor([[[2.343536, 0.085561, -0.654954, 0.074991]],
[[2.343536, 0.085561, -0.654954, 0.074991]]]
)).to(device)
self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
torch.testing.assert_close(result, ref_output, rtol=1e-7, atol=1e-4)
# deterministic input
decoder_input = perm_fn(torch.tensor([[[0.4517, 0.6793, 0.5313, 0.0034],
[0.2678, 0.3677, 0.4459, 0.7166]],
[[0.8100, 0.3716, 0.4096, 0.1976],
[0.6958, 0.8844, 0.6081, 0.8315]],
[[0.0494, 0.9343, 0.5955, 0.3830],
[0.5404, 0.3464, 0.9378, 0.6200]]]
)).to(device)
memory_input = perm_fn(torch.tensor([[[0.7462, 0.6653, 0.5679, 0.4891],
[0.5387, 0.1655, 0.3565, 0.0471]],
[[0.8335, 0.2799, 0.5031, 0.2947],
[0.1402, 0.0318, 0.7636, 0.1346]],
[[0.6333, 0.9344, 0.1376, 0.9938],
[0.8924, 0.2872, 0.6692, 0.2944]],
[[0.9897, 0.6915, 0.3154, 0.1733],
[0.8645, 0.3513, 0.3064, 0.0767]],
[[0.8117, 0.2366, 0.4838, 0.7881],
[0.3718, 0.4945, 0.9511, 0.0864]]]
)).to(device)
result = model(decoder_input, memory_input)
ref_output = perm_fn(torch.tensor([[[2.430065, 0.027862, -0.601136, -0.073096],
[2.431935, 0.028907, -0.599809, -0.072488]],
[[2.428457, 0.027053, -0.602275, -0.073462],
[2.431970, 0.029387, -0.599789, -0.071621]],
[[2.431934, 0.028196, -0.599802, -0.073809],
[2.432306, 0.028858, -0.599542, -0.072846]]]
)).to(device)
self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
torch.testing.assert_close(result, ref_output, rtol=1e-7, atol=1e-5)
# key_padding_mask
key_padding_mask = torch.zeros(2, 3).to(device) == 1
result = model(decoder_input, memory_input,
tgt_key_padding_mask=key_padding_mask)
ref_output = perm_fn(torch.tensor([[[2.430065, 0.027862, -0.601136, -0.073096],
[2.431935, 0.028907, -0.599809, -0.072488]],
[[2.428457, 0.027053, -0.602275, -0.073462],
[2.431970, 0.029387, -0.599789, -0.071621]],
[[2.431934, 0.028196, -0.599802, -0.073809],
[2.432306, 0.028858, -0.599542, -0.072846]]]
)).to(device)
self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
torch.testing.assert_close(result, ref_output, rtol=1e-7, atol=1e-5)
# key_padding_mask
key_padding_mask[0, 2] = 1
key_padding_mask[1, 1] = 1
key_padding_mask[1, 2] = 1
result = model(decoder_input, memory_input,
tgt_key_padding_mask=key_padding_mask)
ref_output = perm_fn(torch.tensor([[[2.430025, 0.027643, -0.601164, -0.073476],
[2.4323, 0.029375, -0.599553, -0.071881]],
[[2.428523, 0.026838, -0.602226, -0.07391],
[2.432634, 0.029842, -0.599318, -0.071253]],
[[2.432278, 0.028152, -0.599555, -0.074139],
[2.432659, 0.029244, -0.599294, -0.072382]]]
)).to(device)
self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
torch.testing.assert_close(result, ref_output, rtol=1e-7, atol=1e-5)
# memory_key_padding_mask
key_padding_mask = torch.zeros(2, 5).to(device) == 1
result = model(decoder_input, memory_input,
memory_key_padding_mask=key_padding_mask)
ref_output = perm_fn(torch.tensor([[[2.430065, 0.027862, -0.601136, -0.073096],
[2.431935, 0.028907, -0.599809, -0.072488]],
[[2.428457, 0.027053, -0.602275, -0.073462],
[2.431970, 0.029387, -0.599789, -0.071621]],
[[2.431934, 0.028196, -0.599802, -0.073809],
[2.432306, 0.028858, -0.599542, -0.072846]]]
)).to(device)
self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
torch.testing.assert_close(result, ref_output, rtol=1e-7, atol=1e-5)
# memory_key_padding_mask
key_padding_mask[0, 4] = 1
key_padding_mask[1, 3] = 1
key_padding_mask[1, 4] = 1
result = model(decoder_input,
memory_input,
memory_key_padding_mask=key_padding_mask)
ref_output = perm_fn(torch.tensor([[[2.429757, 0.027358, -0.601351, -0.073816],
[2.432692, 0.028583, -0.599263, -0.073634]],
[[2.428247, 0.02662, -0.602419, -0.074123],
[2.432657, 0.029055, -0.599293, -0.072732]],
[[2.431515, 0.027687, -0.600096, -0.074459],
[2.433075, 0.028543, -0.598987, -0.073985]]]
)).to(device)
self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
torch.testing.assert_close(result, ref_output, rtol=1e-7, atol=1e-5)
# multiple layers no norm
model = nn.TransformerDecoder(decoder_layer, 2).to(device)
# deterministic input
decoder_input = torch.tensor([[[20., 30., 40., 50.]]]).to(device)
memory_input = torch.tensor([[[60., 70., 80., 90.]]]).to(device)
result = model(decoder_input, memory_input)
ref_output = torch.tensor(
[[[2.31316, 0.0950293, -0.671995, 0.102802]]]).to(device)
self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
torch.testing.assert_close(result, ref_output, rtol=1e-7, atol=1e-3)
# multiple layers no norm
model = nn.TransformerDecoder(decoder_layer, 6).to(device)
# deterministic input
decoder_input = perm_fn(torch.tensor([[[0.4517, 0.6793, 0.5313, 0.0034],
[0.2678, 0.3677, 0.4459, 0.7166]],
[[0.8100, 0.3716, 0.4096, 0.1976],
[0.6958, 0.8844, 0.6081, 0.8315]],
[[0.0494, 0.9343, 0.5955, 0.3830],
[0.5404, 0.3464, 0.9378, 0.6200]]]
)).to(device)
memory_input = perm_fn(torch.tensor([[[0.7462, 0.6653, 0.5679, 0.4891],
[0.5387, 0.1655, 0.3565, 0.0471]],
[[0.8335, 0.2799, 0.5031, 0.2947],
[0.1402, 0.0318, 0.7636, 0.1346]],
[[0.6333, 0.9344, 0.1376, 0.9938],
[0.8924, 0.2872, 0.6692, 0.2944]],
[[0.9897, 0.6915, 0.3154, 0.1733],
[0.8645, 0.3513, 0.3064, 0.0767]],
[[0.8117, 0.2366, 0.4838, 0.7881],
[0.3718, 0.4945, 0.9511, 0.0864]]]
)).to(device)
result = model(decoder_input, memory_input)
ref_output = perm_fn(torch.tensor([[[2.42794, 0.026164, -0.60263, -0.0747591],
[2.43113, 0.0279516, -0.600376, -0.0736896]],
[[2.42794, 0.026164, -0.60263, -0.0747591],
[2.43113, 0.0279516, -0.600376, -0.0736896]],
[[2.42794, 0.026164, -0.60263, -0.0747591],
[2.43113, 0.0279516, -0.600376, -0.0736896]]]
)).to(device)
self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
torch.testing.assert_close(result, ref_output, rtol=1e-7, atol=1e-5)
# multiple layers with norm
# d_model = 4
norm = nn.LayerNorm(4)
model = nn.TransformerDecoder(decoder_layer, 2, norm=norm).to(device)
# deterministic input
decoder_input = torch.tensor([[[20., 30., 40., 50.]]]).to(device)
memory_input = torch.tensor([[[60., 70., 80., 90.]]]).to(device)
result = model(decoder_input, memory_input)
ref_output = torch.tensor(
[[[1.66166, -0.326986, -1.01466, -0.320017]]]).to(device)
self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
torch.testing.assert_close(result, ref_output, rtol=1e-7, atol=1e-3)
# multiple layers with norm
model = nn.TransformerDecoder(decoder_layer, 6, norm=norm).to(device)
# deterministic input
decoder_input = perm_fn(torch.tensor([[[0.4517, 0.6793, 0.5313, 0.0034],
[0.2678, 0.3677, 0.4459, 0.7166]],
[[0.8100, 0.3716, 0.4096, 0.1976],
[0.6958, 0.8844, 0.6081, 0.8315]],
[[0.0494, 0.9343, 0.5955, 0.3830],
[0.5404, 0.3464, 0.9378, 0.6200]]]
)).to(device)
memory_input = perm_fn(torch.tensor([[[0.7462, 0.6653, 0.5679, 0.4891],
[0.5387, 0.1655, 0.3565, 0.0471]],
[[0.8335, 0.2799, 0.5031, 0.2947],
[0.1402, 0.0318, 0.7636, 0.1346]],
[[0.6333, 0.9344, 0.1376, 0.9938],
[0.8924, 0.2872, 0.6692, 0.2944]],
[[0.9897, 0.6915, 0.3154, 0.1733],
[0.8645, 0.3513, 0.3064, 0.0767]],
[[0.8117, 0.2366, 0.4838, 0.7881],
[0.3718, 0.4945, 0.9511, 0.0864]]]
)).to(device)
result = model(decoder_input, memory_input)
ref_output = perm_fn(torch.tensor([[[1.69559, -0.357291, -0.894741, -0.443553],
[1.69571, -0.357363, -0.894154, -0.444196]],
[[1.69559, -0.357291, -0.894741, -0.443553],
[1.69571, -0.357363, -0.894154, -0.444196]],
[[1.69559, -0.357291, -0.894741, -0.443553],
[1.69571, -0.357363, -0.894154, -0.444196]]]
)).to(device)
self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
torch.testing.assert_close(result, ref_output, rtol=1e-7, atol=1e-5)
# gelu activation test cases
activation = "gelu"
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
decoder_layer = get_a_test_layer(use_cuda=use_cuda, activation=activation,
batch_first=batch_first)
model = nn.TransformerDecoder(decoder_layer, 1).to(device)
# deterministic input
decoder_input = torch.tensor([[[20., 30., 40., 50.]]]).to(device)
memory_input = torch.tensor([[[60., 70., 80., 90.]]]).to(device)
result = model(decoder_input, memory_input)
ref_output = torch.tensor([[[2.306435, 0.095946, -0.675796, 0.10687]]]).to(device)
self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
torch.testing.assert_close(result, ref_output, rtol=1e-7, atol=1e-3)
# deterministic input
decoder_input = perm_fn(torch.tensor([[[9., 10., 11., 12.]],
[[11., 12., 13., 14.]]])).to(device)
memory_input = perm_fn(torch.tensor([[[1., 2., 3., 4.]]])).to(device)
result = model(decoder_input, memory_input)
ref_output = perm_fn(torch.tensor([[[2.415448, 0.054389, -0.610932, -0.0156613]],
[[2.415448, 0.054389, -0.610932, -0.0156613]]])).to(device)
self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
torch.testing.assert_close(result, ref_output, rtol=1e-7, atol=1e-4)
# deterministic input
decoder_input = perm_fn(torch.tensor([[[1., 2., 3., 4.]],
[[5., 6., 7., 8.]]])).to(device)
memory_input = perm_fn(torch.tensor([[[9., 10., 11., 12.]],
[[11., 12., 13., 14.]]])).to(device)
result = model(decoder_input, memory_input)
ref_output = perm_fn(torch.tensor([[[2.338531, 0.087709, -0.65776, 0.080646]],
[[2.338531, 0.087709, -0.65776, 0.080646]]])).to(device)
self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
torch.testing.assert_close(result, ref_output, rtol=1e-7, atol=1e-4)
# deterministic input
decoder_input = perm_fn(torch.tensor([[[0.4517, 0.6793, 0.5313, 0.0034],
[0.2678, 0.3677, 0.4459, 0.7166]],
[[0.8100, 0.3716, 0.4096, 0.1976],
[0.6958, 0.8844, 0.6081, 0.8315]],
[[0.0494, 0.9343, 0.5955, 0.3830],
[0.5404, 0.3464, 0.9378, 0.6200]]]
)).to(device)
memory_input = perm_fn(torch.tensor([[[0.7462, 0.6653, 0.5679, 0.4891],
[0.5387, 0.1655, 0.3565, 0.0471]],
[[0.8335, 0.2799, 0.5031, 0.2947],
[0.1402, 0.0318, 0.7636, 0.1346]],
[[0.6333, 0.9344, 0.1376, 0.9938],
[0.8924, 0.2872, 0.6692, 0.2944]],
[[0.9897, 0.6915, 0.3154, 0.1733],
[0.8645, 0.3513, 0.3064, 0.0767]],
[[0.8117, 0.2366, 0.4838, 0.7881],
[0.3718, 0.4945, 0.9511, 0.0864]]]
)).to(device)
result = model(decoder_input, memory_input)
ref_output = perm_fn(torch.tensor([[[2.42049104, 0.03443088, -0.60793706, -0.05436271],
[2.42210631, 0.03546578, -0.60679895, -0.05357488]],
[[2.41907674, 0.0336104, -0.60892977, -0.05490462],
[2.42216881, 0.03586554, -0.6067524, -0.05289126]],
[[2.42205716, 0.03488046, -0.60683681, -0.05460596],
[2.42240309, 0.0354595, -0.60659063, -0.05378816]]]
)).to(device)
self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
torch.testing.assert_close(result, ref_output, rtol=1e-7, atol=1e-5)
@unittest.skipIf(not (TEST_CUDNN and TEST_MULTIGPU), 'CUDNN or multi-gpu not available')
def test_cudnn_rnn_dropout_states_device(self):
rnn = nn.RNN(10, 20, num_layers=2, dropout=.5)
device = 1
input = torch.randn(5, 4, 10).cuda(device)
rnn.cuda(device)
hx = torch.randn(2, 4, 20).cuda(device)
output = rnn(input, hx)
@unittest.skipIf(not TEST_CUDNN, 'CUDNN not available')
@skipIfRocm
def test_cudnn_weight_format(self):
rnns = [
nn.LSTM(10, 20, batch_first=True),
nn.LSTM(10, 20, batch_first=True, proj_size=10),
nn.GRU(10, 20, batch_first=True),
nn.RNN(10, 20, batch_first=True)
]
first_warn = True
for rnn in rnns:
rnn.cuda()
input = torch.randn(5, 4, 10, requires_grad=True, device="cuda")
hx = torch.randn(1, 5, 20, requires_grad=True, device="cuda")
all_vars = [input, hx] + list(rnn.parameters())
if isinstance(rnn, nn.LSTM):
# LSTM with projections has different hx size
if rnn.proj_size > 0:
hx = torch.randn(1, 5, 10, requires_grad=True, device="cuda")
all_vars[1] = hx
cx = torch.randn(1, 5, 20, requires_grad=True, device="cuda")
all_vars[2:2] = [cx]
hx = (hx, cx)
output = rnn(input, hx)
output[0].sum().backward()
grads = [v.grad.data.clone() for v in all_vars]
for v in all_vars:
v.grad.data.zero_()
# Weights will no longer view onto the same chunk of memory
weight = all_vars[4]
weight_data = weight.data.clone()
with torch.no_grad():
weight.set_(weight_data)
for _ in range(2):
with warnings.catch_warnings(record=True) as w:
output_noncontig = rnn(input, hx)
if first_warn:
self.assertEqual(len(w), 1)
self.assertIn('weights are not part of single contiguous chunk of memory', w[0].message.args[0])
first_warn = False
warnings.resetwarnings()
output_noncontig[0].sum().backward()
grads_noncontig = [v.grad.data.clone() for v in all_vars]
for v in all_vars:
v.grad.data.zero_()
self.assertEqual(output, output_noncontig)
self.assertEqual(grads_noncontig, grads)
# Make sure these still share storage
weight_data[:] = 4
self.assertEqual(weight_data, all_vars[4].data)
@unittest.skipIf(not TEST_CUDNN, 'CUDNN not available')
def test_cudnn_weight_tying(self):
rnns = [
nn.LSTM(10, 20, batch_first=True, bidirectional=True),
nn.LSTM(10, 20, batch_first=True, bidirectional=True, proj_size=10),
nn.GRU(10, 20, batch_first=True, bidirectional=True),
nn.RNN(10, 20, batch_first=True, bidirectional=True)
]
for rnn in rnns:
rnn.bias_ih_l0_reverse = rnn.bias_ih_l0
rnn.cuda()
input = torch.randn(5, 4, 10, requires_grad=True, device="cuda")
hx = torch.randn(2, 5, 20, requires_grad=True, device="cuda")
all_vars = [input, hx] + list(rnn.parameters())
opt = torch.optim.SGD(rnn.parameters(), lr=0.1)
opt.zero_grad()
if isinstance(rnn, nn.LSTM):
# LSTM with projections has different hx size
if rnn.proj_size > 0:
hx = torch.randn(2, 5, 10, requires_grad=True, device="cuda")
all_vars[1] = hx
cx = torch.randn(2, 5, 20, requires_grad=True, device="cuda")
all_vars[2:2] = [cx]
hx = (hx, cx)
with warnings.catch_warnings(record=True) as w:
output = rnn(input, hx)
output[0].sum().backward()
opt.step()
with warnings.catch_warnings(record=True) as w:
output_cuda = rnn(input, hx)
rnn.cpu()
hx = (hx[0].cpu(), hx[1].cpu()) if isinstance(rnn, nn.LSTM) else hx.cpu()
output_cpu = rnn(input.cpu(), hx)
self.assertEqual(output_cuda, output_cpu)
def test_transformer_args_check(self):
model_name = 'Transformer'
d_model = 128
nhead = 4
num_encoder_layers = 2
num_decoder_layers = 3
dim_feedforward = 65
dropout = 0.3
bsz = 3
seq_len = 35
tgt_len = 15
activations = [F.relu, F.gelu]
wrong_bsz = 7
wrong_d_model = 63
wrong_nhead = 5
wrong_activation = "abc"
def test(encoder_input_shape, decoder_input_shape,
src_mask_len=None, tgt_mask_len=None, memory_mask_size=None,
src_key_padding_mask_size=None, tgt_key_padding_mask_size=None,
memory_key_padding_mask_size=None,
src_is_causal=False, tgt_is_causal=False,
memory_is_causal=False):
encoder_input = torch.randn(encoder_input_shape)
decoder_input = torch.randn(decoder_input_shape)
model = getattr(nn, model_name)(d_model, nhead, num_encoder_layers,
num_decoder_layers, dim_feedforward, dropout)
if src_mask_len is not None:
src_mask = model.generate_square_subsequent_mask(src_mask_len)
else:
src_mask = None
if tgt_mask_len is not None:
tgt_mask = model.generate_square_subsequent_mask(tgt_mask_len)
else:
tgt_mask = None
if memory_mask_size is not None:
memory_task = torch.rand(memory_mask_size)
else:
memory_task = None
if src_key_padding_mask_size is not None:
src_key_padding_mask = torch.rand(src_key_padding_mask_size) >= 0.5
else:
src_key_padding_mask = None
if tgt_key_padding_mask_size is not None:
tgt_key_padding_mask = torch.rand(tgt_key_padding_mask_size) >= 0.5
else:
tgt_key_padding_mask = None
if memory_key_padding_mask_size is not None:
memory_key_padding_mask = torch.rand(memory_key_padding_mask_size) >= 0.5
else:
memory_key_padding_mask = None
with self.assertRaises(RuntimeError):
model(encoder_input, decoder_input,
src_mask=src_mask,
tgt_mask=tgt_mask,
memory_mask=memory_task,
src_key_padding_mask=src_key_padding_mask,
tgt_key_padding_mask=tgt_key_padding_mask,
memory_key_padding_mask=memory_key_padding_mask,
src_is_causal=src_is_causal,
tgt_is_causal=tgt_is_causal,
memory_is_causal=memory_is_causal)
correct_encoder_input_shape = (seq_len, bsz, d_model)
correct_decoder_input_shape = (tgt_len, bsz, d_model)
def update_shape(shape, dim, new_dim_size):
new_shape = list(shape)
new_shape[dim] = new_dim_size
return tuple(new_shape)
# Incorrect encoder_input batch size
encoder_input_shape = update_shape(correct_encoder_input_shape, 1, wrong_bsz)
decoder_input_shape = correct_decoder_input_shape
test(encoder_input_shape, decoder_input_shape)
# Incorrect decoder_input batch size
encoder_input_shape = correct_encoder_input_shape
decoder_input_shape = update_shape(correct_decoder_input_shape, 1, wrong_bsz)
test(encoder_input_shape, decoder_input_shape)
# Incorrect encoder_input input size
encoder_input_shape = update_shape(correct_encoder_input_shape, 2, wrong_d_model)
decoder_input_shape = correct_decoder_input_shape
test(encoder_input_shape, decoder_input_shape)
# Incorrect decoder_input input size
encoder_input_shape = correct_encoder_input_shape
decoder_input_shape = update_shape(correct_decoder_input_shape, 2, wrong_d_model)
test(encoder_input_shape, decoder_input_shape)
# Incorrect nhead
encoder_input_shape = correct_encoder_input_shape
decoder_input_shape = correct_decoder_input_shape
with self.assertRaises(AssertionError):
model = getattr(nn, model_name)(d_model, wrong_nhead, num_encoder_layers,
num_decoder_layers, dim_feedforward, dropout)
# Incorrect src_mask
encoder_input_shape = correct_encoder_input_shape
decoder_input_shape = correct_decoder_input_shape
wrong_src_mask_size = seq_len + 1
test(encoder_input_shape, decoder_input_shape, src_mask_len=wrong_src_mask_size)
# Incorrect tgt_mask
encoder_input_shape = correct_encoder_input_shape
decoder_input_shape = correct_decoder_input_shape
wrong_tgt_mask_size = tgt_len + 1
test(encoder_input_shape, decoder_input_shape, tgt_mask_len=wrong_tgt_mask_size)
# Incorrect memory_mask
encoder_input_shape = correct_encoder_input_shape
decoder_input_shape = correct_decoder_input_shape
wrong_tgt_mask_size = tgt_len + 1
test(encoder_input_shape, decoder_input_shape,
memory_mask_size=(wrong_tgt_mask_size, wrong_src_mask_size))
# Incorrect src_key_padding_mask
encoder_input_shape = correct_encoder_input_shape
decoder_input_shape = correct_decoder_input_shape
with self.assertRaises(AssertionError):
test(encoder_input_shape, decoder_input_shape,
src_key_padding_mask_size=(wrong_bsz, wrong_src_mask_size))
# Incorrect tgt_key_padding_mask
encoder_input_shape = correct_encoder_input_shape
decoder_input_shape = correct_decoder_input_shape
with self.assertRaises(AssertionError):
test(encoder_input_shape, decoder_input_shape,
tgt_key_padding_mask_size=(wrong_bsz, wrong_tgt_mask_size))
# Incorrect memory_key_padding_mask
encoder_input_shape = correct_encoder_input_shape
decoder_input_shape = correct_decoder_input_shape
with self.assertRaises(AssertionError):
test(encoder_input_shape, decoder_input_shape,
memory_key_padding_mask_size=(wrong_bsz, wrong_src_mask_size))
# Correct activations
for activation in activations:
model = getattr(nn, model_name)(d_model, nhead, num_encoder_layers, num_decoder_layers,
dim_feedforward, dropout, activation)
# Incorrect activation
with self.assertRaises(RuntimeError):
model = getattr(nn, model_name)(d_model, nhead, num_encoder_layers, num_decoder_layers,
dim_feedforward, dropout, wrong_activation)
def test_transformer_layer_args_check(self):
model_names = ['TransformerEncoderLayer', 'TransformerDecoderLayer']
d_model = 128
nhead = 4
dim_feedforward = 65
dropout = 0.3
bsz = 3
seq_len = 35
tgt_len = 15
activations = [F.relu, F.gelu]
wrong_activation = "abc"
encoder_input_shape = (seq_len, bsz, d_model)
decoder_input_shape = (tgt_len, bsz, d_model)
encoder_input = torch.randn(encoder_input_shape)
decoder_input = torch.randn(decoder_input_shape)
for model_name in model_names:
for activation in activations:
model = getattr(nn, model_name)(d_model, nhead, dim_feedforward,
dropout, activation)
# Incorrect activation
for model_name in model_names:
with self.assertRaises(RuntimeError):
model = getattr(nn, model_name)(d_model, nhead, dim_feedforward,
dropout, wrong_activation)
def test_rnn_args_check(self):
input_size = 3
hidden_size = 5
num_layers = 2
batch_size = 4
seq_len = 6
num_directions = 1
bad_size = 7 # prime number so that no size can divide it.
def test(input_shape, hidden_shape, mode):
for input, hidden in get_inputs(input_shape, hidden_shape, mode):
model = getattr(nn, mode)(input_size, hidden_size, num_layers)
self.assertRaises(RuntimeError, lambda: model(input, hidden))
correct_input_shape = (seq_len, batch_size, input_size)
correct_hidden_shape = (num_layers * num_directions, batch_size, hidden_size)
def update_shape(shape, dim, new_dim_size):
new_shape = list(shape)
new_shape[dim] = new_dim_size
return tuple(new_shape)
def get_inputs(input_shape, hidden_shape, mode):
'''returns list( tuple(input, hidden) )
where input, hidden are inputs to a model'''
input = torch.randn(input_shape)
hidden = torch.randn(hidden_shape)
if mode != 'LSTM':
return [(input, hidden)]
if hidden_shape == correct_hidden_shape:
return [(input, (hidden, hidden))]
good_hidden = torch.randn(correct_hidden_shape)
return [
(input, (hidden, good_hidden)),
(input, (good_hidden, hidden)),
]
rnn_modes = ['RNN', 'GRU', 'LSTM']
for mode in rnn_modes:
# Incorrect input batch size
input_shape = update_shape(correct_input_shape, 1, bad_size)
hidden_shape = correct_hidden_shape
test(input_shape, hidden_shape, mode)
# Incorrect hidden batch size
input_shape = correct_input_shape
hidden_shape = update_shape(correct_hidden_shape, 1, bad_size)
test(input_shape, hidden_shape, mode)
# Incorrect input size
input_shape = update_shape(correct_input_shape, 2, bad_size)
hidden_shape = correct_hidden_shape
test(input_shape, hidden_shape, mode)
# Incorrect hidden size
input_shape = correct_input_shape
hidden_shape = update_shape(correct_hidden_shape, 2, bad_size)
test(input_shape, hidden_shape, mode)
# Incorrect hidden[0]
input_shape = correct_input_shape
hidden_shape = update_shape(correct_hidden_shape, 0, bad_size)
test(input_shape, hidden_shape, mode)
def test_projections_lstm_args_check(self):
input_size = 3
hidden_size = 5
proj_size = 2
num_layers = 2
batch_size = 4
seq_len = 6
num_directions = 1
bad_size = 7 # prime number so that no size can divide it.
def test(input_shape, hidden_h_shape, hidden_c_shape):
for input, hidden in get_inputs(input_shape, hidden_h_shape, hidden_c_shape):
model = nn.LSTM(input_size, hidden_size, num_layers, proj_size=proj_size)
self.assertRaises(RuntimeError, lambda: model(input, hidden))
correct_input_shape = (seq_len, batch_size, input_size)
correct_hidden_h_shape = (num_layers * num_directions, batch_size, proj_size)
correct_hidden_c_shape = (num_layers * num_directions, batch_size, hidden_size)
def update_shape(shape, dim, new_dim_size):
new_shape = list(shape)
new_shape[dim] = new_dim_size
return tuple(new_shape)
def get_inputs(input_shape, hidden_h_shape, hidden_c_shape):
'''returns list( tuple(input, hidden) )
where input, hidden are inputs to a model'''
input = torch.randn(input_shape)
hidden_h = torch.randn(hidden_h_shape)
hidden_c = torch.randn(hidden_c_shape)
return [(input, (hidden_h, hidden_c))]
# Incorrect input batch size
input_shape = update_shape(correct_input_shape, 1, bad_size)
test(input_shape, correct_hidden_h_shape, correct_hidden_c_shape)
# Incorrect hidden batch size
input_shape = correct_input_shape
hidden_h_shape = update_shape(correct_hidden_h_shape, 1, bad_size)
hidden_c_shape = update_shape(correct_hidden_c_shape, 1, bad_size)
test(input_shape, hidden_h_shape, hidden_c_shape)
# Incorrect input size
input_shape = update_shape(correct_input_shape, 2, bad_size)
test(input_shape, correct_hidden_h_shape, correct_hidden_c_shape)
# Incorrect hidden size
input_shape = correct_input_shape
hidden_h_shape = update_shape(correct_hidden_h_shape, 2, bad_size)
hidden_c_shape = update_shape(correct_hidden_c_shape, 2, bad_size)
test(input_shape, hidden_h_shape, hidden_c_shape)
# Incorrect hidden[0]
input_shape = correct_input_shape
hidden_h_shape = update_shape(correct_hidden_h_shape, 0, bad_size)
hidden_c_shape = update_shape(correct_hidden_c_shape, 0, bad_size)
test(input_shape, hidden_h_shape, hidden_c_shape)
# Incorrect proj size = hidden size
input_shape = correct_input_shape
hidden_h_shape = update_shape(correct_hidden_h_shape, 0, hidden_size)
hidden_c_shape = correct_hidden_c_shape
test(input_shape, hidden_h_shape, hidden_c_shape)
# Incorrect proj size != hidden size
input_shape = correct_input_shape
hidden_h_shape = update_shape(correct_hidden_h_shape, 0, bad_size)
hidden_c_shape = correct_hidden_c_shape
test(input_shape, hidden_h_shape, hidden_c_shape)
# Incorrect cell size != hidden size
input_shape = correct_input_shape
hidden_h_shape = correct_hidden_h_shape
hidden_c_shape = update_shape(correct_hidden_c_shape, 0, bad_size)
test(input_shape, hidden_h_shape, hidden_c_shape)
@unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
def test_rnn_check_device(self):
import copy
input_size = 3
hidden_size = 5
num_layers = 2
batch_size = 4
seq_len = 6
num_directions = 1
correct_input_shape = (seq_len, batch_size, input_size)
correct_hidden_shape = (num_layers * num_directions, batch_size, hidden_size)
rnn_modes = ['RNN', 'GRU', 'LSTM']
for mode in rnn_modes:
model = getattr(nn, mode)(input_size, hidden_size, num_layers)
model_cuda = copy.deepcopy(model).to('cuda:0')
input = torch.randn(correct_input_shape)
hidden = torch.randn(correct_hidden_shape)
# input and weights are not at the same device
with self.assertRaisesRegex(RuntimeError,
"Input and parameter tensors are not at the same device"):
model(input.to('cuda:0'))
with self.assertRaisesRegex(RuntimeError,
"Input and parameter tensors are not at the same device"):
model_cuda(input)
# input and hiddens are not at the same device
with self.assertRaisesRegex(RuntimeError,
r"Input and hidden tensors are not at the same device"):
if mode == 'LSTM':
model(input, (hidden.to('cuda:0'), hidden.to('cuda:0')))
else:
model(input, (hidden.to('cuda:0')))
with self.assertRaisesRegex(RuntimeError,
r"Input and hidden tensors are not at the same device"):
if mode == 'LSTM':
model_cuda(input.to('cuda:0'), (hidden, hidden))
else:
model_cuda(input.to('cuda:0'), (hidden))
# hidden tensors are not at the same CUDA device
if mode == 'LSTM':
with self.assertRaisesRegex(RuntimeError,
"Input and hidden tensors are not at the same device"):
model(input.to('cuda:0'), (hidden.to('cuda:0'), hidden.to('cuda:1')))
@unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
def test_projections_lstm_check_device(self):
input_size = 3
hidden_size = 5
proj_size = 2
num_layers = 2
batch_size = 4
seq_len = 6
num_directions = 1
correct_input_shape = (seq_len, batch_size, input_size)
correct_hidden_h_shape = (num_layers * num_directions, batch_size, proj_size)
correct_hidden_c_shape = (num_layers * num_directions, batch_size, hidden_size)
model = nn.LSTM(input_size, hidden_size, num_layers, proj_size=proj_size)
input = torch.randn(correct_input_shape)
hidden_h = torch.randn(correct_hidden_h_shape)
hidden_c = torch.randn(correct_hidden_c_shape)
# input and weights are not at the same device
with self.assertRaisesRegex(RuntimeError,
"Input and parameter tensors are not at the same device"):
model(input.to('cuda:0'))
# input and hiddens are not at the same device
with self.assertRaisesRegex(RuntimeError,
r"Input and hidden tensors are not at the same device"):
model(input, (hidden_h.to('cuda:0'), hidden_c.to('cuda:0')))
# hidden tensors are not at the same CUDA device
with self.assertRaisesRegex(RuntimeError,
"Input and hidden tensors are not at the same device"):
model(input.to('cuda:0'), (hidden_h.to('cuda:0'), hidden_c.to('cuda:1')))
def test_rnn_initial_hidden_state(self):
rnn_modes = ['RNN', 'GRU', 'LSTM']
for mode in rnn_modes:
rnn = getattr(nn, mode)(30, 20, 2)
input = torch.randn(10, 32, 30)
hidden = torch.zeros(2, 32, 20)
if mode == 'LSTM':
hidden = (hidden, hidden)
output1, hidden1 = rnn(input, hidden)
output2, hidden2 = rnn(input)
self.assertEqual(output1, output2)
self.assertEqual(hidden1, hidden2)
def test_projections_lstm_initial_hidden_state(self):
for bidir in [False, True]:
rnn = nn.LSTM(30, 20, 2, bidirectional=bidir, proj_size=10)
num_dirs = 2 if bidir else 1
input = torch.randn(10, 32, 30)
hidden_h = torch.zeros(2 * num_dirs, 32, 10)
hidden_c = torch.zeros(2 * num_dirs, 32, 20)
hidden = (hidden_h, hidden_c)
output1, hidden1 = rnn(input, hidden)
output2, hidden2 = rnn(input)
self.assertEqual(output1, output2)
self.assertEqual(hidden1, hidden2)
def test_projections_errors_on_gru_and_rnn(self):
error_msg = "proj_size argument is only supported for LSTM, not RNN or GRU"
for mode in ['RNN', 'GRU']:
with self.assertRaisesRegex(ValueError, error_msg):
rnn = getattr(nn, mode)(30, 20, 2, proj_size=10)
def _test_RNN_cpu_vs_cudnn(self, dropout, dtype=torch.double):
def forward_backward(cuda, rnn, input_val, grad_output, weights_val, hx_val, grad_hy,
cx_val=None, grad_cy=None):
is_lstm = isinstance(rnn, nn.LSTM)
for x_layer, y_layer in zip(rnn.all_weights, weights_val):
for x, y in zip(x_layer, y_layer):
x.data.copy_(y.data)
if isinstance(input_val, rnn_utils.PackedSequence):
input = rnn_utils.PackedSequence(
input_val.data.data.requires_grad_(True), input_val.batch_sizes)
input_var = input.data
else:
input = input_val.clone().requires_grad_(True)
input_var = input
if is_lstm:
if cx_val is None:
hx = (hx_val.clone().requires_grad_(True),
hx_val.add(1).requires_grad_(True))
else:
hx = (hx_val.clone().requires_grad_(True),
cx_val.add(1).requires_grad_(True))
else:
hx = hx_val.clone().requires_grad_(True)
if cuda:
rnn.cuda()
input_var.data = input_var.data.cuda()
if is_lstm:
hx[0].data = hx[0].data.cuda()
hx[1].data = hx[1].data.cuda()
else:
hx.data = hx.data.cuda()
grad_hy = grad_hy.cuda()
if grad_cy is not None:
grad_cy = grad_cy.cuda()
grad_output = grad_output.cuda()
output, hy = rnn(input, hx)
if isinstance(output, rnn_utils.PackedSequence):
output = output.data
if is_lstm:
if grad_cy is None:
torch.autograd.backward([output, hy[0], hy[1]], [grad_output, grad_hy, grad_hy + 1])
else:
torch.autograd.backward([output, hy[0], hy[1]], [grad_output, grad_hy, grad_cy + 1])
else:
torch.autograd.backward([output, hy], [grad_output, grad_hy])
return {'output': output.data,
'hy': hy[0].data if is_lstm else hy.data,
'weights': rnn.all_weights,
'grad_input': input_var.grad.data,
'grad_hx': hx[0].grad.data if is_lstm else hx.grad.data,
'cy': hy[1].data if is_lstm else None,
'grad_cx': hx[1].grad.data if is_lstm else None}
input_size = 10
hidden_size = 6
proj_size = 3
num_layers = 2
seq_length = 7
batch = 6
def make_noncontig(tensor):
ndim = tensor.dim()
return torch.stack([tensor.clone().zero_(), tensor], ndim).select(ndim, 1)
def compare_cpu_gpu(outputs_cpu, outputs_gpu):
self.assertEqual(list(outputs_cpu.keys()), list(outputs_gpu.keys()))
for key in outputs_cpu.keys():
if key != 'weights':
self.assertEqual(outputs_cpu[key], outputs_gpu[key], atol=5e-5, rtol=0, msg=key)
# check grad weights separately, as nested dict
for cpu_layer_weight, gpu_layer_weight in zip(outputs_cpu['weights'], outputs_gpu['weights']):
for (cpu_weight, gpu_weight) in zip(cpu_layer_weight, gpu_layer_weight):
self.assertEqual(cpu_weight.grad.data, gpu_weight.grad.data, atol=5e-5, rtol=0)
for module in (nn.RNN, nn.LSTM, nn.GRU):
for bias, bidirectional, batch_first, contig, variable_len, lens_as_tensor \
in product((True, False), repeat=6):
num_directions = 2 if bidirectional else 1
if batch_first:
input_val = torch.randn(batch, seq_length, input_size, dtype=dtype)
grad_output = torch.randn(batch, seq_length, hidden_size * num_directions, dtype=dtype)
else:
input_val = torch.randn(seq_length, batch, input_size, dtype=dtype)
grad_output = torch.randn(seq_length, batch, hidden_size * num_directions, dtype=dtype)
hx_val = torch.randn(num_layers * num_directions, batch, hidden_size, dtype=dtype)
grad_hy = torch.randn(num_layers * num_directions, batch, hidden_size, dtype=dtype)
if not contig:
grad_output = make_noncontig(grad_output)
grad_hy = make_noncontig(grad_hy)
input_var = make_noncontig(input_val)
hx_val = make_noncontig(hx_val)
if variable_len:
lengths = [7, 5, 5, 2, 1, 1]
if lens_as_tensor:
lengths = torch.tensor(lengths, dtype=torch.long)
input_val = rnn_utils.pack_padded_sequence(input_val, lengths, batch_first=batch_first)
grad_output = rnn_utils.pack_padded_sequence(grad_output, lengths, batch_first=batch_first).data
rnn = module(input_size,
hidden_size,
num_layers,
bias=bias,
dropout=dropout,
bidirectional=bidirectional,
batch_first=batch_first).to(dtype)
outputs_cpu = forward_backward(
False, rnn, input_val, grad_output, rnn.all_weights, hx_val, grad_hy)
rnn_gpu = module(input_size,
hidden_size,
num_layers,
bias=bias,
dropout=dropout,
bidirectional=bidirectional,
batch_first=batch_first).to(dtype)
outputs_gpu = forward_backward(
True, rnn_gpu, input_val, grad_output, rnn.all_weights, hx_val, grad_hy)
compare_cpu_gpu(outputs_cpu, outputs_gpu)
for nonlinearity in ('tanh', 'relu'):
hx_val = torch.randn(num_layers, batch, hidden_size, dtype=dtype)
input_val = torch.randn(seq_length, batch, input_size, dtype=dtype)
grad_output = torch.randn(
seq_length, batch, hidden_size * num_directions, dtype=dtype)
grad_hy = torch.randn(
num_layers * num_directions, batch, hidden_size, dtype=dtype)
rnn = nn.RNN(input_size, hidden_size, num_layers, bias=bias, nonlinearity=nonlinearity).to(dtype)
outputs_cpu = forward_backward(False, rnn, input_val, grad_output, rnn.all_weights, hx_val, grad_hy)
rnn_gpu = nn.RNN(input_size, hidden_size, num_layers, bias=bias, nonlinearity=nonlinearity).to(dtype)
outputs_gpu = forward_backward(True, rnn_gpu, input_val, grad_output, rnn.all_weights, hx_val, grad_hy)
compare_cpu_gpu(outputs_cpu, outputs_gpu)
# checking LSTM with projections
for bias, bidirectional, batch_first, contig, variable_len, lens_as_tensor \
in product((True, False), repeat=6):
num_directions = 2 if bidirectional else 1
if batch_first:
input_val = torch.randn(batch, seq_length, input_size, dtype=dtype)
grad_output = torch.randn(batch, seq_length, proj_size * num_directions, dtype=dtype)
else:
input_val = torch.randn(seq_length, batch, input_size, dtype=dtype)
grad_output = torch.randn(seq_length, batch, proj_size * num_directions, dtype=dtype)
hx_val = torch.randn(num_layers * num_directions, batch, proj_size, dtype=dtype)
cx_val = torch.randn(num_layers * num_directions, batch, hidden_size, dtype=dtype)
grad_hy = torch.randn(num_layers * num_directions, batch, proj_size, dtype=dtype)
grad_cy = torch.randn(num_layers * num_directions, batch, hidden_size, dtype=dtype)
if not contig:
grad_output = make_noncontig(grad_output)
grad_hy = make_noncontig(grad_hy)
grad_cy = make_noncontig(grad_cy)
input_var = make_noncontig(input_val)
hx_val = make_noncontig(hx_val)
cx_val = make_noncontig(cx_val)
if variable_len:
lengths = [7, 5, 5, 2, 1, 1]
if lens_as_tensor:
lengths = torch.tensor(lengths, dtype=torch.long)
input_val = rnn_utils.pack_padded_sequence(input_val, lengths, batch_first=batch_first)
grad_output = rnn_utils.pack_padded_sequence(grad_output, lengths, batch_first=batch_first).data
rnn = nn.LSTM(input_size,
hidden_size,
num_layers,
bias=bias,
dropout=dropout,
bidirectional=bidirectional,
batch_first=batch_first,
proj_size=proj_size).to(dtype)
outputs_cpu = forward_backward(
False, rnn, input_val, grad_output, rnn.all_weights,
hx_val, grad_hy, cx_val, grad_cy)
rnn_gpu = nn.LSTM(input_size,
hidden_size,
num_layers,
bias=bias,
dropout=dropout,
bidirectional=bidirectional,
batch_first=batch_first,
proj_size=proj_size).to(dtype)
outputs_gpu = forward_backward(
True, rnn_gpu, input_val, grad_output, rnn.all_weights,
hx_val, grad_hy, cx_val, grad_cy)
compare_cpu_gpu(outputs_cpu, outputs_gpu)
@unittest.skipIf(not TEST_CUDNN, "needs cudnn")
def test_RNN_cpu_vs_cudnn_no_dropout(self):
dtype = torch.double
self._test_RNN_cpu_vs_cudnn(0, dtype)
@unittest.skipIf(not (TEST_CUDNN and (TEST_CUDNN_VERSION if TEST_CUDNN_VERSION else 0) >= 5103), "needs cudnn >= 5.1")
def test_RNN_cpu_vs_cudnn_with_dropout(self):
# Because of dropout randomness, can only compare dropout=0 and dropout=1
self._test_RNN_cpu_vs_cudnn(1)
@unittest.skipIf(not TEST_CUDNN, "needs cudnn")
def test_RNN_cudnn_weight_norm(self):
input_size = 10
hidden_size = 6
num_layers = 2
seq_length = 7
batch = 6
# runs on CPU to acquire expected output
def check_weight_norm(m, name):
input = torch.randn(seq_length, batch, input_size)
expected_output = m(input)
# adds weight normalization
m = torch.nn.utils.weight_norm(m, name=name)
# moves to CUDA
m = m.cuda()
input = input.cuda()
# otherwise, subsequent warnings will be hidden, and further tests rely on them
warnings.simplefilter("always")
self.assertEqual(m(input), expected_output)
# remove weight norm
m = torch.nn.utils.remove_weight_norm(m, name=name)
self.assertEqual(m(input), expected_output)
check_weight_norm(nn.LSTM(input_size, hidden_size, num_layers), 'weight_hh_l0')
check_weight_norm(nn.LSTM(input_size, hidden_size, num_layers, proj_size=3), 'weight_hr_l0')
@unittest.skipIf(not TEST_CUDA, 'CUDA not available')
def test_partial_flat_weights(self):
input_size = 10
hidden_size = 6
num_layers = 2
m = nn.LSTM(input_size, hidden_size, num_layers)
inp = torch.randn(3, 2, 10)
out_expected = m(inp)
# deletes an attribute of original LSTM
weight_orig = m.weight_hh_l0
del m.weight_hh_l0
self.assertFalse(hasattr(m, "weight_hh_l0"))
# verifies that moving to CUDA with only some attributes defined
# does not throw an error
m.cuda()
# recompute the weight and make sure that module can be used
m.weight_hh_l0 = weight_orig.cuda()
inp = inp.cuda()
# otherwise, subsequent warnings will be hidden, and further tests rely on them
warnings.simplefilter("always")
self.assertEqual(m(inp)[0].cpu(), out_expected[0])
@unittest.skipIf(not (TEST_CUDNN and (TEST_CUDNN_VERSION if TEST_CUDNN_VERSION else 0) >= 5103), "needs cudnn >= 5.1")
@set_default_dtype(torch.double)
def test_RNN_dropout(self):
# checking the assumption that cuDNN sticks dropout in between
# RNN layers
for p in (0, 0.276, 0.731, 1):
for train in (True, False):
for cuda in (True, False):
rnn = nn.RNN(10, 1000, 2, bias=False, dropout=p, nonlinearity='relu')
if cuda:
rnn.cuda()
if train:
rnn.train()
else:
rnn.eval()
rnn.weight_ih_l0.data.fill_(1)
rnn.weight_hh_l0.data.fill_(1)
rnn.weight_ih_l1.data.fill_(1)
rnn.weight_hh_l1.data.fill_(1)
input = torch.ones(1, 1, 10)
hx = torch.zeros(2, 1, 1000)
if cuda:
input = input.cuda()
hx = hx.cuda()
output, hy = rnn(input, hx)
self.assertEqual(output.data.min(), output.data.max())
output_val = output.data[0][0][0]
if p == 0 or not train:
self.assertEqual(output_val, 10000)
elif p == 1:
self.assertEqual(output_val, 0)
else:
self.assertGreater(output_val, 8000)
self.assertLess(output_val, 12000)
denorm_mod = (output_val * (1 - p)) % 10
self.assertLess(min(denorm_mod, 10 - denorm_mod), 1e-2)
self.assertEqual(hy[0].data.min(), hy[0].data.max())
self.assertEqual(hy[1].data.min(), hy[1].data.max())
self.assertEqual(hy.data[0][0][0], 10)
self.assertEqual(hy.data[1][0][0], output_val)
@set_default_dtype(torch.double)
def test_error_RNN_seq_len_zero(self):
# checking error message when RNN has seq_len = 0
for module in (nn.RNN, nn.LSTM, nn.GRU):
for bidirectional in [True, False]:
for device in get_all_device_types():
input = torch.ones(0, 10, 5)
rnn = module(5, 6, bidirectional=bidirectional)
if device == 'cuda':
rnn.cuda()
input = input.cuda()
with self.assertRaisesRegex(RuntimeError, "Expected sequence length to be larger than 0 in RNN"):
rnn(input)
def test_RNN_input_size_zero(self):
for module in (nn.RNN, nn.LSTM, nn.GRU):
for device in get_all_device_types():
input = torch.zeros((5, 0, 3))
rnn = module(input_size=3, hidden_size=4)
if device == 'cuda':
rnn.cuda()
input = input.cuda()
outs = rnn(input)
self.assertEqual(outs[0].shape, torch.Size([5, 0, 4]))
# Check that backward does not cause a hard error
outs[0].sum().backward()
@unittest.skipIf(not (TEST_CUDNN and (TEST_CUDNN_VERSION if TEST_CUDNN_VERSION else 0) >= 5103), "needs cudnn >= 5.1")
def test_RNN_dropout_state(self):
for p in (0, 0.1234):
for train in (True, False):
for cuda in (True, False):
rnn = nn.RNN(100, 100, 2, bias=False, dropout=p, nonlinearity='relu')
if cuda:
rnn.cuda()
if train:
rnn.train()
else:
rnn.eval()
input = torch.rand(1, 1, 100)
hx = torch.rand(2, 1, 100)
if cuda:
input = input.cuda()
hx = hx.cuda()
output1, hy1 = rnn(input, hx)
output2, hy2 = rnn(input, hx)
buf = io.BytesIO()
rnn_pickle = torch.save(rnn, buf)
buf.seek(0)
rnn2 = torch.load(buf)
rnn2.flatten_parameters()
output3, hy3 = rnn2(input, hx)
if p == 0 or not train:
self.assertEqual(output1, output2)
self.assertEqual(output1, output3)
self.assertEqual(hy1, hy2)
self.assertEqual(hy1, hy3)
else:
self.assertNotEqual(output1, output2)
self.assertNotEqual(output1, output3)
self.assertNotEqual(hy1, hy2)
self.assertNotEqual(hy1, hy3)
@unittest.skipIf(not (TEST_CUDNN and (TEST_CUDNN_VERSION if TEST_CUDNN_VERSION else 0) >= 5103), "needs cudnn >= 5.1")
@set_default_dtype(torch.double)
def test_RNN_change_dropout(self):
for train, cuda in product((True, False), repeat=2):
rnn = nn.RNN(100, 100, 2, dropout=0, nonlinearity='relu')
input = torch.rand(3, 2, 100)
if cuda:
input.data = input.data.cuda()
rnn.cuda()
if train:
rnn.train()
else:
rnn.eval()
prev_output = None
for p in (0, 0.5, 0, 0.7, 0.2, 1, 0.2, 0):
rnn.dropout = p
output1, hy1 = rnn(input)
output2, hy2 = rnn(input)
if p == 0 or p == 1 or not train:
self.assertEqual(output1, output2)
self.assertEqual(hy1, hy2)
else:
self.assertNotEqual(output1, output2)
self.assertNotEqual(hy1, hy2)
if prev_output is not None:
if not train:
self.assertEqual(output1.data, prev_output)
self.assertEqual(output2.data, prev_output)
else:
self.assertNotEqual(output1.data, prev_output)
self.assertNotEqual(output2.data, prev_output)
prev_output = output1.data
def test_inplace_thnn(self):
modules = [nn.ReLU, nn.ELU, nn.SELU, nn.CELU, nn.RReLU]
for mod in modules:
r = mod(inplace=True)
input = torch.randn(5, 5, requires_grad=True)
output = r(input + 0)
grad_output = torch.randn(5, 5)
grad_output_clone = grad_output.clone()
output.backward(grad_output)
self.assertEqual(grad_output, grad_output_clone)
def test_pixel_shuffle_unshuffle(self):
def _test_pixel_shuffle_unshuffle_helper(num_input_dims, valid_channels_dim=True,
upscale_factor=None):
# Function to imperatively ensure pixels are shuffled to the correct locations.
# Used to validate the batch operations in pixel_shuffle.
def _verify_pixel_shuffle(input, output, upscale_factor):
for c in range(output.size(-3)):
for h in range(output.size(-2)):
for w in range(output.size(-1)):
height_idx = h // upscale_factor
weight_idx = w // upscale_factor
channel_idx = (upscale_factor * (h % upscale_factor)) + (w % upscale_factor) + \
(c * upscale_factor ** 2)
self.assertEqual(output[..., c, h, w], input[..., channel_idx, height_idx, weight_idx])
upscale_factor = random.randint(2, 5) if upscale_factor is None else upscale_factor
# If valid_channels_dim=False, add 1 to make channels dim indivisible by upscale_factor ** 2.
channels = random.randint(1, 4) * upscale_factor ** 2 + (0 if valid_channels_dim else 1)
height = random.randint(5, 10)
width = random.randint(5, 10)
if num_input_dims == 1:
input = torch.rand(channels, requires_grad=True)
elif num_input_dims == 2:
input = torch.rand(height, width, requires_grad=True)
else:
batch_sizes = [random.randint(1, 3) for _ in range(num_input_dims - 3)]
input = torch.rand(*batch_sizes, channels, height, width, requires_grad=True)
ps = nn.PixelShuffle(upscale_factor)
pus = nn.PixelUnshuffle(downscale_factor=upscale_factor)
if num_input_dims >= 3 and valid_channels_dim and upscale_factor > 0:
output = ps(input)
_verify_pixel_shuffle(input, output, upscale_factor)
output.backward(output.data)
self.assertEqual(input.data, input.grad.data)
# Ensure unshuffle properly inverts shuffle.
unshuffle_output = pus(output)
self.assertEqual(input, unshuffle_output)
else:
self.assertRaises(RuntimeError, lambda: ps(input))
def _test_pixel_unshuffle_error_case_helper(num_input_dims, valid_height_dim=True, valid_width_dim=True,
downscale_factor=None):
downscale_factor = random.randint(2, 5) if downscale_factor is None else downscale_factor
channels = random.randint(1, 4)
# If valid_height_dim=False, add 1 to make height dim indivisible by downscale_factor.
height = random.randint(3, 5) * abs(downscale_factor) + (0 if valid_height_dim else 1)
# If valid_width_dim=False, add 1 to make width dim indivisible by downscale_factor.
width = random.randint(3, 5) * abs(downscale_factor) + (0 if valid_width_dim else 1)
if num_input_dims == 1:
input = torch.rand(channels, requires_grad=True)
elif num_input_dims == 2:
input = torch.rand(height, width, requires_grad=True)
else:
batch_sizes = [random.randint(1, 3) for _ in range(num_input_dims - 3)]
input = torch.rand(*batch_sizes, channels, height, width, requires_grad=True)
pus = nn.PixelUnshuffle(downscale_factor)
self.assertRaises(RuntimeError, lambda: pus(input))
def _test_pixel_shuffle_unshuffle_for_input_dims(num_input_dims):
# For 1D - 2D, this is an error case.
# For 3D - 5D, this is a success case for pixel_shuffle + pixel_unshuffle.
_test_pixel_shuffle_unshuffle_helper(num_input_dims=num_input_dims)
# Error cases for pixel_shuffle.
_test_pixel_shuffle_unshuffle_helper(num_input_dims=num_input_dims, valid_channels_dim=False)
_test_pixel_shuffle_unshuffle_helper(num_input_dims=num_input_dims, upscale_factor=0)
_test_pixel_shuffle_unshuffle_helper(num_input_dims=num_input_dims, upscale_factor=-2)
# Error cases for pixel_unshuffle.
_test_pixel_unshuffle_error_case_helper(num_input_dims=num_input_dims, valid_height_dim=False)
_test_pixel_unshuffle_error_case_helper(num_input_dims=num_input_dims, valid_width_dim=False)
_test_pixel_unshuffle_error_case_helper(num_input_dims=num_input_dims, downscale_factor=0)
_test_pixel_unshuffle_error_case_helper(num_input_dims=num_input_dims, downscale_factor=-2)
def test_pixel_shuffle_unshuffle_1D():
_test_pixel_shuffle_unshuffle_for_input_dims(num_input_dims=1)
def test_pixel_shuffle_unshuffle_2D():
_test_pixel_shuffle_unshuffle_for_input_dims(num_input_dims=2)
def test_pixel_shuffle_unshuffle_3D():
_test_pixel_shuffle_unshuffle_for_input_dims(num_input_dims=3)
def test_pixel_shuffle_unshuffle_4D():
_test_pixel_shuffle_unshuffle_for_input_dims(num_input_dims=4)
def test_pixel_shuffle_unshuffle_5D():
_test_pixel_shuffle_unshuffle_for_input_dims(num_input_dims=5)
test_pixel_shuffle_unshuffle_1D()
test_pixel_shuffle_unshuffle_2D()
test_pixel_shuffle_unshuffle_3D()
test_pixel_shuffle_unshuffle_4D()
test_pixel_shuffle_unshuffle_5D()
@set_default_dtype(torch.double)
def test_pixel_shuffle_nhwc_cpu(self):
input = torch.randn(3, 18, 4, 4, device='cpu')
input = input.contiguous(memory_format=torch.channels_last).requires_grad_()
grad = torch.randn(3, 18, 4, 4, device='cpu')
ps = torch.nn.PixelShuffle(3)
pus = torch.nn.PixelUnshuffle(3)
ref_input = input.detach().clone().contiguous().requires_grad_(True)
ref_grad = grad.detach().clone().contiguous()
ref_ps = torch.nn.PixelShuffle(3)
ref_pus = torch.nn.PixelUnshuffle(3)
out = pus(ps(input))
out.backward(grad)
ref_out = ref_pus(ref_ps(ref_input))
ref_out.backward(ref_grad)
self.assertTrue(out.is_contiguous(memory_format=torch.channels_last))
self.assertTrue(ref_out.is_contiguous())
self.assertEqual(out, ref_out)
self.assertEqual(input.grad, ref_input.grad)
# These tests should be OpInfo'd
def test_elu_inplace_on_view(self):
v = torch.tensor([1.0, -1.0, 1.0, -1.0], requires_grad=True, dtype=torch.double)
def func(root):
x = root.clone()
view = x.narrow(0, 1, 2)
res = F.elu(view, inplace=True)
self.assertIs(res, view)
return x
gradcheck(func, [v])
gradgradcheck(func, [v])
def test_elu_inplace_gradgrad(self):
v = torch.randn(8, requires_grad=True, dtype=torch.double)
def func(root):
x = root.clone()
return F.elu(x, inplace=True)
gradcheck(func, [v])
gradgradcheck(func, [v])
def test_relu_inplace_on_view(self):
v = torch.tensor([1.0, -1.0, 1.0, -1.0], requires_grad=True, dtype=torch.double)
def func(root):
x = root.clone()
view = x.narrow(0, 1, 2)
res = F.relu(view, inplace=True)
self.assertIs(res, view)
return x
gradcheck(func, [v])
gradgradcheck(func, [v])
def test_PReLU_backward_requires_grad_false(self):
devices = ['cpu']
devices += ['cuda'] if TEST_CUDA else []
for d in devices:
m = nn.PReLU().to(d)
x = torch.randn(2, 3, 4, 5, device=d, requires_grad=False)
y = m(x)
y.mean().backward()
self.assertEqual(x.grad, None)
def test_bce_loss_always_nonnegative(self):
target = torch.ones(5)
input = torch.ones(5)
self.assertEqual((nn.BCELoss()(input, target) < 0).sum(), 0)
target = torch.zeros(5)
input = torch.zeros(5)
self.assertEqual((nn.BCELoss()(input, target) < 0).sum(), 0)
def test_bce_with_logits_raises_if_target_and_input_are_different_size(self):
target = torch.rand(5)
input = torch.rand(5, 1)
with self.assertRaises(ValueError):
nn.BCEWithLogitsLoss()(input, target)
target = torch.rand(5, 1)
input = torch.rand(5)
with self.assertRaises(ValueError):
nn.BCEWithLogitsLoss()(input, target)
def test_bce_with_logits_gives_same_result_as_sigmoid_and_bce_loss(self):
sigmoid = nn.Sigmoid()
target = torch.rand(64, 4)
output = torch.rand(64, 4) - 0.5
self.assertEqual(nn.BCEWithLogitsLoss()(output, target), nn.BCELoss()(sigmoid(output), target))
weight = torch.rand(4)
self.assertEqual(nn.BCEWithLogitsLoss(weight)(output, target), nn.BCELoss(weight)(sigmoid(output), target))
target = torch.zeros(4, 1, dtype=torch.float)
output = torch.empty(4, 1, dtype=torch.float).fill_(-100)
self.assertEqual(nn.BCEWithLogitsLoss()(output, target), nn.BCELoss()(sigmoid(output), target))
self.assertEqual(nn.BCEWithLogitsLoss(reduction='none')(output, target),
nn.BCELoss(reduction='none')(sigmoid(output), target))
weight = torch.rand(1, dtype=torch.float)
self.assertEqual(nn.BCEWithLogitsLoss(weight)(output, target), nn.BCELoss(weight)(sigmoid(output), target))
def test_bce_loss_input_range(self):
bceloss = nn.BCELoss()
target = torch.rand(25, 25)
output_valid = torch.rand(25, 25)
output_too_negative = output_valid - 1.0
output_too_positive = output_valid + 1.0
loss_valid = bceloss(output_valid, target)
with self.assertRaisesRegex(RuntimeError, 'between 0 and 1'):
loss_too_negative = bceloss(output_too_negative, target)
with self.assertRaisesRegex(RuntimeError, 'between 0 and 1'):
loss_too_positive = bceloss(output_too_positive, target)
def test_bce_loss_size_mismatch(self):
bceloss = nn.BCELoss()
a = torch.rand(25)
b = torch.rand(25, 1)
with self.assertRaisesRegex(ValueError, r'Using a target size \('):
bceloss(a, b)
def test_bce_with_logits_gives_same_result_as_sigmoid_and_bce_loss_large_tensors_with_grad(self):
x_size = 1024
y_size = 256
target = torch.rand(x_size, y_size)
for reduction in ['none', 'mean', 'sum']:
output_sig = torch.rand(x_size, y_size) - 0.5
output_logits = output_sig.clone().detach()
output_sig.requires_grad = True
output_logits.requires_grad = True
weight = torch.rand(y_size)
loss_sig = nn.BCELoss(weight, reduction=reduction)(
torch.sigmoid(output_sig), target
)
loss_logits = nn.BCEWithLogitsLoss(weight, reduction=reduction)(
output_logits, target
)
self.assertEqual(loss_logits, loss_sig)
if reduction == 'none':
grad = torch.rand(x_size, y_size)
loss_sig.backward(grad)
loss_logits.backward(grad)
else:
loss_sig.backward()
loss_logits.backward()
self.assertEqual(output_sig.grad, output_logits.grad)
def test_bce_with_logits_has_correct_forward_grad(self):
output = torch.randn(3, 5, requires_grad=True, dtype=torch.double)
target = torch.randn(3, 5, dtype=torch.double)
for reduction in ('sum', 'mean', 'none'):
gradcheck(lambda self, target: nn.BCEWithLogitsLoss(reduction=reduction)(self, target),
(output, target), check_forward_ad=True)
def test_bce_with_logits_has_correct_grad_at_zero(self):
output = torch.zeros(3, 1, requires_grad=True)
target = torch.zeros(3, 1)
nn.BCEWithLogitsLoss(reduction='sum')(output, target).backward()
expected_grad = torch.empty(3, 1).fill_(0.5)
self.assertEqual(output.grad, expected_grad)
def test_bce_with_logits_broadcasts_weights(self):
target = torch.rand(16, 4)
output = torch.rand(16, 4) - 0.5
weight = torch.rand(4)
out1 = nn.BCEWithLogitsLoss(weight)(output, target)
weight = weight.expand(16, 4).contiguous()
out2 = nn.BCEWithLogitsLoss(weight)(output, target)
self.assertEqual(out1, out2)
weight = torch.rand(16, 1)
out1 = nn.BCEWithLogitsLoss(weight)(output, target)
weight = weight.expand(16, 4).contiguous()
out2 = nn.BCEWithLogitsLoss(weight)(output, target)
self.assertEqual(out1, out2)
def test_bce_with_logits_ones_in_pos_weights_are_the_same_as_none(self):
target = torch.rand(64, 4)
output = torch.rand(64, 4) - 0.5
pos_weight = torch.ones(64, 4)
self.assertEqual(nn.BCEWithLogitsLoss()(output, target),
nn.BCEWithLogitsLoss(pos_weight=pos_weight)(output, target))
def test_bce_with_logits_broadcasts_pos_weights(self):
target = torch.rand(64, 4)
output = torch.rand(64, 4) - 0.5
pos_weight = torch.rand(4)
out1 = nn.BCEWithLogitsLoss(pos_weight=pos_weight)(output, target)
pos_weight1 = pos_weight.expand(1, 4)
out2 = nn.BCEWithLogitsLoss(pos_weight=pos_weight1)(output, target)
pos_weight2 = pos_weight.expand(64, 4)
out3 = nn.BCEWithLogitsLoss(pos_weight=pos_weight2)(output, target)
self.assertEqual(out1, out2)
self.assertEqual(out1, out3)
def test_bce_with_logits_with_pos_weight_has_correct_grad_at_zero(self):
output = torch.zeros(3, 1, requires_grad=True)
target = torch.zeros(3, 1)
pos_weight = torch.ones(3, 1)
nn.BCEWithLogitsLoss(pos_weight=pos_weight, reduction='sum')(output, target).backward()
expected_grad = torch.empty(3, 1).fill_(0.5)
grad = output.grad
self.assertEqual(grad, expected_grad)
def test_bce_with_logits_stability(self):
output = torch.tensor([0., -120.])
target = torch.tensor([0., 1.])
pos_weight = torch.tensor([1., 1.])
out1 = nn.BCEWithLogitsLoss()(output, target)
self.assertTrue(torch.isfinite(out1).all().item())
out2 = nn.BCEWithLogitsLoss(pos_weight=pos_weight)(output, target)
self.assertTrue(torch.isfinite(out2).all().item())
def test_bce_loss_broadcasts_weights(self):
sigmoid = nn.Sigmoid()
target = torch.rand(16, 4)
output = torch.rand(16, 4) - 0.5
weight = torch.rand(4)
out1 = nn.BCELoss(weight)(sigmoid(output), target)
weight = weight.expand(16, 4).contiguous()
out2 = nn.BCELoss(weight)(sigmoid(output), target)
self.assertEqual(out1, out2)
weight = torch.rand(16, 1)
out1 = nn.BCELoss(weight)(sigmoid(output), target)
weight = weight.expand(16, 4).contiguous()
out2 = nn.BCELoss(weight)(sigmoid(output), target)
self.assertEqual(out1, out2)
def test_hardtanh_inplace_gradgrad(self):
v = torch.randn(8, requires_grad=True, dtype=torch.double)
def func(root):
x = root.clone()
return F.hardtanh(x, inplace=True)
gradcheck(func, [v])
gradgradcheck(func, [v])
# test hardtanh backward froo large tensor
def test_hardtanh_backward(self):
x = torch.randn(128, 10000, requires_grad=True)
grad = torch.randn(128, 10000)
z = torch.zeros(128, 10000)
y = F.hardtanh(x)
y.backward(grad)
# ref backward path for hardtanh
mask = (x > -1) & (x < 1)
x_grad_ref = torch.where(mask, grad, z)
self.assertEqual(x.grad, x_grad_ref)
def test_batchnorm_nhwc_cpu(self):
def helper(self, mod, size, dtype, mixed_dtype=False, format=torch.channels_last):
channels = size[1]
input = torch.randn(size, dtype=dtype, device='cpu', requires_grad=True)
input = input.contiguous(memory_format=format).to(dtype)
input.retain_grad()
grad = torch.randn(size, dtype=dtype, device='cpu')
grad = grad.contiguous(memory_format=format)
bn = mod(channels).cpu().to(dtype)
bn.weight.data.uniform_()
bn.bias.data.uniform_()
ref_input = input.detach().clone().contiguous().requires_grad_(True)
ref_grad = grad.detach().clone().contiguous()
ref_bn = mod(channels).cpu().to(dtype)
ref_bn.load_state_dict(bn.state_dict())
if mixed_dtype:
bn.float()
ref_bn.float()
out = bn(input)
out.backward(grad)
ref_out = ref_bn(ref_input)
ref_out.backward(ref_grad)
self.assertTrue(out.is_contiguous(memory_format=format))
self.assertTrue(ref_out.is_contiguous())
self.assertEqual(out, ref_out)
self.assertEqual(bn.weight.grad, ref_bn.weight.grad)
self.assertEqual(bn.bias.grad, ref_bn.bias.grad)
self.assertEqual(input.grad, ref_input.grad)
# test NC11 and N1HW; test mixed dtype
for shape in [(4, 8, 10, 10), (4, 1, 9, 9), (4, 9, 1, 1)]:
helper(self, nn.BatchNorm2d, shape, torch.float, False, torch.channels_last)
helper(self, nn.BatchNorm2d, shape, torch.bfloat16, False, torch.channels_last)
helper(self, nn.BatchNorm2d, shape, torch.bfloat16, True, torch.channels_last)
for shape in [(4, 8, 2, 10, 10), (4, 1, 2, 9, 9), (4, 9, 1, 1, 1)]:
helper(self, nn.BatchNorm3d, shape, torch.float, False, torch.channels_last_3d)
helper(self, nn.BatchNorm3d, shape, torch.bfloat16, False, torch.channels_last_3d)
helper(self, nn.BatchNorm3d, shape, torch.bfloat16, True, torch.channels_last_3d)
@parametrize_test(
'bn_module',
[
subtest(torch.nn.BatchNorm2d, name="BatchNorm2d"),
subtest(torch.nn.SyncBatchNorm, name="SyncBatchNorm"),
],
)
def test_batchnorm_non_contig_cpu(self, bn_module):
input = torch.arange(6, dtype=torch.float).reshape(1, 3, 2, 1).cpu()
input = input.permute(0, 2, 1, 3)
bn = bn_module(2).cpu().float().eval()
bn.weight.data.uniform_()
bn.bias.data.uniform_()
ref_input = input.detach().clone().contiguous()
ref_bn = nn.BatchNorm2d(2).cpu().float().eval()
ref_bn.load_state_dict(bn.state_dict())
out = bn(input)
ref_out = ref_bn(ref_input)
self.assertTrue(out.is_contiguous(memory_format=torch.channels_last))
self.assertTrue(ref_out.is_contiguous())
self.assertEqual(out, ref_out)
input_bf = torch.arange(24, dtype=torch.bfloat16).reshape(1, 3, 2, 4)
input_bf = input_bf.permute(0, 2, 1, 3)
input_f = input_bf.float()
bn_mix = bn_module(2).float().eval()
ref_bn_f = deepcopy(bn_mix)
out_bf = bn_mix(input_bf)
ref_out_bf = ref_bn_f(input_f)
self.assertEqual(ref_out_bf, out_bf.float(), atol=0.05, rtol=0.05)
@unittest.skipIf(not TEST_CUDA, "CUDA unavailable")
@unittest.skipIf(not TEST_CUDNN, "needs cudnn")
def test_batchnorm_cudnn_nhwc(self):
def run_test(input, grad_output):
c = input.size(1)
mod = nn.BatchNorm2d(c).cuda().float()
mod.weight.data.uniform_()
mod.bias.data.uniform_()
ref_input = input.detach().clone().contiguous().requires_grad_(True)
ref_grad = grad.detach().clone().contiguous()
ref_mod = nn.BatchNorm2d(c).cuda().float()
ref_mod.load_state_dict(mod.state_dict())
out = mod(input)
out.backward(grad_output)
ref_out = ref_mod(ref_input)
ref_out.backward(ref_grad)
self.assertTrue(out.is_contiguous(memory_format=torch.channels_last))
self.assertTrue(ref_out.is_contiguous())
self.assertEqual(out, ref_out)
self.assertEqual(mod.weight.grad, ref_mod.weight.grad)
self.assertEqual(mod.bias.grad, ref_mod.bias.grad)
self.assertEqual(input.grad, ref_input.grad)
input = torch.randint(1, 10, (4, 8, 2, 2), dtype=torch.float32, device="cuda")
input = input.contiguous(memory_format=torch.channels_last).detach().requires_grad_()
grad = torch.randint(1, 10, (4, 8, 2, 2), dtype=torch.float32, device="cuda")
grad = grad.contiguous(memory_format=torch.channels_last)
run_test(input, grad)
# see #42588, grad is channels_last contiguous, but grad.suggest_memory_format (rightly) return "contiguous"
# not channels_last
input = torch.randint(1, 10, (2, 8, 8, 1), dtype=torch.float32, device="cuda")
input = input.contiguous(memory_format=torch.channels_last).detach().requires_grad_()
grad = torch.randint(1, 10, (2, 8, 8, 1), dtype=torch.float32, device="cuda")
grad = grad.permute(0, 2, 1, 3)
run_test(input, grad)
@unittest.skipIf(not TEST_CUDA, "CUDA unavailable")
def test_batchnorm_cudnn_half(self):
# THNN
input = torch.randint(1, 10, (2, 3, 2, 2), dtype=torch.half, device="cuda", requires_grad=True)
m = nn.BatchNorm2d(3).half().cuda()
thnn_output = m(input)
thnn_output.sum().backward()
thnn_input_grad = input.grad.data.clone()
self.assertEqualTypeString(thnn_output, input)
# cuDNN
if TEST_CUDNN:
input.grad = None
m = m.float()
cudnn_output = m(input)
cudnn_output.sum().backward()
cudnn_input_grad = input.grad.data.clone()
self.assertEqualTypeString(cudnn_output, input)
self.assertEqual(cudnn_output, thnn_output)
self.assertEqual(cudnn_input_grad, thnn_input_grad, atol=1e-3, rtol=0)
@unittest.skipIf(not TEST_CUDA, "CUDA unavailable")
def test_batchnorm_nonaffine_cuda_half_input(self):
input = torch.randn(16, 3, 24, 24, dtype=torch.half, device="cuda")
m = nn.BatchNorm2d(3, affine=False).cuda().float() # keep running stats in FP32
output = m(input)
self.assertEqualTypeString(output, input)
m.eval()
output = m(input)
self.assertEqualTypeString(output, input)
def test_batchnorm_raises_error_if_less_than_one_value_per_channel(self):
x = torch.rand(10)[None, :, None]
with self.assertRaises(ValueError):
torch.nn.BatchNorm1d(10)(x)
def test_batchnorm_raises_error_if_running_mean_is_not_same_size_as_input(self):
input = torch.rand(2, 10)
running_var = torch.rand(10)
wrong_sizes = [9, 11]
for size in wrong_sizes:
with self.assertRaises(RuntimeError):
F.batch_norm(input, torch.rand(size), running_var)
def test_batchnorm_raises_error_if_running_var_is_not_same_size_as_input(self):
input = torch.rand(2, 10)
running_mean = torch.rand(10)
wrong_sizes = [9, 11]
for size in wrong_sizes:
with self.assertRaises(RuntimeError):
F.batch_norm(input, running_mean, torch.rand(size))
def test_batchnorm_raises_error_if_weight_is_not_same_size_as_input(self):
input = torch.rand(2, 10)
running_mean = torch.rand(10)
running_var = torch.rand(10)
wrong_sizes = [9, 11]
for size in wrong_sizes:
with self.assertRaises(RuntimeError):
F.batch_norm(input, running_mean, running_var, weight=Parameter(torch.rand(size)))
def test_batchnorm_raises_error_if_bias_is_not_same_size_as_input(self):
input = torch.rand(2, 10)
running_mean = torch.rand(10)
running_var = torch.rand(10)
wrong_sizes = [9, 11]
for size in wrong_sizes:
with self.assertRaises(RuntimeError):
F.batch_norm(input, running_mean, running_var, bias=Parameter(torch.rand(size)))
def test_batchnorm_raises_error_if_running_var_or_running_mean_have_forward_grad(self):
args = (
torch.randn(3, 2, 5), # input
torch.randn(2), # running_mean
torch.randn(2), # running_var
)
kwargs = {'training': False, 'momentum': -1.2}
fn = partial(F.batch_norm, **kwargs)
for dual_indices in ((0,), (1,), (1, 2), (0, 1), (0, 1, 2),):
tangents = tuple(torch.rand_like(x) for x in args)
with fwAD.dual_level():
duals = [fwAD.make_dual(primal, tangent) if i in dual_indices else primal
for i, (primal, tangent) in enumerate(zip(args, tangents))]
msg = "batch_norm is not differentiable wrt running_mean and running_var"
# 0 needs to have forward grad because otherwise we won't even run batch_norm_jvp
if (1 in dual_indices or 2 in dual_indices) and 0 in dual_indices:
with self.assertRaisesRegex(RuntimeError, msg):
fn(*duals)
else:
fn(*duals)
def test_batchnorm_buffer_update_when_stats_are_not_tracked(self):
input_size = (32, 4)
# Instantiate BN with buffers that are not None
bn = nn.BatchNorm1d(input_size[1], track_running_stats=True)
# Use buffers for normalization but don't update them
bn.track_running_stats = False
# Store initial values
num_batches = bn.num_batches_tracked.clone()
running_mean = bn.running_mean.clone()
running_var = bn.running_var.clone()
# Forward random tensor
_ = bn(torch.rand(input_size))
# Ensure none of the buffers has been updated
self.assertTrue(torch.equal(num_batches, bn.num_batches_tracked))
self.assertTrue(torch.equal(running_mean, bn.running_mean))
self.assertTrue(torch.equal(running_var, bn.running_var))
@unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
def test_batchnorm_nhwc_cuda(self):
for dtype in (torch.half, torch.float):
(N, C, H, W) = 2, 64, 50, 50
model = torch.nn.BatchNorm2d(C, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
model = model.eval().cuda().to(dtype)
inp1 = torch.randn(N, C, H, W, device=torch.device('cuda'), dtype=dtype)
inp2 = inp1.contiguous(memory_format=torch.channels_last)
out1 = model(inp1)
out2 = model(inp2)
self.assertTrue(torch.equal(out1, out2))
def test_pairwise_distance(self):
input1 = torch.randn(4, 4, requires_grad=True, dtype=torch.double)
input2 = torch.randn(4, 4, requires_grad=True, dtype=torch.double)
self.assertTrue(gradcheck(lambda x, y: F.pairwise_distance(x, y), (input1, input2)))
# TODO: Create an OpInfo for pdist
def test_pdist(self):
for device, trans in itertools.product(device_(), [False, True]):
inp = torch.randn(4, 5, dtype=torch.double, device=device, requires_grad=True)
if trans:
inp = inp.transpose(0, 1)
for p in [0, 1, 2, 0.5, 1.5, 2.5, float('inf')]:
self.assertTrue(gradcheck(lambda x: F.pdist(x, p), (inp,)))
def test_pdist_zeros(self):
"""Test that grad is still valid when dist is 0"""
for device in device_():
inp = torch.randn(1, 3, dtype=torch.double, device=device, requires_grad=True).repeat([2, 1])
for p in [0, 1, 2, 0.5, 1.5, 2.5, float('inf')]:
self.assertTrue(gradcheck(lambda x: F.pdist(x, p), (inp,)))
def test_pdist_empty_row(self):
for device in device_():
inp = torch.randn(1, 3, dtype=torch.double, device=device, requires_grad=True)
self.assertTrue(gradcheck(F.pdist, (inp,)))
def test_pdist_empty_col(self):
for device in device_():
inp = torch.randn(4, 0, dtype=torch.double, device=device, requires_grad=True)
self.assertTrue(gradcheck(F.pdist, (inp,)))
@unittest.expectedFailure
def test_pdist_cpu_gradgrad_unimplemented(self):
inp = torch.randn(4, 5, requires_grad=True)
gradgradcheck(F.pdist, (inp,))
@unittest.expectedFailure
def test_pdist_cuda_gradgrad_unimplemented(self):
inp = torch.randn(4, 5, device='cuda', requires_grad=True)
gradgradcheck(F.pdist, (inp,))
# Merge into OpInfo?
# test for backward in https://github.com/pytorch/pytorch/issues/15511
def test_pdist_large(self):
for device in device_():
def func(x):
return torch.pdist(x, p=2)
# shape[0] should be able to be (roughly) arbitrarily large, but the kernel
# is currently limited to smaller sizes (see issue above); this is just testing
# a floor.
shape = (1000, 1)
x = torch.randn(shape, device=device).requires_grad_()
output = torch.pdist(x, p=2)
# just run a single backward, as gradcheck/gradgradcheck is expensive here
output.sum().backward()
def test_cosine_embedding_loss_with_diff_type(self):
for device in device_():
input1 = torch.tensor([[2, 3, 4], [6, 2, 4]], dtype=torch.double, device=device)
input2 = torch.tensor([[2, 3, 5], [3, 2, 1]], dtype=torch.double, device=device)
target = torch.tensor([1, -1], dtype=torch.int, device=device)
expected = torch.nn.functional.cosine_embedding_loss(input1, input2, target)
for dt1 in get_all_math_dtypes(device):
for dt2 in get_all_math_dtypes(device):
for dt3 in get_all_math_dtypes(device):
# dt3 is used as dtype for target = [1, -1], so let's skip unsigned type
if dt3 == torch.uint8:
continue
if dt1.is_complex or dt2.is_complex or dt3.is_complex:
continue
input1 = input1.to(dt1)
input2 = input2.to(dt2)
target = target.to(dt3)
result = torch.nn.functional.cosine_embedding_loss(input1, input2, target)
self.assertEqual(result.item(), expected.item(), atol=0.001, rtol=0)
def test_kl_div_with_diff_type(self):
for device in device_():
input = torch.tensor([[2, 3, 5], [3, 2, 1]], dtype=torch.double, device=device)
target = torch.tensor([[1, 2, 3], [4, 5, 6]], dtype=torch.double, device=device)
expected = torch.nn.functional.kl_div(input, target)
real_dtypes = (torch.float32, torch.float64, torch.float16)
for input_dtype, target_dtype in product(real_dtypes, repeat=2):
if (torch.device(device).type == 'cpu' and target_dtype == torch.float16):
continue
input = input.to(input_dtype)
target = target.to(target_dtype)
result = torch.nn.functional.kl_div(input, target)
self.assertEqual(result.item(), expected.item(), atol=0.001, rtol=0)
def test_kl_div_with_diff_type_log_target(self):
for device in device_():
input = torch.tensor([[2, 3, 5], [3, 2, 1]], dtype=torch.double, device=device)
target = torch.tensor([[1, 2, 3], [4, 5, 6]], dtype=torch.double, device=device).log()
expected = torch.nn.functional.kl_div(input, target, log_target=True)
real_dtypes = (torch.float32, torch.float64, torch.float16)
for input_dtype, target_dtype in product(real_dtypes, repeat=2):
if (torch.device(device).type == 'cpu' and target_dtype == torch.float16):
continue
input = input.to(input_dtype)
target = target.to(target_dtype)
result = torch.nn.functional.kl_div(input, target, log_target=True)
self.assertEqual(result.item(), expected.item(), atol=0.001, rtol=0)
def test_kl_div_log_softmax_target(self):
for device in device_():
a = torch.tensor([[1.0, 2, 3], [5.0, 5, 5]], device=device)
b = torch.tensor([[1.0, 2, 3], [5.0, 5, 5]], device=device)
self.assertEqual(
F.kl_div(F.log_softmax(a, 1), F.log_softmax(b, 1), reduction='none', log_target=True),
torch.zeros_like(a)
)
def test_cosine_embedding_loss_no_reduce(self):
input1 = torch.randn(15, 10, requires_grad=True, dtype=torch.double)
input2 = torch.randn(15, 10, requires_grad=True, dtype=torch.double)
target = torch.randn(15, dtype=torch.double).sign()
self.assertTrue(gradcheck(lambda x, y, z: F.cosine_embedding_loss(
x, y, z, reduction='none'), (input1, input2, target)))
self.assertEqual(F.cosine_embedding_loss(input1, input2, target, reduction='none'),
loss_reference_fns['CosineEmbeddingLoss'](input1, input2, target, reduction='none'))
def test_cosine_embedding_loss_margin_no_reduce(self):
input1 = torch.randn(15, 10, requires_grad=True, dtype=torch.double)
input2 = torch.randn(15, 10, requires_grad=True, dtype=torch.double)
target = torch.randn(15, dtype=torch.double).sign()
self.assertTrue(gradcheck(lambda x, y, z: F.cosine_embedding_loss(
x, y, z, margin=0.5, reduction='none'), (input1, input2, target)))
self.assertEqual(F.cosine_embedding_loss(input1, input2, target, margin=0.5, reduction='none'),
loss_reference_fns['CosineEmbeddingLoss'](input1, input2, target,
margin=0.5, reduction='none'))
def test_cosine_embedding_loss_invalid_shape(self):
input1 = torch.randn(15, 10)
input2 = torch.randn(15, 10)
target = torch.randn(15, 1).sign()
with self.assertRaisesRegex(RuntimeError, "1D target tensor expected"):
F.cosine_embedding_loss(input1, input2, target)
with self.assertRaisesRegex(RuntimeError, "1D target tensor expects 2D input tensors"):
F.cosine_embedding_loss(torch.randn(10), torch.randn(10), torch.randn(10))
with self.assertRaisesRegex(RuntimeError, "0D target tensor expects 1D input tensors"):
F.cosine_embedding_loss(torch.randn(2, 5), torch.randn(2, 5), torch.randn(()))
def test_margin_ranking_loss_no_reduce(self):
input1 = torch.randn(15, dtype=torch.double).mul_(10).requires_grad_()
input2 = torch.randn(15, dtype=torch.double).mul_(10).requires_grad_()
target = torch.randn(15, dtype=torch.double).sign()
self.assertTrue(gradcheck(lambda x, y, z: F.margin_ranking_loss(
x, y, z, reduction='none'), (input1, input2, target)))
self.assertEqual(F.margin_ranking_loss(input1, input2, target, reduction='none'),
loss_reference_fns['MarginRankingLoss'](input1, input2, target, reduction='none'))
def test_margin_ranking_loss_margin_no_reduce(self):
input1 = torch.randn(15, dtype=torch.double).mul_(10).requires_grad_()
input2 = torch.randn(15, dtype=torch.double).mul_(10).requires_grad_()
target = torch.randn(15, dtype=torch.double).sign()
self.assertTrue(gradcheck(lambda x, y, z: F.margin_ranking_loss(
x, y, z, margin=0.5, reduction='none'), (input1, input2, target)))
self.assertEqual(F.margin_ranking_loss(input1, input2, target, margin=0.5, reduction='none'),
loss_reference_fns['MarginRankingLoss'](input1, input2, target, margin=0.5, reduction='none'))
def test_triplet_margin_loss(self):
input1 = torch.randn(5, 10, requires_grad=True, dtype=torch.double)
input2 = torch.randn(5, 10, requires_grad=True, dtype=torch.double)
input3 = torch.randn(5, 10, requires_grad=True, dtype=torch.double)
self.assertTrue(gradcheck(lambda x1, x2, x3: F.triplet_margin_loss(
x1, x2, x3), (input1, input2, input3)))
self.assertEqual(F.triplet_margin_loss(input1, input2, input3),
loss_reference_fns['TripletMarginLoss'](input1, input2, input3))
def test_triplet_margin_loss_swap(self):
input1 = torch.randn(5, 10, requires_grad=True, dtype=torch.double)
input2 = torch.randn(5, 10, requires_grad=True, dtype=torch.double)
input3 = torch.randn(5, 10, requires_grad=True, dtype=torch.double)
self.assertTrue(gradcheck(lambda x1, x2, x3: F.triplet_margin_loss(
x1, x2, x3, swap=True), (input1, input2, input3)))
self.assertEqual(F.triplet_margin_loss(input1, input2, input3, swap=True),
loss_reference_fns['TripletMarginLoss'](input1, input2, input3, swap=True))
def test_triplet_margin_loss_no_reduce(self):
input1 = torch.randn(5, 10, requires_grad=True, dtype=torch.double)
input2 = torch.randn(5, 10, requires_grad=True, dtype=torch.double)
input3 = torch.randn(5, 10, requires_grad=True, dtype=torch.double)
self.assertTrue(gradcheck(lambda x1, x2, x3: F.triplet_margin_loss(
x1, x2, x3, reduction='none'), (input1, input2, input3)))
self.assertEqual(F.triplet_margin_loss(input1, input2, input3, reduction='none'),
loss_reference_fns['TripletMarginLoss'](input1, input2, input3, reduction='none'))
def test_triplet_margin_loss_swap_no_reduce(self):
input1 = torch.randn(5, 10, requires_grad=True, dtype=torch.double)
input2 = torch.randn(5, 10, requires_grad=True, dtype=torch.double)
input3 = torch.randn(5, 10, requires_grad=True, dtype=torch.double)
self.assertTrue(gradcheck(lambda x1, x2, x3: F.triplet_margin_loss(
x1, x2, x3, swap=True, reduction='none'), (input1, input2, input3)))
self.assertEqual(F.triplet_margin_loss(input1, input2, input3, swap=True, reduction='none'),
loss_reference_fns['TripletMarginLoss'](input1, input2, input3, swap=True, reduction='none'))
def test_pointwise_loss_target_grad_none_reduction(self):
i = torch.randn(5, 10)
t = torch.randn(5, 10, requires_grad=True)
self.assertEqual(F.mse_loss(i, t, reduction='none').size(), t.size())
self.assertEqual(F.l1_loss(i, t, reduction='none').size(), t.size())
def test_pointwise_loss_broadcast(self):
losses = {
'mse_loss': lambda x, y, r: F.mse_loss(x, y, reduction=r),
'l1_loss': lambda x, y, r: F.l1_loss(x, y, reduction=r),
'smooth_l1_loss': lambda x, y, r: F.smooth_l1_loss(x, y, reduction=r),
'huber_loss': lambda x, y, r: F.huber_loss(x, y, reduction=r),
}
input = torch.randn(2, 1, requires_grad=True, dtype=torch.double)
for fn in losses.values():
for requires_grad in [True, False]:
# When target.requires_grad=True, its impl is in Python, while the other is in TH.
target = torch.randn(2, 10, requires_grad=requires_grad, dtype=torch.double)
for reduction in ['none', 'mean', 'sum']:
l = fn(input, target, reduction)
if reduction == 'none':
self.assertEqual(l.size(), target.size())
self.assertTrue(gradcheck(fn, (input, target, reduction)))
# https://github.com/pytorch/pytorch/issues/27692 reports
# that l1_loss get a wrong result for big batch size
def test_l1_loss_correct(self):
for dtype in [torch.float, torch.cfloat]:
for N in range(1, 50, 10):
input = torch.rand(N, 3, 1024, 1024, dtype=dtype)
self.assertEqual(
torch.nn.L1Loss()(input, torch.zeros_like(input)),
input.abs().mean())
def test_smoothl1loss_intergral_target(self):
def _input_grad(input, target, reduction):
output = F.smooth_l1_loss(input, target, reduction=reduction, beta=0.5)
output.sum().backward()
return input.grad
for device, dtype, reduction in product(device_(),
integral_types(),
('none', 'sum', 'mean')):
input = torch.randn(2, 2, device=device, requires_grad=True)
target = torch.randint(0, 9, (2, 2), device=device, dtype=dtype)
input_grad_with_float_target = _input_grad(input, target.float(), reduction)
input_grad = _input_grad(input.detach().clone().requires_grad_(True),
target,
reduction)
self.assertEqual(input_grad, input_grad_with_float_target)
def test_smoothl1loss_negative_beta_not_supported(self):
with self.assertRaises(RuntimeError):
F.smooth_l1_loss(torch.randn(2, 2), torch.randn(2, 2), beta=-1.0)
def test_huber_loss_invalid_delta(self):
def _test_huber_loss_delta_error_helper(delta):
input, target = torch.randn(2, 2), torch.randn(2, 2)
loss = torch.nn.HuberLoss(delta=delta)
with self.assertRaises(RuntimeError):
loss(input, target)
def test_huber_loss_negative_delta():
_test_huber_loss_delta_error_helper(delta=-0.5)
def test_huber_loss_zero_delta():
_test_huber_loss_delta_error_helper(delta=0.0)
test_huber_loss_negative_delta()
test_huber_loss_zero_delta()
@set_default_dtype(torch.double)
def test_cosine_similarity(self):
# Check cosine_similarity input/output shapes
input_size = (1, 3, 2, 1)
expected_size = (1, 2, 1)
input1 = torch.randn(input_size, requires_grad=True)
input2 = torch.randn(input_size, requires_grad=True)
self.assertEqual(F.cosine_similarity(input1, input2, dim=1).size(), expected_size)
# Check numerical precision, issue #18057
vv1 = torch.tensor([float(i) for i in range(84)]).unsqueeze(0)
vv2 = torch.tensor([float(i) for i in range(84)]).unsqueeze(0)
out = F.cosine_similarity(vv1, vv2)
self.assertLessEqual(out, 1.0)
# Check dividing by 0.
# previous behavior: <x,y>/max(eps, ||x|| * ||y||)
# current: <x/max(eps, ||x||), y/max(eps,||y||)>
# if f(x,y) is the cosine similarity, then
# df/dx = y/(||x|| * ||y||) - (x * <x,y> * ||y||/||x||)/(||x|| * ||y||)^2
# the tests below check division by zero in the backward formula when
# x := input2 = 0, y := input1 != 0.
# For these inputs the gradient wrt x simplifies to g(x,y) := y/(||x|| * ||y||)
# Previous test checks g(x,y) == y/eps,
# Current test checks g(x,y) == (y/||y||)/eps.
input1 = torch.randn(10).requires_grad_()
input2 = torch.zeros_like(input1).requires_grad_()
torch.cosine_similarity(input1, input2, 0).sum().backward()
self.assertEqual(input1.grad, torch.zeros_like(input1))
self.assertEqual(input2.grad, input1 / input1.norm() * 1e8)
# Check type promotion, issue #61454
input = torch.tensor(12.)
out = F.cosine_similarity(input.to(torch.int8), input, dim=-1)
self.assertEqual(out, 1.)
def test_grid_sample_error_checking(self):
input = torch.empty(1, 1, 2, 2)
grid = torch.empty(1, 1, 1, 2)
# assert no error
F.grid_sample(input, grid, align_corners=False)
with self.assertRaisesRegex(ValueError, "but got: 'garbage'"):
F.grid_sample(input, grid, mode='garbage', align_corners=False)
with self.assertRaisesRegex(ValueError, "but got: 'garbage'"):
F.grid_sample(input, grid, padding_mode='garbage', align_corners=False)
with self.assertRaisesRegex(RuntimeError, "expected grid to have size 1 in last dimension"):
F.grid_sample(input[0], grid, align_corners=False)
with self.assertRaisesRegex(RuntimeError, "expected grid to have size 2 in last dimension"):
F.grid_sample(input, torch.empty(1, 1, 1, 1, 3), align_corners=False)
with self.assertRaisesRegex(RuntimeError, "expected grid and input to have same batch size"):
F.grid_sample(input, torch.empty(2, 1, 1, 2), align_corners=False)
with self.assertRaisesRegex(RuntimeError, "expected grid to have size 2 in last dimension"):
F.grid_sample(input, torch.empty(1, 1, 1, 3), align_corners=False)
with self.assertRaisesRegex(RuntimeError, "expected input to have non-empty spatial dimensions"):
F.grid_sample(torch.empty(1, 1, 0, 2), grid, align_corners=False)
with self.assertRaisesRegex(RuntimeError, "bicubic interpolation only supports 4D input"):
F.grid_sample(torch.empty(1, 1, 2, 2, 2), torch.empty(1, 1, 1, 1, 3), mode='bicubic')
if TEST_CUDA:
with self.assertRaisesRegex(RuntimeError, "Expected all tensors to be on the same device"):
F.grid_sample(input.cuda(), grid, align_corners=False)
def test_affine_grid_error_checking(self):
# 2D affine
theta = torch.empty(1, 2, 3, dtype=torch.double)
size = torch.Size([1, 1, 2, 2])
# assert no error
F.affine_grid(theta, size, align_corners=False)
# check for warning for empty span along dimension
with warnings.catch_warnings(record=True) as w:
# Ensure warnings are being shown
warnings.simplefilter("always")
# Should not trigger warning
F.affine_grid(theta, torch.Size([1, 1, 2, 1]), align_corners=False)
# Check no warning occurs
self.assertNotIn('See the documentation of affine_grid for details.', ' '.join(map(str, w)))
# Should trigger warning
F.affine_grid(theta, torch.Size([1, 1, 2, 1]), align_corners=True)
# Check warning occurs
self.assertIn('See the documentation of affine_grid for details.', ' '.join(map(str, w)))
with self.assertRaisesRegex(ValueError, "Expected theta to have floating point type"):
F.affine_grid(theta.int(), size, align_corners=False)
with self.assertRaisesRegex(ValueError, "Expected a batch of 2D affine matrices of shape Nx2x3"):
F.affine_grid(theta[0], size, align_corners=False)
with self.assertRaisesRegex(ValueError, "Expected a batch of 2D affine matrices of shape Nx2x3"):
F.affine_grid(theta.unsqueeze(0), size, align_corners=False)
with self.assertRaisesRegex(ValueError, "Expected a batch of 2D affine matrices of shape Nx2x3"):
F.affine_grid(theta.repeat(1, 2, 1), size, align_corners=False)
with self.assertRaisesRegex(ValueError, "Expected a batch of 2D affine matrices of shape Nx2x3"):
F.affine_grid(theta.repeat(1, 1, 2), size, align_corners=False)
# 3D affine
theta = torch.empty(1, 3, 4, dtype=torch.double)
size = torch.Size([1, 1, 2, 2, 2])
# assert no error
F.affine_grid(theta, size, align_corners=False)
# check for warning for empty span along dimension
with warnings.catch_warnings(record=True) as w:
# Ensure warnings are being shown
warnings.simplefilter("always")
# Should not trigger warning
F.affine_grid(theta, torch.Size([1, 1, 3, 2, 1]), align_corners=False)
# Check no warning occurs
self.assertNotIn('See the documentation of affine_grid for details.', ' '.join(map(str, w)))
# Should trigger warning
F.affine_grid(theta, torch.Size([1, 1, 3, 2, 1]), align_corners=True)
# Check warning occurs
self.assertIn('See the documentation of affine_grid for details.', ' '.join(map(str, w)))
with self.assertRaisesRegex(ValueError, "Expected a batch of 3D affine matrices of shape Nx3x4"):
F.affine_grid(theta[0], size, align_corners=False)
with self.assertRaisesRegex(ValueError, "Expected a batch of 3D affine matrices of shape Nx3x4"):
F.affine_grid(theta.unsqueeze(0), size, align_corners=False)
with self.assertRaisesRegex(ValueError, "Expected a batch of 3D affine matrices of shape Nx3x4"):
F.affine_grid(theta.repeat(1, 2, 1), size, align_corners=False)
with self.assertRaisesRegex(ValueError, "Expected a batch of 3D affine matrices of shape Nx3x4"):
F.affine_grid(theta.repeat(1, 1, 2), size, align_corners=False)
with self.assertRaisesRegex(NotImplementedError, "affine_grid only supports 4D and 5D sizes"):
F.affine_grid(theta, torch.Size([1, 2, 2]), align_corners=False)
with self.assertRaisesRegex(NotImplementedError, "affine_grid only supports 4D and 5D sizes"):
F.affine_grid(theta, torch.Size([1, 1, 2, 2, 2, 2]), align_corners=False)
@set_default_dtype(torch.double)
def test_grid_sample(self):
# Backward pass of native C++ and CUDA kernels branch depending on whether input requires gradient,
# so we test both cases.
def test(N, C, H, W, mode, padding_mode, align_corners, input_requires_grad):
def test_shape(N, C, IH, IW, H, W, mode, padding_mode, align_corners):
for grid_dim_contig_order in [(0, 1, 2, 3), (0, 3, 1, 2), (3, 0, 1, 2), (0, 2, 1, 3)]:
# grid_dim_contig_order specifies the dimension order that can
# make grid to be contiguous.
# i.e., grid.permute(grid_dim_contig_order) is contiguous.
# e.g., with grid_dim_contig_order=[0, 3, 1, 2], grid should be
# initialized with contiguous tensor of shape [N, 2, H, W]
# and permuted to [N, H, W, 2] afterwards.
grid_shape = [N, H, W, 2]
grid_init_shape = [grid_shape[d] for d in grid_dim_contig_order]
grid_fwd_permute = [None, None, None, None]
for i, d in enumerate(grid_dim_contig_order):
grid_fwd_permute[d] = i
def get_grid(device='cpu', data=None):
if data is not None:
assert list(data.shape) == grid_shape
data = data.permute(grid_dim_contig_order).to(device)
else:
data = torch.randn(grid_init_shape, device=device)
grid = data.permute(grid_fwd_permute)
assert grid.permute(grid_dim_contig_order).is_contiguous()
return grid
input_cpu = torch.randn(C, N, IH, IW).transpose(0, 1).requires_grad_(input_requires_grad)
grid_cpu = get_grid().requires_grad_()
out_cpu = F.grid_sample(input_cpu, grid_cpu, mode=mode, padding_mode=padding_mode,
align_corners=align_corners)
self.assertTrue(out_cpu.size() == torch.Size([N, C, H, W]))
gradients = torch.randn_like(out_cpu)
out_cpu.backward(gradients)
# Compare against unvectorized CPU fallback
# NOTE [ grid_sample CPU fallback ]
# grid_sample uses AVX for 2d images, but that requires 32-bit indexing for
# 32-bit floats. So we also have a fallback that is used only for float tensors
# requiring 64-bit indexing. That requires too much memory to run on CI, so we
# also export the fallback and test it here to ensure feature parity with
# the vectorized version.
input_fallback = input_cpu.float().detach_().requires_grad_()
grid_fallback = grid_cpu.float().detach_().requires_grad_()
out_fallback = torch._grid_sampler_2d_cpu_fallback(
input_fallback, grid_fallback,
F.GRID_SAMPLE_INTERPOLATION_MODES[mode],
F.GRID_SAMPLE_PADDING_MODES[padding_mode],
align_corners)
self.assertEqual(out_fallback, out_cpu.float(), atol=1e-5, rtol=5e-5)
out_fallback.backward(gradients.float())
if input_requires_grad:
self.assertEqual(input_fallback.grad, input_cpu.grad.float(), atol=1e-4, rtol=5e-5)
self.assertEqual(grid_fallback.grad, grid_cpu.grad.float(), atol=1e-4, rtol=5e-5)
if TEST_CUDA:
input_cuda = input_cpu.detach().transpose(0, 1).cuda().transpose(0, 1).requires_grad_(input_requires_grad)
grid_cuda = get_grid('cuda', grid_cpu.detach()).requires_grad_()
out_cuda = F.grid_sample(input_cuda, grid_cuda, mode=mode, padding_mode=padding_mode,
align_corners=align_corners)
self.assertEqual(out_cpu, out_cuda)
out_cuda.backward(gradients.cuda())
if input_requires_grad:
self.assertEqual(input_cpu.grad, input_cuda.grad)
self.assertEqual(grid_cpu.grad, grid_cuda.grad, atol=5e-5, rtol=0)
# check that zero-dimensional input strides don't error out
base_input = torch.randn(N, C, 1, IW)
input_cpu = base_input.expand_as(input_cuda).requires_grad_(input_requires_grad)
out_cpu = F.grid_sample(input_cpu, grid_cpu, mode=mode, padding_mode=padding_mode,
align_corners=align_corners)
input_cuda = base_input.cuda().expand_as(input_cuda).requires_grad_(input_requires_grad)
out_cuda = F.grid_sample(input_cuda, grid_cuda, mode=mode, padding_mode=padding_mode,
align_corners=align_corners)
self.assertEqual(out_cpu, out_cuda)
# test same size output
test_shape(N, C, H, W, H, W, mode, padding_mode, align_corners)
# test larger output
N = random.randint(2, 8)
C = random.randint(2, 8)
IH = random.randint(2, 8)
IW = random.randint(2, 8)
H = random.randint(IH + 1, 12)
W = random.randint(IW + 1, 12)
test_shape(N, C, IH, IW, H, W, mode, padding_mode, align_corners)
# test smaller output
N = random.randint(2, 8)
C = random.randint(2, 8)
IH = random.randint(2, 8)
IW = random.randint(2, 8)
H = random.randint(2, IH)
W = random.randint(2, IW)
test_shape(N, C, IH, IW, H, W, mode, padding_mode, align_corners)
# test 1x1 inpput
N = random.randint(2, 8)
C = random.randint(2, 8)
IH = 1
IW = 1
H = random.randint(2, 5)
W = random.randint(2, 5)
test_shape(N, C, IH, IW, H, W, mode, padding_mode, align_corners)
# testing empty grid
N = random.randint(2, 8)
C = random.randint(2, 8)
IH = random.randint(2, 8)
IW = random.randint(2, 8)
W = random.randint(3, IW + 2)
test_shape(N, C, IH, IW, 0, W, mode, padding_mode, align_corners)
# testing empty channel
N = random.randint(2, 8)
IH = random.randint(2, 8)
IW = random.randint(2, 8)
H = random.randint(3, IH + 2)
W = random.randint(3, IW + 2)
test_shape(N, 0, IH, IW, H, W, mode, padding_mode, align_corners)
# testing empty batch
C = random.randint(2, 8)
IH = random.randint(2, 8)
IW = random.randint(2, 8)
H = random.randint(3, IH + 2)
W = random.randint(3, IW + 2)
test_shape(0, C, IH, IW, H, W, mode, padding_mode, align_corners)
for mode in ('bilinear', 'nearest', 'bicubic'):
for padding_mode in ('zeros', 'border', 'reflection'):
for align_corners in (True, False):
# test known input on CPU
input = torch.arange(1., 11).view(1, 1, 2, 5)
grid = torch.tensor(
[[[-0.9, -4.1], [0, 0.2000], [1, -1], [-0.333, 1e-6], [0.5, 1.0]],
[[-1.0, -0.5], [0, 0.3333], [1, -1], [-0.200, 1e-6], [1.5, 0.5]]]).view(1, 2, 5, 2)
if mode == 'bilinear':
if padding_mode == 'zeros':
if align_corners:
groundtruth = torch.tensor(
[[0.0000, 6.0000000000, 5.0000, 4.8340, 9.0000],
[2.2500, 6.3332500450, 5.0000, 5.1000, 0.0000]]).view(1, 1, 2, 5)
else:
groundtruth = torch.tensor(
[[0.0000, 6.5000000000, 1.2500, 4.6675000191, 4.6250],
[0.5000, 7.1665000916, 1.2500, 5.0000000000, 0.0000]]).view(1, 1, 2, 5)
elif padding_mode == 'border':
if align_corners:
groundtruth = torch.tensor(
[[1.2000, 6.0000000000, 5.0000, 4.8340, 9.0000],
[2.2500, 6.3332500450, 5.0000, 5.1000, 8.7500]]).view(1, 1, 2, 5)
else:
groundtruth = torch.tensor(
[[1.0000, 6.5000000000, 5.0000, 4.6675000191, 9.2500],
[1.0000, 7.1665000916, 5.0000, 5.0000000000, 10.0000]]).view(1, 1, 2, 5)
elif padding_mode == 'reflection':
if align_corners:
groundtruth = torch.tensor(
[[3.4500, 6.0000000000, 5.0000, 4.8340, 9.0000],
[2.2500, 6.3332500450, 5.0000, 5.1000, 7.7500]]).view(1, 1, 2, 5)
else:
groundtruth = torch.tensor(
[[3.0000004768, 6.5000000000, 5.0000, 4.6675000191, 9.2500],
[1.0000000000, 7.1665000916, 5.0000, 5.0000000000, 9.2500]]).view(1, 1, 2, 5)
else:
raise AssertionError(f"missing groundtruth test for padding mode '{padding_mode}'")
elif mode == 'nearest':
if padding_mode == 'zeros':
if align_corners:
groundtruth = torch.tensor(
[[0., 8., 5., 7., 9.],
[1., 8., 5., 8., 0.]]).view(1, 1, 2, 5)
else:
groundtruth = torch.tensor(
[[0., 8., 5., 7., 0.],
[1., 8., 5., 8., 0.]]).view(1, 1, 2, 5)
elif padding_mode == 'border':
if align_corners:
groundtruth = torch.tensor(
[[1., 8., 5., 7., 9.],
[1., 8., 5., 8., 10.]]).view(1, 1, 2, 5)
else:
groundtruth = torch.tensor(
[[1., 8., 5., 7., 9.],
[1., 8., 5., 8., 10.]]).view(1, 1, 2, 5)
elif padding_mode == 'reflection':
if align_corners:
groundtruth = torch.tensor(
[[1., 8., 5., 7., 9.],
[1., 8., 5., 8., 9.]]).view(1, 1, 2, 5)
else:
groundtruth = torch.tensor(
[[1., 8., 5., 7., 9.],
[1., 8., 5., 8., 9.]]).view(1, 1, 2, 5)
else:
raise AssertionError(f"missing groundtruth test for padding mode '{padding_mode}'")
elif mode == 'bicubic':
if padding_mode == 'zeros':
if align_corners:
groundtruth = torch.tensor(
[[-0.10424726, 7.1400003, 5.0000, 5.7842274, 9.0000],
[2.4492188, 7.4814040, 5.0000, 6.0277520, 0.0000]]).view(1, 1, 2, 5)
else:
groundtruth = torch.tensor(
[[0.00000, 7.6287503, 1.0625, 5.5977230, 5.3270264],
[0.40625, 8.0288770, 1.0625, 5.9375067, -0.3515625]]).view(1, 1, 2, 5)
elif padding_mode == 'border':
if align_corners:
groundtruth = torch.tensor(
[[1.1520010, 6.0599990, 5.0000, 4.870930, 9.0000000],
[2.1328125, 6.4258375, 5.0000, 5.076003, 8.8671875]]).view(1, 1, 2, 5)
else:
groundtruth = torch.tensor(
[[0.894531, 6.6050020, 4.625, 4.7138715, 9.800781],
[0.906250, 7.2822485, 4.625, 5.0000052, 10.00000]]).view(1, 1, 2, 5)
elif padding_mode == 'reflection':
if align_corners:
groundtruth = torch.tensor(
[[3.1822524, 6.239998, 5.0000, 4.8709273, 9.00000],
[1.7812500, 6.703594, 5.0000, 5.0760007, 8.21875]]).view(1, 1, 2, 5)
else:
groundtruth = torch.tensor(
[[2.7993753, 6.6050020, 4.25, 4.7138715, 10.269531],
[0.8125000, 7.2822485, 4.25, 5.0000052, 9.332031]]).view(1, 1, 2, 5)
else:
raise AssertionError(f"missing groundtruth test for padding mode '{padding_mode}'")
else:
raise AssertionError(f"missing groundtruth test for interpolation mode '{mode}'")
output = F.grid_sample(input, grid, mode=mode, padding_mode=padding_mode,
align_corners=align_corners)
self.assertEqual(output, groundtruth, atol=1e-5, rtol=0,
msg=f"groundtruth comparison failed for mode={mode}, "
f"padding_mode={padding_mode}")
# See NOTE [ grid_sample CPU fallback ]
output = torch._grid_sampler_2d_cpu_fallback(
input.float(), grid.float(),
F.GRID_SAMPLE_INTERPOLATION_MODES[mode],
F.GRID_SAMPLE_PADDING_MODES[padding_mode],
align_corners)
self.assertEqual(output, groundtruth.float(), atol=1e-5, rtol=0)
# explicit check for gradient edge cases
input = torch.arange(0., 5).expand((1, 1, 5, 5))
grid = torch.tensor(
[[[1.0, 1.0], [1.0, -1.0], [0.8, 0.8], [0.8, -0.8]],
[[-1.0, -1.0], [-1.0, 1.0], [-0.8, -0.8], [-0.8, 0.8]]]).view(1, 2, 4, 2).requires_grad_()
if mode == 'bilinear':
if padding_mode == 'zeros':
if align_corners:
groundtruth = torch.tensor(
[[[[-8., -8.], [-8., 0.], [2., 0.], [2., 0.]],
[[2., 0.], [2., 0.], [2., 0.], [2., 0.]]]]).view(1, 2, 4, 2)
else:
groundtruth = torch.tensor(
[[[[-5., -5.], [-5., 5.], [-10., -10.], [-10., 10.]],
[[0., 0.], [0., 0.], [0., 0.], [0., 0.]]]]).view(1, 2, 4, 2)
elif padding_mode == 'border':
if align_corners:
groundtruth = torch.tensor(
[[[[-0., -0.], [-0., 0.], [2., 0.], [2., 0.]],
[[0., 0.], [0., 0.], [2., 0.], [2., 0.]]]]).view(1, 2, 4, 2)
else:
groundtruth = torch.tensor(
[[[[-0., -0.], [-0., 0.], [-0., -0.], [-0., 0.]],
[[0., 0.], [0., 0.], [0., 0.], [0., 0.]]]]).view(1, 2, 4, 2)
elif padding_mode == 'reflection':
if align_corners:
groundtruth = torch.tensor(
[[[[-0., -0.], [-0., 0.], [2., 0.], [2., 0.]],
[[0., 0.], [0., 0.], [2., 0.], [2., 0.]]]]).view(1, 2, 4, 2)
else:
groundtruth = torch.tensor(
[[[[-0., -0.], [-0., 0.], [-0., -0.], [-0., 0.]],
[[0., 0.], [0., 0.], [0., 0.], [0., 0.]]]]).view(1, 2, 4, 2)
else:
raise AssertionError(f"missing gradient groundtruth test for padding mode '{padding_mode}'")
elif mode == 'nearest':
groundtruth = torch.tensor(
[[[[-0., -0.], [-0., 0.], [-0., -0.], [-0., 0.]],
[[0., 0.], [0., 0.], [0., 0.], [0., 0.]]]]).view(1, 2, 4, 2)
elif mode == 'bicubic':
if padding_mode == 'zeros':
if align_corners:
groundtruth = torch.tensor(
[[[[-4.5, -6.], [-4.5, 6.], [2.725679, 0.740878], [2.725679, -0.740878]],
[[1.5, 0.], [1.5, 0.], [1.927921, -0.05688], [1.927921, 0.05688]]]]).view(1, 2, 4, 2)
else:
groundtruth = torch.tensor(
[[[[-5.859375, -5.888672], [-5.859375, 5.888672], [-5.6250, -7.5000], [-5.6250, 7.5000]],
[[-0.234375, -0.263672], [-0.234375, 0.263672], [1.8750, 0.], [1.8750, 0.]]]]
).view(1, 2, 4, 2)
elif padding_mode == 'border':
if align_corners:
groundtruth = torch.tensor(
[[[[1.5, 0.], [1.5, 0.], [1.74, 0.], [1.74, 0.]],
[[1.5, 0.], [1.5, 0.], [1.74, 0.], [1.74, 0.]]]]).view(1, 2, 4, 2)
else:
groundtruth = torch.tensor(
[[[[-0.46875, 0.], [-0.46875, 0.], [1.8750, 0.], [1.8750, 0.]],
[[-0.46875, 0.], [-0.46875, 0.], [1.8750, 0.], [1.8750, 0.]]]]).view(1, 2, 4, 2)
elif padding_mode == 'reflection':
if align_corners:
groundtruth = torch.tensor(
[[[[0., 0.], [0., 0.], [1.92, 0.], [1.92, 0.]],
[[0., 0.], [0., 0.], [1.92, 0.], [1.92, 0.]]]]).view(1, 2, 4, 2)
else:
groundtruth = torch.tensor(
[[[[0., 0.], [0., 0.], [1.875, 0.], [1.875, 0.]],
[[0., 0.], [0., 0.], [1.875, 0.], [1.875, 0.]]]]).view(1, 2, 4, 2)
else:
raise AssertionError(f"missing gradient groundtruth test for padding mode '{padding_mode}'")
else:
raise AssertionError(f"missing gradient groundtruth test for interpolation mode '{mode}'")
for input_requires_grad in [False, True]:
input = input.requires_grad_(input_requires_grad)
F.grid_sample(input, grid, mode=mode, padding_mode=padding_mode,
align_corners=align_corners).sum().backward()
self.assertEqual(grid.grad, groundtruth, atol=1e-5, rtol=0,
msg=f"gradient groundtruth comparison failed for mode={mode}, "
f"padding_mode={padding_mode}, input_requires_grad={input_requires_grad}")
grid.grad.zero_()
# See NOTE [ grid_sample CPU fallback ]
torch._grid_sampler_2d_cpu_fallback(
input.float(), grid.float(),
F.GRID_SAMPLE_INTERPOLATION_MODES[mode],
F.GRID_SAMPLE_PADDING_MODES[padding_mode],
align_corners).sum().backward()
self.assertEqual(grid.grad, groundtruth, atol=1e-5, rtol=0)
# do gradcheck
N = random.randint(2, 8)
C = random.randint(2, 6)
H = random.randint(2, 8)
W = random.randint(2, 8)
input = torch.randn(N, C, H, W, requires_grad=True)
grid = torch.randn(N, H, W, 2, requires_grad=True)
for input_requires_grad in [False, True]:
input.requires_grad_(input_requires_grad)
self.assertTrue(gradcheck(
lambda inp, grd: F.grid_sample(inp, grd, mode=mode, padding_mode=padding_mode,
align_corners=align_corners),
(input, grid)))
test(N, C, H, W, mode, padding_mode, align_corners, input_requires_grad)
if TEST_CUDNN:
with cudnn.flags(enabled=False):
test(N, C, H, W, mode, padding_mode, align_corners, input_requires_grad)
@set_default_dtype(torch.double)
def test_grid_sample_3d(self):
# Backward pass of native C++ and CUDA kernels branch depending on whether input requires gradient,
# so we test both cases.
def test(N, C, D, H, W, mode, padding_mode, align_corners, input_requires_grad):
def test_shape(N, C, ID, IH, IW, D, H, W, mode, padding_mode, align_corners):
input_cpu = torch.randn(C, N, ID, IH, IW).transpose(0, 1).requires_grad_(input_requires_grad)
grid_cpu = torch.randn(D, N, H, W, 3).transpose(0, 1).requires_grad_()
out_cpu = F.grid_sample(input_cpu, grid_cpu, mode=mode, padding_mode=padding_mode,
align_corners=align_corners)
self.assertTrue(out_cpu.size() == torch.Size([N, C, D, H, W]))
gradients = torch.randn_like(out_cpu)
out_cpu.backward(gradients)
if TEST_CUDA:
input_cuda = input_cpu.detach().transpose(0, 1).cuda().transpose(0, 1).requires_grad_(input_requires_grad)
grid_cuda = grid_cpu.detach().transpose(0, 1).cuda().transpose(0, 1).requires_grad_()
out_cuda = F.grid_sample(input_cuda, grid_cuda, mode=mode, padding_mode=padding_mode,
align_corners=align_corners)
self.assertEqual(out_cpu, out_cuda)
out_cuda.backward(gradients.cuda())
if input_requires_grad:
self.assertEqual(input_cpu.grad, input_cuda.grad)
self.assertEqual(grid_cpu.grad, grid_cuda.grad, atol=5e-5, rtol=0)
# check that zero-dimensional input strides don't error out
base_input = torch.randn(N, C, 1, IH, IW)
input_cpu = base_input.expand_as(input_cuda).requires_grad_(input_requires_grad)
grid_cpu = torch.randn(N, D, H, W, 3, requires_grad=True)
out_cpu = F.grid_sample(input_cpu, grid_cpu, mode=mode, padding_mode=padding_mode,
align_corners=align_corners)
input_cuda = base_input.cuda().expand_as(input_cuda).requires_grad_(input_requires_grad)
grid_cuda = grid_cpu.detach().cuda().requires_grad_()
out_cuda = F.grid_sample(input_cuda, grid_cuda, mode=mode, padding_mode=padding_mode,
align_corners=align_corners)
self.assertEqual(out_cpu, out_cuda)
# test same size output
test_shape(N, C, D, H, W, D, H, W, mode, padding_mode, align_corners)
# test larger output
N = random.randint(2, 7)
C = random.randint(2, 5)
ID = random.randint(2, 7)
IH = random.randint(2, 7)
IW = random.randint(2, 7)
D = random.randint(ID + 1, 10)
H = random.randint(IH + 1, 10)
W = random.randint(IW + 1, 10)
test_shape(N, C, ID, IH, IW, D, H, W, mode, padding_mode, align_corners)
# test smaller output
N = random.randint(2, 7)
C = random.randint(2, 5)
ID = random.randint(2, 7)
IH = random.randint(2, 7)
IW = random.randint(2, 7)
D = random.randint(2, ID)
H = random.randint(2, IH)
W = random.randint(2, IW)
test_shape(N, C, ID, IH, IW, D, H, W, mode, padding_mode, align_corners)
# test 1x1 inpput
N = random.randint(2, 7)
C = random.randint(2, 7)
ID = 1
IH = 1
IW = 1
H = random.randint(2, 5)
W = random.randint(2, 5)
test_shape(N, C, ID, IH, IW, D, H, W, mode, padding_mode, align_corners)
# testing empty grid
N = random.randint(2, 7)
C = random.randint(2, 5)
ID = random.randint(2, 7)
IH = random.randint(2, 7)
IW = random.randint(2, 7)
D = random.randint(3, ID + 2)
W = random.randint(3, IW + 2)
test_shape(N, C, ID, IH, IW, D, 0, W, mode, padding_mode, align_corners)
# testing empty channel
N = random.randint(2, 7)
ID = random.randint(2, 5)
IH = random.randint(2, 7)
IW = random.randint(2, 7)
D = random.randint(3, ID + 2)
H = random.randint(3, IH + 2)
W = random.randint(3, IW + 2)
test_shape(N, 0, ID, IH, IW, D, H, W, mode, padding_mode, align_corners)
# testing empty batch
C = random.randint(2, 5)
ID = random.randint(2, 7)
IH = random.randint(2, 7)
IW = random.randint(2, 7)
D = random.randint(3, ID + 2)
H = random.randint(3, IH + 2)
W = random.randint(3, IW + 2)
test_shape(0, C, ID, IH, IW, D, H, W, mode, padding_mode, align_corners)
for mode in ('bilinear', 'nearest'):
for padding_mode in ('zeros', 'border', 'reflection'):
for align_corners in (True, False):
# do gradcheck
N = random.randint(2, 5)
C = random.randint(2, 4)
D = random.randint(2, 5)
H = random.randint(2, 5)
W = random.randint(2, 5)
input = torch.randn(N, C, D, H, W, requires_grad=True)
grid = torch.randn(N, D, H, W, 3, requires_grad=True)
self.assertTrue(gradcheck(
lambda inp, grid: F.grid_sample(inp, grid, mode=mode, padding_mode=padding_mode,
align_corners=align_corners),
(input, grid)))
input = input.requires_grad_(False)
self.assertTrue(gradcheck(
lambda grid: F.grid_sample(input, grid, mode=mode, padding_mode=padding_mode,
align_corners=align_corners),
(grid,)))
for input_requires_grad in [False, True]:
test(N, C, D, H, W, mode, padding_mode, align_corners, input_requires_grad)
def test_grid_sample_nearest_neighbor_rounding_mode_consistency(self):
device_list = ['cpu']
if TEST_CUDA:
device_list.append('cuda')
def normalize_indices(indices_unnormalized: torch.Tensor, dim_size: int, align_corners: bool):
if align_corners:
indices_normalized = 2 * indices_unnormalized / (dim_size - 1) - 1
else:
indices_normalized = (indices_unnormalized * 2 + 1) / dim_size - 1
return indices_normalized
test_dim_size = 10
non_test_dim_size = 9
step_size = 0.1
batch_size = 1
channel_size = 1
mode = 'nearest'
for device in device_list:
for padding_mode in ('zeros', 'border', 'reflection'):
for align_corners in (True, False):
# Unnormalized inquiry indices
inquiry_indices_unnormalized = torch.arange(
0,
test_dim_size - 1 + step_size, step_size,
dtype=torch.float32,
device=device
)
# Note that even though we are trying to create normalized indices
# which results in x.0 and x.5 indices after unnormalization,
# because of the numerical error,
# the rounding direction might not always be expected as designed.
# The best we could do is to ensure the rounding behaviors across
# different implementations for different dimensions are
# exactly the same.
inquiry_indices = normalize_indices(
indices_unnormalized=inquiry_indices_unnormalized,
dim_size=test_dim_size,
align_corners=align_corners
)
num_inqueries = inquiry_indices.shape[0]
inquiry_fixed_indices = torch.full((num_inqueries,), 0.5, dtype=torch.float32, device=device)
array_data = torch.rand(test_dim_size, dtype=torch.float32, device=device)
# 2D grid sample x-dim interpolation
# The input_tensor_2d_x is of shape
# [batch_size, channel_size, non_test_dim_size, test_dim_size]
input_tensor_2d_x = array_data.reshape(1, test_dim_size).repeat(
batch_size,
channel_size,
non_test_dim_size,
1
)
# The grid_tensor_2d_x is of shape
# [batch_size, 1, num_inqueries]
grid_tensor_2d_x = torch.cat(
tensors=(
inquiry_indices.reshape(num_inqueries, 1),
inquiry_fixed_indices.reshape(num_inqueries, 1),
),
dim=1
).repeat(batch_size, 1, 1, 1)
# The output_tensor_2d_x is of shape
# [batch_size, channel_size, 1, num_inqueries]
output_tensor_2d_x = F.grid_sample(
input=input_tensor_2d_x,
grid=grid_tensor_2d_x,
mode=mode,
padding_mode=padding_mode,
align_corners=align_corners,
)
# 2D grid sample y-dim interpolation
# The input_tensor_2d_y is of shape
# [batch_size, channel_size, test_dim_size, non_test_dim_size]
input_tensor_2d_y = torch.transpose(input_tensor_2d_x, 3, 2)
# The grid_tensor_2d_y is of shape
# [batch_size, 1, num_inqueries]
grid_tensor_2d_y = torch.index_select(
grid_tensor_2d_x,
-1,
torch.tensor([1, 0], dtype=torch.int64, device=device)
)
# The output_tensor_2d_y is of shape
# [batch_size, channel_size, 1, num_inqueries]
output_tensor_2d_y = F.grid_sample(
input=input_tensor_2d_y,
grid=grid_tensor_2d_y,
mode=mode,
padding_mode=padding_mode,
align_corners=align_corners,
)
self.assertEqual(output_tensor_2d_x[0, 0, 0, :], output_tensor_2d_y[0, 0, 0, :], atol=0, rtol=0)
# 3D grid sample x-dim interpolation
# The input_tensor_3d_x is of shape
# [batch_size, channel_size, non_test_dim_size, non_test_dim_size, test_dim_size]
input_tensor_3d_x = array_data.reshape(1, test_dim_size).repeat(
batch_size, channel_size, non_test_dim_size, non_test_dim_size, 1)
# The grid_tensor_3d_x is of shape
# [batch_size, 1, 1, num_inqueries]
grid_tensor_3d_x = torch.cat(
tensors=(
inquiry_indices.reshape(num_inqueries, 1),
inquiry_fixed_indices.reshape(num_inqueries, 1),
inquiry_fixed_indices.reshape(num_inqueries, 1),
),
dim=1
).repeat(batch_size, 1, 1, 1, 1)
# The output_tensor_3d_x is of shape
# [batch_size, channel_size, 1, 1, num_inqueries]
output_tensor_3d_x = F.grid_sample(
input=input_tensor_3d_x,
grid=grid_tensor_3d_x,
mode=mode,
padding_mode=padding_mode,
align_corners=align_corners,
)
self.assertEqual(output_tensor_2d_x[0, 0, 0, :], output_tensor_3d_x[0, 0, 0, 0, :], atol=0, rtol=0)
# 3D grid sample y-dim interpolation
# The input_tensor_3d_y is of shape
# [batch_size, channel_size, non_test_dim_size, test_dim_size, non_test_dim_size]
input_tensor_3d_y = torch.transpose(input_tensor_3d_x, 4, 3)
# The grid_tensor_3d_y is of shape
# [batch_size, 1, 1, num_inqueries]
grid_tensor_3d_y = torch.index_select(
grid_tensor_3d_x,
-1,
torch.tensor([1, 0, 2], dtype=torch.int64, device=device)
)
# The output_tensor_3d_y is of shape
# [batch_size, channel_size, 1, 1, num_inqueries]
output_tensor_3d_y = F.grid_sample(
input=input_tensor_3d_y,
grid=grid_tensor_3d_y,
mode=mode,
padding_mode=padding_mode,
align_corners=align_corners,
)
self.assertEqual(output_tensor_2d_x[0, 0, 0, :], output_tensor_3d_y[0, 0, 0, 0, :], atol=0, rtol=0)
# 3D grid sample z-dim interpolation
# The input_tensor_3d_z is of shape
# [batch_size, channel_size, non_test_dim_size, non_test_dim_size, test_dim_size]
input_tensor_3d_z = torch.transpose(input_tensor_3d_x, 4, 2)
# The grid_tensor_3d_z is of shape
# [batch_size, 1, 1, num_inqueries]
grid_tensor_3d_z = torch.index_select(
grid_tensor_3d_x,
-1,
torch.tensor([1, 2, 0], dtype=torch.int64, device=device)
)
# The output_tensor_3d_z is of shape
# [batch_size, channel_size, 1, 1, num_inqueries]
output_tensor_3d_z = F.grid_sample(
input=input_tensor_3d_z,
grid=grid_tensor_3d_z,
mode=mode,
padding_mode=padding_mode,
align_corners=align_corners,
)
self.assertEqual(output_tensor_2d_x[0, 0, 0, :], output_tensor_3d_z[0, 0, 0, 0, :], atol=0, rtol=0)
@set_default_dtype(torch.double)
def test_affine_grid(self):
# test known input on CPU
input = torch.arange(1., 7).view(1, 2, 3)
output = F.affine_grid(input, torch.Size([1, 1, 2, 2]), align_corners=True)
groundtruth = torch.tensor(
[[[0., -3.], [2., 5.]], [[4., 7.], [6., 15.]]]).view(1, 2, 2, 2)
self.assertEqual(output, groundtruth)
output = F.affine_grid(input, torch.Size([1, 1, 2, 2]), align_corners=False)
groundtruth = torch.tensor(
[[[1.5, 1.5], [2.5, 5.5]], [[3.5, 6.5], [4.5, 10.5]]]).view(1, 2, 2, 2)
self.assertEqual(output, groundtruth)
for align_corners in (True, False):
# do gradcheck
N = random.randint(1, 8)
C = random.randint(1, 8)
H = random.randint(1, 8)
W = random.randint(1, 8)
sz = torch.Size([N, C, H, W])
inp = torch.randn(N, 2, 3, requires_grad=True)
with warnings.catch_warnings(record=True):
warnings.simplefilter("always") # python2 requires this so other tests can trigger
self.assertTrue(gradcheck(
lambda inp: F.affine_grid(inp, sz, align_corners=align_corners),
(inp,)))
# test CPU against CUDA
if TEST_CUDA:
N = random.randint(1, 8)
C = random.randint(1, 8)
H = random.randint(1, 8)
W = random.randint(1, 8)
sz = torch.Size([N, C, H, W])
for align_corners in (True, False):
input_cpu = torch.randn(N, 2, 3, requires_grad=True)
with warnings.catch_warnings(record=True):
warnings.simplefilter("always") # python2 requires this so other tests can trigger
out_cpu = F.affine_grid(input_cpu, sz, align_corners=align_corners)
gradients = torch.randn(out_cpu.size())
out_cpu.backward(gradients)
input_gpu = input_cpu.detach().cuda().requires_grad_()
with warnings.catch_warnings(record=True):
warnings.simplefilter("always") # python2 requires this so other tests can trigger
out_cuda = F.affine_grid(input_gpu, sz, align_corners=align_corners)
out_cuda.backward(gradients.cuda())
self.assertEqual(out_cpu, out_cuda)
self.assertEqual(input_cpu.grad, input_gpu.grad)
@set_default_dtype(torch.double)
def test_affine_grid_3d(self):
# test known input on CPU
input = torch.arange(1., 13).view(1, 3, 4)
output = F.affine_grid(input, torch.Size([1, 1, 2, 2, 2]), align_corners=True)
groundtruth = torch.tensor(
[[[[[-2., -10., -18.], [0., 0., 0.]], [[2., 2., 2.], [4., 12., 20.]]],
[[[4., 4., 4.], [6., 14., 22.]], [[8., 16., 24.], [10., 26., 42.]]]]]).view(1, 2, 2, 2, 3)
self.assertEqual(output, groundtruth)
output = F.affine_grid(input, torch.Size([1, 1, 2, 2, 2]), align_corners=False)
groundtruth = torch.tensor(
[[[[[1., -1., -3.], [2., 4., 6.]], [[3., 5., 7.], [4., 10., 16.]]],
[[[4., 6., 8.], [5., 11., 17.]], [[6., 12., 18.], [7., 17., 27.]]]]]).view(1, 2, 2, 2, 3)
self.assertEqual(output, groundtruth)
for align_corners in (True, False):
# do gradcheck
N = random.randint(1, 8)
C = random.randint(1, 8)
D = random.randint(1, 8)
H = random.randint(1, 8)
W = random.randint(1, 8)
sz = torch.Size([N, C, D, H, W])
inp = torch.randn(N, 3, 4, requires_grad=True)
with warnings.catch_warnings(record=True):
warnings.simplefilter("always") # python2 requires this so other tests can trigger
self.assertTrue(gradcheck(
lambda inp: F.affine_grid(inp, sz, align_corners=align_corners),
(inp,)))
# test CPU against CUDA
if TEST_CUDA:
N = random.randint(1, 8)
C = random.randint(1, 8)
D = random.randint(1, 8)
H = random.randint(1, 8)
W = random.randint(1, 8)
sz = torch.Size([N, C, D, H, W])
for align_corners in (True, False):
input_cpu = torch.randn(N, 3, 4, requires_grad=True)
with warnings.catch_warnings(record=True):
warnings.simplefilter("always") # python2 requires this so other tests can trigger
out_cpu = F.affine_grid(input_cpu, sz, align_corners=align_corners)
gradients = torch.randn(out_cpu.size())
out_cpu.backward(gradients)
input_gpu = input_cpu.detach().cuda().requires_grad_()
with warnings.catch_warnings(record=True):
warnings.simplefilter("always") # python2 requires this so other tests can trigger
out_cuda = F.affine_grid(input_gpu, sz, align_corners=align_corners)
out_cuda.backward(gradients.cuda())
self.assertEqual(out_cpu, out_cuda)
self.assertEqual(input_cpu.grad, input_gpu.grad)
def test_channel_shuffle_return_alias_of_self(self):
# gh-76616: nn.ChannelShuffle will return alias of self with an empty input tensor
groups = 3
input_tensor = torch.rand([0, 9, 4, 4])
output = torch.nn.ChannelShuffle(groups)(input_tensor)
torch.testing.assert_close(output, input_tensor)
@set_default_dtype(torch.double)
def test_upsamplingLinear1d(self):
for align_corners in [True, False]:
for recompute_scale_factor in [True, False]:
kwargs = dict(
mode='linear', align_corners=align_corners, recompute_scale_factor=recompute_scale_factor
)
# test float scale factor up & downsampling
for scale_factor in [0.5, 1.5, 2]:
m = nn.Upsample(scale_factor=scale_factor, **kwargs)
in_t = torch.ones(1, 1, 2)
out_size = int(math.floor(in_t.shape[-1] * scale_factor))
with warnings.catch_warnings(record=True) as w:
out_t = m(in_t)
self.assertEqual(torch.ones(1, 1, out_size), out_t.data)
input = torch.randn(1, 1, 2, requires_grad=True)
if not recompute_scale_factor:
gradcheck(lambda x: F.interpolate(x, out_size, **kwargs), (input,))
else:
gradcheck(lambda x: F.interpolate(x, scale_factor=scale_factor, **kwargs), (input,))
def test_upsamplingLinear1d_spatial_invariance(self):
m = nn.Upsample(scale_factor=3, mode='linear', align_corners=False)
in_t_9 = torch.zeros(1, 1, 9)
in_t_9[:, :, :4].normal_()
with warnings.catch_warnings(record=True) as w:
out_t_9 = m(in_t_9)
out_t_5 = m(in_t_9[:, :, :5])
self.assertEqual(out_t_9[:, :, :15], out_t_5)
@set_default_dtype(torch.double)
def test_upsampling_not_recompute_scale_factor(self):
# test output against known input: result must match opencv
in_t = torch.arange(8.).view(1, 2, 2, 2)
expected_out_t = torch.tensor(
[[[[-0.32725, -0.08843, 0.37933, 0.79744],
[0.15039, 0.38921, 0.85697, 1.27508],
[1.08591, 1.32473, 1.79249, 2.21060],
[1.92213, 2.16095, 2.62871, 3.04682]],
[[3.67275, 3.91157, 4.37933, 4.79744],
[4.15039, 4.38921, 4.85697, 5.27508],
[5.08591, 5.32473, 5.79249, 6.21060],
[5.92213, 6.16095, 6.62871, 7.04682]]]])
if IS_PPC:
# Both OpenCV and PyTorch give a slightly different result on PPC
expected_out_t = torch.tensor(
[[[[-0.32725, -0.08843, 0.37933, 0.79744],
[0.15039, 0.38921, 0.85697, 1.27508],
[1.08591, 1.32473, 1.79249, 2.21060],
[1.92212, 2.16094, 2.62870, 3.04681]],
[[3.67275, 3.91157, 4.37933, 4.79743],
[4.15039, 4.38921, 4.85697, 5.27508],
[5.08591, 5.32473, 5.79249, 6.21059],
[5.92212, 6.16094, 6.62870, 7.04680]]]])
out_t = F.interpolate(in_t, scale_factor=2.3, mode='bicubic', align_corners=False, recompute_scale_factor=False)
torch.set_printoptions(precision=5)
self.assertEqual(out_t, expected_out_t, atol=1e-4, rtol=0)
device_list = ['cpu']
if TEST_CUDA:
device_list.append('cuda')
for align_corners in [True, False]:
kwargs = dict(mode='bicubic', align_corners=align_corners)
# test float scale factor up & downsampling
for device in device_list:
for scale_factor in [0.6, 1.6, 2.3]:
in_t = torch.ones(2, 2, 2, 2).to(device)
out_t = F.interpolate(in_t, scale_factor=scale_factor, **kwargs)
out_size = int(math.floor(in_t.shape[-1] * scale_factor))
self.assertEqual(torch.ones(2, 2, out_size, out_size), out_t.data, atol=1e-5, rtol=0)
input = torch.randn(2, 2, 2, 2, requires_grad=True)
gradcheck(lambda x: F.interpolate(x, out_size, **kwargs), [input])
def test_upsamplingBilinear2d_spatial_invariance(self):
m = nn.Upsample(scale_factor=3, mode='bilinear', align_corners=False)
in_t_9 = torch.zeros(1, 1, 9, 9)
in_t_9[:, :, :4, :4].normal_()
with warnings.catch_warnings(record=True) as w:
out_t_9 = m(in_t_9)
out_t_5 = m(in_t_9[:, :, :5, :5])
self.assertEqual(out_t_9[:, :, :15, :15], out_t_5)
def test_upsamplingTrilinear3d_spatial_invariance(self):
m = nn.Upsample(scale_factor=3, mode='trilinear', align_corners=False)
in_t_9 = torch.zeros(1, 1, 9, 9, 9)
in_t_9[:, :, :4, :4, :4].normal_()
with warnings.catch_warnings(record=True) as w:
out_t_9 = m(in_t_9)
out_t_5 = m(in_t_9[:, :, :5, :5, :5])
self.assertEqual(out_t_9[:, :, :15, :15, :15], out_t_5)
def test_upsampling_small_scale(self):
m = torch.nn.Upsample(scale_factor=0.5, mode="bilinear")
in_t = torch.arange(1, 5, dtype=torch.get_default_dtype()).reshape(1, 1, 2, 2)
out_t = m(in_t)
expected_out_t = torch.tensor([[[[2.5]]]])
self.assertEqual(expected_out_t, out_t)
def test_upsampling_bfloat16(self, dtype=torch.bfloat16):
def helper(size, scale_factor, mode, device, memory_format=torch.contiguous_format):
input = torch.randn(size, device=device, dtype=dtype).to(memory_format=memory_format).detach().requires_grad_(True)
inputf = input.to(torch.float32).to(memory_format=torch.contiguous_format).detach().requires_grad_(True)
m = nn.Upsample(scale_factor=scale_factor, mode=mode)
outf = m(inputf)
out = m(input)
self.assertEqual(out.to(torch.float32), outf, atol=0.05, rtol=0)
ginput = torch.randn(out.shape, device=device, dtype=dtype).to(memory_format=memory_format)
ginputf = ginput.to(torch.float32).to(memory_format=torch.contiguous_format)
out.backward(ginput)
outf.backward(ginputf)
self.assertEqual(input.grad.to(torch.float32), inputf.grad, atol=0.01, rtol=0.01)
for device in ['cpu']:
helper([3, 20, 11, 7], 2, 'nearest', device)
helper([3, 20, 11, 7], 2, 'nearest', device, torch.channels_last)
helper([3, 20, 11, 7, 3], 2, 'nearest', device)
helper([3, 20, 30], 2, 'linear', device)
helper([3, 20, 11, 7], 2, 'bilinear', device)
helper([3, 20, 11, 7], 2, 'bilinear', device, torch.channels_last)
helper([1, 3, 11, 7], 2, 'bicubic', device)
helper([1, 3, 11, 7], 2, 'bicubic', device, torch.channels_last)
helper([3, 20, 11, 7, 3], 2, 'trilinear', device)
helper([3, 5, 5], 257., 'nearest', device)
helper([3, 20, 11, 7], 20, 'nearest', device)
helper([3, 20, 11, 7, 3], 20, 'nearest', device)
helper([1, 2, 11, 7], 257, 'nearest', device, torch.channels_last)
helper([1, 2, 2000, 2000], 1 / 377., 'nearest', device)
helper([1, 2, 2000, 2000], 1 / 257., 'nearest', device, torch.channels_last)
helper([3, 2, 11, 7, 3], 20, 'nearest', device, torch.channels_last_3d)
helper([3, 5, 5], 10, 'linear', device)
helper([3, 5, 5], 257, 'linear', device)
helper([1, 2, 11, 7], 257, 'bilinear', device)
helper([1, 2, 11, 7], 257, 'bilinear', device, torch.channels_last)
helper([1, 3, 11, 7], 10, 'bicubic', device)
helper([1, 3, 11, 7], 10, 'bicubic', device, torch.channels_last)
helper([1, 1, 11, 7], 257, 'bicubic', device)
helper([3, 2, 11, 7, 3], 20, 'trilinear', device)
helper([3, 2, 11, 7, 3], 20, 'trilinear', device, torch.channels_last_3d)
@unittest.skipIf(not TEST_CUDA, "CUDA unavailable")
def test_interpolate_illegal_memory_access(self):
in_s = 45
out_s = 14
input = torch.ones((1, 1, in_s), device='cuda', requires_grad=True)
# note we allocated grad_output to be larger so out of bound access
# would be visible in grad_input
grad = torch.ones((1, 1, out_s * 2), device='cuda', requires_grad=True)
grad = grad[:, :, :out_s]
input_ref = input.detach().cpu().requires_grad_()
grad_ref = grad.cpu()
out = F.interpolate(input, size=(out_s,), mode='nearest')
out.backward(grad)
out_ref = F.interpolate(input_ref, size=(out_s,), mode='nearest')
out_ref.backward(grad_ref)
self.assertEqual(out_ref, out)
self.assertEqual(input_ref.grad, input.grad)
def test_interpolate_undefined_behavior_casting(self):
x = torch.ones([1, 1, 16, 16])
self.assertRaises(RuntimeError, lambda: F.interpolate(x, scale_factor=-1e20, mode="bilinear"))
self.assertRaises(RuntimeError, lambda: F.interpolate(x, scale_factor=1e20, mode="bilinear"))
def test_interpolate_buffer_overflow(self):
# Test buffer overflow issue due to inaccurate floating point
# representation for integer values. See issue below for details.
# https://github.com/pytorch/pytorch/issues/88939
def helper(size, dtype, mode, device, is_channels_last):
input = torch.ones(size, dtype=dtype, device=device)
if is_channels_last:
if len(size) == 3:
input = input.transpose(1, 2).contiguous().transpose(1, 2)
elif len(size) == 4:
input = input.to(memory_format=torch.channels_last)
else:
input = input.to(memory_format=torch.channels_last_3d)
output1 = F.interpolate(input, 2, mode=mode, align_corners=True)
# reset the corner value and expect the output is changed as well
# the output won't be changed on buffer overflow
input[(-1,) * len(size)] = 0.5
output2 = F.interpolate(input, 2, mode=mode, align_corners=True)
self.assertNotEqual(output1, output2)
size_dtype_list = []
# We set the size larger than the floating point exactly representable range
# float: exact representable range (-2**24,2**24)
size_dtype_list.append(([1, 10, 2**24 + 4], torch.float))
size_dtype_list.append(([1, 10, 2, 2**24 + 4], torch.float))
size_dtype_list.append(([1, 10, 2, 2, 2**24 + 4], torch.float))
# bfloat16: exact representable range (-2**8, 2**8)
size_dtype_list.append(([1, 10, 2**8 + 4], torch.bfloat16))
size_dtype_list.append(([1, 10, 2, 2**8 + 4], torch.bfloat16))
size_dtype_list.append(([1, 10, 2, 2, 2**8 + 4], torch.bfloat16))
# half: exact representable range (-2**11, 2**11)
size_dtype_list.append(([1, 10, 2**11 + 4], torch.half))
size_dtype_list.append(([1, 10, 2, 2**11 + 4], torch.half))
size_dtype_list.append(([1, 10, 2, 2, 2**11 + 4], torch.half))
# TODO: turn on cuda test after buffer overflow issue is fixed in cuda kernel
# devices = ['cpu'] + (['cuda'] if torch.cuda.is_available() else [])
devices = ['cpu']
for mode in ('linear', 'bilinear', 'bicubic', 'trilinear'):
for size_dtype in size_dtype_list:
size, dtype = size_dtype
if (
mode == 'linear' and len(size) != 3
or (mode == 'bilinear' and len(size) != 4)
or (mode == 'bicubic' and len(size) != 4)
or (mode == 'trilinear' and len(size) != 5)
):
continue
for device in devices:
if (
device == 'cpu' and dtype == torch.half
or (device == 'cuda' and dtype == torch.bfloat16)
):
# no half precision support on cpu or bfloat16 on cuda yet
continue
for is_channels_last in (True, False):
helper(size, dtype, mode, device, is_channels_last)
@set_default_dtype(torch.double)
def test_interpolate(self):
def _test_interpolate_non_integer_size_warning(in_t, out_size, dim, **kwargs):
test_sizes = [float(out_size),
torch.tensor(out_size, dtype=torch.float)]
for size in test_sizes:
self.assertRaisesRegex(TypeError,
"(expected size to be one of int or).*",
F.interpolate, in_t, size=(size,) * dim, **kwargs)
def _test_interpolate_helper(in_t, scale_factor, layer):
out_size = int(math.floor(in_t.shape[-1] * scale_factor))
dim = len(in_t.shape) - 2
out_shape = [1, 1] + [out_size] * dim
with warnings.catch_warnings(record=True) as w:
out_t = layer(in_t)
self.assertEqual(torch.ones(out_shape), out_t)
self.assertEqual(
F.interpolate(in_t, (out_size,) * dim, **kwargs),
F.interpolate(in_t, scale_factor=scale_factor, **kwargs))
gradcheck(lambda x: F.interpolate(x, out_size, **kwargs), [in_t], nondet_tol=GRADCHECK_NONDET_TOL)
gradgradcheck(lambda x: F.interpolate(x, out_size, **kwargs), [in_t], nondet_tol=GRADCHECK_NONDET_TOL)
_test_interpolate_non_integer_size_warning(in_t, out_size, dim, **kwargs)
def _make_input(dim, device):
size = [1, 1]
size += [2] * dim
return torch.ones(size, requires_grad=True, device=device)
device_list = ['cpu']
if TEST_CUDA:
device_list.append('cuda')
for device in device_list:
for scale_factor in [0.5, 1.5, 2]:
for mode in ['nearest', 'area']:
kwargs = dict(mode=mode)
m = nn.Upsample(scale_factor=scale_factor, **kwargs).to(device)
for input in [_make_input(1, device), _make_input(2, device), _make_input(3, device)]:
_test_interpolate_helper(input, scale_factor, m)
for align_corners in [True, False]:
kwargs = dict(mode='linear', align_corners=align_corners)
m = nn.Upsample(scale_factor=scale_factor, **kwargs).to(device)
_test_interpolate_helper(_make_input(1, device), scale_factor, m)
kwargs = dict(mode='bilinear', align_corners=align_corners)
m = nn.Upsample(scale_factor=scale_factor, **kwargs).to(device)
_test_interpolate_helper(_make_input(2, device), scale_factor, m)
kwargs = dict(mode='bicubic', align_corners=align_corners)
def m(t):
return F.interpolate(t, scale_factor=scale_factor, **kwargs).to(device)
_test_interpolate_helper(_make_input(2, device), scale_factor, m)
kwargs = dict(mode='trilinear', align_corners=align_corners)
m = nn.Upsample(scale_factor=scale_factor, **kwargs).to(device)
_test_interpolate_helper(_make_input(3, device), scale_factor, m)
def test_linear_broadcasting(self):
m = nn.Linear(5, 8)
inp = torch.randn(2, 3, 5)
expected = m(inp.view(6, 5)).view(2, 3, 8)
self.assertEqual(expected, m(inp))
@parametrize_test('device', ['cpu'] + (['cuda'] if TEST_CUDA else []))
@parametrize_test('bias', [
subtest(False, name='nobias'), subtest(True, name='bias')])
@parametrize_test('weight_layout', [
subtest(torch.strided, name='weightStrided'),
subtest(torch.sparse_coo, name='weightCOO'),
subtest(torch.sparse_csr, name='weightCSR'),
subtest(torch.sparse_csc, name='weightCSC'),
# TODO: addmm: computation on CPU is not implemented for Strided + Strided @ SparseBsr
# subtest(torch.sparse_bsr, name='weightBSR'),
# subtest(torch.sparse_bsc, name='weightBSC'),
])
def test_linear_autograd(self, device, bias, weight_layout):
module = nn.Linear(4, 4, bias=bias, device=device)
if weight_layout == torch.strided:
pass
elif weight_layout == torch.sparse_csr:
module.weight = nn.Parameter(module.weight.to_sparse_csr())
elif weight_layout == torch.sparse_csc:
module.weight = nn.Parameter(module.weight.to_sparse_csc())
elif weight_layout == torch.sparse_bsr:
module.weight = nn.Parameter(module.weight.to_sparse_bsr((2, 2)))
elif weight_layout == torch.sparse_bsc:
module.weight = nn.Parameter(module.weight.to_sparse_bsc((2, 2)))
elif weight_layout == torch.sparse_coo:
module.weight = nn.Parameter(module.weight.to_sparse_coo())
else:
assert(0)
inp = torch.randn(4, requires_grad=True, device=device)
res = module(inp)
if bias:
expected = (torch.einsum("i,ji->j", inp, module.weight.to_dense())) + module.bias
else:
expected = (torch.einsum("i,ji->j", inp, module.weight.to_dense()))
self.assertEqual(res, expected)
grad_output = torch.randn(4, device=device)
grads = torch.autograd.grad(res, [module.weight, inp], grad_output)
grads_expected = torch.autograd.grad(expected, [module.weight, inp], grad_output)
self.assertEqual(grads_expected[0].layout, weight_layout)
for g, ge in zip(grads, grads_expected):
self.assertEqual(g, ge)
def test_bilinear(self):
module = nn.Bilinear(10, 10, 8)
input1 = torch.randn(4, 10, requires_grad=True)
input2 = torch.randn(4, 10, requires_grad=True)
grad_output = torch.randn(4, 8)
res = module(input1, input2)
expected = (torch.einsum("bi,kij,bj->bk", input1, module.weight, input2) +
module.bias)
self.assertEqual(res, expected)
grads = torch.autograd.grad(res, [module.weight, module.bias, input1, input2], grad_output)
grads_expected = torch.autograd.grad(expected, [module.weight, module.bias, input1, input2], grad_output)
for g, ge in zip(grads, grads_expected):
self.assertEqual(g, ge)
def test_bilinear_non_contiguous(self):
module = nn.Bilinear(7, 7, 5)
input1 = torch.randn(4, 7, 10, requires_grad=True)
input2 = torch.randn(4, 7, 10, requires_grad=True)
input1_tp = input1.transpose(1, 2)
input2_tp = input2.transpose(1, 2)
grad_output = torch.randn(4, 10, 5)
def run(input1_tp, input2_tp):
input1.grad = input2.grad = None
output = module(input1_tp, input2_tp)
output.backward(grad_output)
return output.data, input1.grad.data, input2.grad.data
out_nc, g1_nc, g2_nc = run(input1_tp, input2_tp)
input1_tp = input1_tp.contiguous()
input2_tp = input2_tp.contiguous()
out, g1, g2 = run(input1_tp, input2_tp)
self.assertEqual(out, out_nc)
self.assertEqual(g1, g1_nc)
self.assertEqual(g2, g2_nc)
def test_bilinear_no_bias(self):
module = nn.Bilinear(10, 10, 8, dtype=torch.double)
module_no_bias = nn.Bilinear(10, 10, 8, False, dtype=torch.double)
module.bias.data.zero_()
module.weight.data.copy_(module_no_bias.weight)
input1 = torch.randn(4, 10, requires_grad=True, dtype=torch.double)
input2 = torch.randn(4, 10, requires_grad=True, dtype=torch.double)
grad_output = torch.randn(4, 8, dtype=torch.double)
def run(net):
input1.grad = input2.grad = None
output = net(input1, input2)
output.backward(grad_output)
return output.data, input1.grad.data, input2.grad.data
out, g1, g2 = run(module)
out_nb, g1_nb, g2_nb = run(module_no_bias)
self.assertEqual(out, out_nb)
self.assertEqual(g1, g1_nb)
self.assertEqual(g2, g2_nb)
_assertGradAndGradgradChecks(self,
lambda x1, x2: F.bilinear(x1, x2, module_no_bias.weight, module_no_bias.bias),
(input1, input2))
def test_bilinear_broadcasting(self):
m = nn.Bilinear(5, 6, 8)
input1 = torch.randn(2, 3, 5)
input2 = torch.randn(2, 3, 6)
expected = m(input1.view(6, 5), input2.view(6, 6)).view(2, 3, 8)
self.assertEqual(expected, m(input1, input2))
def test_fold_invalid_arg(self):
# input.size(1) not divisible by \prod(kernel_size)
fold = nn.Fold(output_size=(4, 5), kernel_size=(2, 3))
with self.assertRaisesRegex(RuntimeError, r"be divisible by the product of kernel_size"):
fold(torch.randn(1, 5, 9))
with self.assertRaisesRegex(RuntimeError, r"be divisible by the product of kernel_size"):
fold(torch.randn(1, 19, 9))
# input.size(2) not matching the total number of sliding blocks
with self.assertRaisesRegex(RuntimeError, r"match the calculated number of sliding blocks"):
fold = nn.Fold(output_size=(4, 5), kernel_size=(2, 3))
fold(torch.randn(1, 6, 10))
with self.assertRaisesRegex(RuntimeError, r"match the calculated number of sliding blocks"):
fold = nn.Fold(output_size=(4, 5), kernel_size=(2, 3), stride=(2, 2))
fold(torch.randn(1, 6, 5))
with self.assertRaisesRegex(RuntimeError, r"match the calculated number of sliding blocks"):
fold = nn.Fold(output_size=(4, 5), kernel_size=(2, 3), stride=(2, 2), dilation=(1, 2), padding=(2, 0))
fold(torch.randn(1, 6, 5)) # should be 4 * 1 = 4 sliding blocks
fold = nn.Fold(output_size=(4, 5), kernel_size=(2, 2), stride=1, dilation=8, padding=0)
with self.assertRaisesRegex(RuntimeError, r"calculated shape of the array of sliding blocks as"):
fold(torch.randn(1, 12, 12))
def test_unfold_invalid_arg(self):
# input wrong dimension
unfold = nn.Unfold(kernel_size=(2, 3))
# calculated output shape is too small
with self.assertRaisesRegex(RuntimeError, r"its components must be at least one"):
unfold = nn.Unfold(kernel_size=(2, 3))
unfold(torch.randn(1, 2, 2, 2))
with self.assertRaisesRegex(RuntimeError, r"its components must be at least one"):
unfold = nn.Unfold(kernel_size=(5, 3), padding=(1, 1))
unfold(torch.randn(1, 2, 2, 3))
with self.assertRaisesRegex(RuntimeError, r"its components must be at least one"):
unfold = nn.Unfold(kernel_size=(1, 3), padding=(1, 1), dilation=(1, 2))
unfold(torch.randn(1, 2, 2, 2))
def test_softmin(self):
x = torch.randn(2, 16)
self.assertEqual(F.softmin(x, 1), F.softmax(-x, 1))
self.assertEqual(F.softmin(x, 0), F.softmax(-x, 0))
def test_log_softmax_cpu(self, dtype=torch.bfloat16):
for dim in [0, 1]:
inputf = torch.rand(200, 200, device="cpu", dtype=torch.float, requires_grad=True)
input = inputf.to(dtype).detach().requires_grad_(True)
outf = F.log_softmax(inputf, dim=dim)
out = F.log_softmax(input, dim=dim)
self.assertEqual(out, outf.to(dtype=dtype), atol=0.1, rtol=0)
out.sum().backward()
outf.sum().backward()
self.assertEqual(input.grad, inputf.grad.to(dtype), atol=0.1, rtol=0)
def test_softmax_cpu(self, dtype=torch.bfloat16):
for dim in [0, 1]:
inputf = torch.rand(200, 200, device="cpu", dtype=torch.float, requires_grad=True)
input = inputf.to(dtype).detach().requires_grad_(True)
outf = F.softmax(inputf, dim=dim)
out = F.softmax(input, dim=dim)
self.assertEqual(out, outf.to(dtype), atol=1e-3, rtol=0)
out.sum().backward()
outf.sum().backward()
self.assertEqual(input.grad, inputf.grad.to(dtype), atol=1e-3, rtol=0)
def test_adaptive_log_softmax(self):
# args validation
with self.assertRaises(ValueError):
_ = nn.AdaptiveLogSoftmaxWithLoss(16, 20, [5, 15, 15], div_value=2.)
with self.assertRaises(ValueError):
_ = nn.AdaptiveLogSoftmaxWithLoss(16, 20, [5, 15, 10], div_value=2.)
with self.assertRaises(ValueError):
_ = nn.AdaptiveLogSoftmaxWithLoss(16, 20, [5, 10, 25], div_value=2.)
with self.assertRaisesRegex(ValueError, "cutoffs should be a sequence of unique,"):
_ = nn.AdaptiveLogSoftmaxWithLoss(16, 20, [5, 10, 20], div_value=2.)
# not raise
_ = nn.AdaptiveLogSoftmaxWithLoss(16, 20, [5, 10, 19], div_value=2.)
# input shapes
with self.assertRaisesRegex(RuntimeError, r"Input and target should have the same size"):
asfm = nn.AdaptiveLogSoftmaxWithLoss(16, 20, [5, 10, 15], div_value=2.)
x = torch.randn(2, 16)
y = torch.tensor([0, 5, 10])
asfm(x, y)
# out-of-bound targets
with self.assertRaisesRegex(RuntimeError, r"Target values should be in"):
asfm = nn.AdaptiveLogSoftmaxWithLoss(16, 20, [5, 10, 15], div_value=2.)
x = torch.randn(2, 16)
y = torch.tensor([0, 20])
asfm(x, y)
# cluster sizes
asfm = nn.AdaptiveLogSoftmaxWithLoss(16, 20, [5, 10, 15], div_value=2.)
x = torch.randn(2, 16)
y = torch.tensor([0, 17])
self.assertEqual(asfm.head.weight.size(), (5 + 3, 16)) # 5 targets in head, 3 clusters, dimensionality 16
self.assertEqual(asfm.tail[0][1].weight.size(), (5, 8)) # 5 targets in this cluster, dimensionality 8
self.assertEqual(asfm.tail[1][1].weight.size(), (5, 4))
self.assertEqual(asfm.tail[2][1].weight.size(), (5, 2))
self.assertEqual(asfm(x, y).output.size(), (2, ))
# test no_batch_dim support
asfm = nn.AdaptiveLogSoftmaxWithLoss(16, 20, [5, 10, 15], div_value=2.)
x = torch.randn(1, 16)
y = torch.tensor([17])
x2 = x.squeeze(0)
y2 = y.squeeze(0)
self.assertEqual(asfm(x, y).output.squeeze(0), asfm(x2, y2).output)
# log_probs actually returns log_proba
asfm = nn.AdaptiveLogSoftmaxWithLoss(8, 4, [2], div_value=2.)
x = torch.randn(4, 8)
logprob_out = asfm.log_prob(x)
self.assertEqual(torch.exp(logprob_out).data.sum(1), torch.ones(4))
# forward returns the same thing as log_probs
for v in [0, 1, 2, 3]:
y = torch.full((4,), v, dtype=torch.long)
out, loss = asfm(x, y)
self.assertEqual(out, logprob_out.gather(1, y.unsqueeze(1)).squeeze())
self.assertEqual(loss, F.nll_loss(logprob_out, y))
# predict
x = torch.randn(64, 8).abs_()
# argmax in shortlist
asfm = nn.AdaptiveLogSoftmaxWithLoss(8, 10, [4, 8], div_value=2., head_bias=True)
asfm.head.weight.data.abs_()
asfm.head.bias.data.abs_()
asfm.head.weight.data[asfm.shortlist_size:, :].zero_()
out = asfm.predict(x)
self.assertEqual(out, asfm.log_prob(x).argmax(dim=1))
# argmax outside of shortlist
asfm = nn.AdaptiveLogSoftmaxWithLoss(8, 10, [4, 8], div_value=2., head_bias=True)
asfm.head.weight.data.abs_()
asfm.head.bias.data.abs_()
asfm.head.weight.data[:asfm.shortlist_size, :].zero_()
out = asfm.predict(x)
self.assertEqual(out, asfm.log_prob(x).argmax(dim=1))
# half of the argmax in shortlist, half in clusters
asfm = nn.AdaptiveLogSoftmaxWithLoss(8, 10, [4, 8], div_value=2., head_bias=True)
asfm.head.weight.data.abs_()
asfm.head.bias.data.abs_()
x[:32, :asfm.shortlist_size].zero_()
x[32:, asfm.shortlist_size:].zero_()
asfm.head.weight.data[:asfm.shortlist_size, asfm.shortlist_size:].zero_()
asfm.head.weight.data[asfm.shortlist_size:, :asfm.shortlist_size].zero_()
out = asfm.predict(x)
self.assertEqual(out, asfm.log_prob(x).argmax(dim=1))
def test_cross_entropy_loss(self, dtype=torch.bfloat16):
loss_cpu = nn.CrossEntropyLoss().cpu()
inputf = torch.randn(15, 10, device="cpu", dtype=torch.float, requires_grad=True)
input = inputf.to(dtype).detach().requires_grad_(True)
target = torch.empty(15, dtype=torch.long).random_(10)
outf = loss_cpu(inputf, target)
out = loss_cpu(input, target)
self.assertEqual(out, outf.to(dtype=dtype), atol=1e-1, rtol=0)
outf.backward()
out.backward()
self.assertEqual(input.grad, inputf.grad.to(dtype=dtype), atol=1e-1, rtol=0)
def test_cross_entropy_loss_precision(self):
# Regression test for #55657
loss_cpu = nn.CrossEntropyLoss().cpu()
inputf = torch.randn(128, 2, 768, 768, device="cpu", dtype=torch.float)
inputd = inputf.double()
target = torch.randint(2, (128, 768, 768), dtype=torch.long)
outf = loss_cpu(inputf, target)
outd = loss_cpu(inputd, target)
self.assertEqual(outf, outd, exact_dtype=False)
def test_cross_entropy_loss_zero_div(self):
# Test for issue #73165
input_1 = torch.rand([5, 0], dtype=torch.float32)
input_2 = torch.rand([5, 0], dtype=torch.float32)
torch.nn.CrossEntropyLoss()(input_1, input_2)
@unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
def test_convert_sync_batchnorm(self):
module = torch.nn.Sequential(
torch.nn.BatchNorm1d(100),
torch.nn.InstanceNorm1d(100)
).cuda()
# necessary to have an anchor point for comparison, in case the
# convert_sync_batchnorm updates in place
comp_module = torch.nn.Sequential(
torch.nn.BatchNorm1d(100),
torch.nn.InstanceNorm1d(100)
).cuda()
comp_module.load_state_dict(module.state_dict())
sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module)
children = list(sync_bn_module.children())
self.assertEqual(children[0].__class__, torch.nn.SyncBatchNorm)
self.assertEqual(children[1].__class__, torch.nn.InstanceNorm1d)
for layer, converted_layer in zip(comp_module.children(), sync_bn_module.children()):
for key in layer.state_dict().keys():
self.assertEqual(layer.state_dict()[key].device, converted_layer.state_dict()[key].device)
self.assertEqual(layer.state_dict()[key], converted_layer.state_dict()[key])
@unittest.skipIf(not TEST_CUDA, "CUDA not available")
def test_sync_batchnorm_backward_elemt(self):
device = 'cuda'
saved_input = torch.rand(2, 3, 2, 1, device=device)
grad_output = torch.rand(2, 3, 2, 1, device=device)
mean = torch.rand(3, device=device)
invstd = torch.rand(3, device=device)
weight = torch.rand(3, device=device)
sum_dy = torch.rand(3, device=device)
sum_dy_xmu = torch.rand(3, device=device)
count_tensor = torch.tensor([5, 5, 5], dtype=torch.int32, device=device)
gI_contiguous = torch.batch_norm_backward_elemt(
grad_output,
saved_input,
mean,
invstd,
weight,
sum_dy,
sum_dy_xmu,
count_tensor
)
# Test batch_norm_backward_elemt gives the same answer for all
# combinations of contiguous as channels_last input
for a, b in [
(torch.channels_last, torch.contiguous_format),
(torch.contiguous_format, torch.channels_last),
(torch.channels_last, torch.channels_last),
]:
gI_actual = torch.batch_norm_backward_elemt(
grad_output.contiguous(memory_format=a),
saved_input.contiguous(memory_format=b),
mean,
invstd,
weight,
sum_dy,
sum_dy_xmu,
count_tensor
)
self.assertEqual(gI_actual, gI_contiguous)
@unittest.skipIf(not TEST_CUDA, "CUDA not available")
def test_sync_batchnorm_accuracy_cuda(self):
# The target of this test is to test the functionality and accuracy of
# those single-GPU cuda kernels used in SyncBatchNorm
# They are:
# fwd: torch.batch_norm_stats, torch.batch_norm_gather_stats_with_counts, torch.batch_norm_elemt
# bwd: torch.batch_norm_backward_reduce, torch.batch_norm_backward_elemt
def _batch_norm_stats(data, memory_format, mean_axes):
mean1, _ = torch.batch_norm_stats(data, 1e-5)
mean2, _ = torch.batch_norm_stats(data.to(memory_format=memory_format), 1e-5)
mean_ref = torch.mean(data, mean_axes, keepdim=False)
self.assertEqual(mean_ref, mean1)
self.assertEqual(mean_ref, mean2)
_batch_norm_stats(torch.randn(1, 96, 112, 112, dtype=torch.float, device='cuda'), torch.channels_last, (0, 2, 3))
_batch_norm_stats(torch.randn(1, 96, 112, 112, 112, dtype=torch.float, device='cuda'), torch.channels_last_3d, (0, 2, 3, 4))
def test_flatten(self):
tensor_input = torch.randn(2, 1, 2, 3)
# Flatten Tensor
flatten = nn.Flatten(start_dim=1, end_dim=-1)
tensor_output = flatten(tensor_input)
self.assertEqual(tensor_output.size(), torch.Size([2, 6]))
def test_unflatten(self):
tensor_input = torch.randn(2, 50)
# Unflatten Tensor (unflattened_size as a tuple of ints and list of ints)
for us in ((2, 5, 5), [2, 5, 5]):
unflatten = nn.Unflatten(dim=1, unflattened_size=us)
tensor_output = unflatten(tensor_input)
self.assertEqual(tensor_output.size(), torch.Size([2, 2, 5, 5]))
# Unflatten NamedTensor
unflatten = nn.Unflatten(dim='features', unflattened_size=(('C', 2), ('H', 5), ('W', 5)))
named_tensor_input = tensor_input.refine_names('N', 'features')
named_tensor_output = unflatten(named_tensor_input)
self.assertEqual(named_tensor_output.size(), torch.Size([2, 2, 5, 5]))
def test_unflatten_invalid_arg(self):
# Wrong type for unflattened_size (tuple of floats)
with self.assertRaisesRegex(
TypeError,
r"unflattened_size must be tuple of ints, but found element of type float at pos 2"):
nn.Unflatten(dim=1, unflattened_size=(2, 5, 5.0))
# Wrong type for unflattened_size (list of lists and list of tuples)
for us in ([['C', 2], ['W', 5], ['H', 5]], [('C', 2), ('W', 5), ('H', 5)]):
with self.assertRaisesRegex(
TypeError,
r"unflattened_size must be a tuple of tuples, but found type list"):
nn.Unflatten(dim='features', unflattened_size=us)
# Wrong type for unflattened_size (tuple of lists)
with self.assertRaisesRegex(
TypeError,
r"unflattened_size must be tuple of tuples, but found element of type list at pos 0"):
nn.Unflatten(dim='features', unflattened_size=(['C', 2], ['W', 5], ['H', 5]))
# Wrong type for unflattened_size (tuple of dicts)
with self.assertRaisesRegex(
TypeError,
r"unflattened_size must be tuple of tuples, but found element of type dict at pos 0"):
nn.Unflatten(dim='features', unflattened_size=({'C': 2}, {'W': 5}, {'H': 5}))
def test_layer_norm_grads_with_create_graph_flag(self):
atol = 1e-5
rtol = 1e-3
x = torch.randn((4, 4, 16), requires_grad=True)
layer_norm = nn.LayerNorm((16,), 1e-5, True)
with torch.no_grad():
layer_norm.weight = torch.nn.Parameter(0.1 * torch.ones_like(layer_norm.weight))
grads1 = torch.autograd.grad(layer_norm(x).sum(), x, create_graph=False)[0]
grads2 = torch.autograd.grad(layer_norm(x).sum(), x, create_graph=True)[0]
self.assertEqual(grads1, grads2, rtol=rtol, atol=atol)
if TEST_CUDA:
x = x.to('cuda')
layer_norm = layer_norm.to('cuda')
grads1 = torch.autograd.grad(layer_norm(x).sum(), x, create_graph=False)[0]
grads2 = torch.autograd.grad(layer_norm(x).sum(), x, create_graph=True)[0]
self.assertEqual(grads1, grads2, rtol=rtol, atol=atol)
def test_padding_list(self):
# Padding can be a list, or tuple (regression test for gh-54452)
x = torch.randn(4, 8, 32, 32)
net = torch.nn.ConvTranspose2d(8, 16, kernel_size=3, padding=[3, 3])
y = net(x)
net = torch.nn.ConvTranspose2d(8, 16, kernel_size=3, padding=(3, 3))
y = net(x)
def test_fractional_max_pool2d_invalid_output_ratio(self):
arg_1 = [2, 1]
arg_2 = [0.5, 0.5, 0.6]
arg_class = torch.nn.FractionalMaxPool2d(kernel_size=arg_1, output_ratio=arg_2,)
arg_3_0_tensor = torch.rand([20, 16, 50, 32], dtype=torch.float32)
arg_3_0 = arg_3_0_tensor.clone()
arg_3 = [arg_3_0,]
with self.assertRaisesRegex(ValueError,
"fractional_max_pool2d requires output_ratio to either be a single Int or tuple of Ints."):
res = arg_class(*arg_3)
def test_max_pool1d_invalid_output_size(self):
arg_1 = 3
arg_2 = 255
arg_3 = False
arg_class = torch.nn.MaxPool1d(kernel_size=arg_1, stride=arg_2, return_indices=arg_3)
arg_4_0 = torch.as_tensor([[0.3204]])
arg_4 = [arg_4_0,]
with self.assertRaises(RuntimeError):
res = arg_class(*arg_4)
class TestFusionEval(TestCase):
@set_default_dtype(torch.double)
@given(X=hu.tensor(shapes=((5, 3, 5, 5),), dtype=np.double),
running_mean=hu.tensor(shapes=(6,), dtype=np.double),
running_var=hu.tensor(shapes=(6,), dtype=np.double))
def test_fuse_module_eval_numerics(self, X, running_mean, running_var):
inputs, _ = X
iC, oC = inputs.shape[1], len(running_mean[0])
inputs = torch.from_numpy(inputs)
kernel_size = (3, 3)
conv_ref = torch.nn.Conv2d(iC, oC, bias=True, kernel_size=kernel_size)
bn_ref = torch.nn.BatchNorm2d(oC)
bn_ref.running_mean = torch.from_numpy(running_mean[0])
bn_ref.running_var = torch.from_numpy(running_var[0])
conv_ref.eval()
bn_ref.eval()
Y_ref = bn_ref(conv_ref(inputs))
conv_bn_fused = torch.nn.utils.fusion.fuse_conv_bn_eval(conv_ref,
bn_ref)
Y_hat = conv_bn_fused(inputs)
self.assertEqual(Y_ref, Y_hat, msg="Conv+BN fusion results are off")
na_bn_ref = torch.nn.BatchNorm2d(oC, affine=False)
na_bn_ref.running_mean = torch.from_numpy(running_mean[0])
na_bn_ref.running_var = torch.from_numpy(running_var[0])
na_bn_ref.eval()
Y_ref = na_bn_ref(conv_ref(inputs))
conv_na_bn_fused = torch.nn.utils.fusion.fuse_conv_bn_eval(conv_ref,
na_bn_ref)
Y_hat = conv_na_bn_fused(inputs)
self.assertEqual(Y_ref, Y_hat, msg="Conv+BN(non-affine) fusion results are off")
class TestConstantPadNd(TestCase):
def test_constant_pad_nd(self):
a = torch.tensor([[1, 2], [3, 4]])
res = torch.constant_pad_nd(a, [1, 2, 1, 0], 9)
expected = torch.tensor([
[9, 9, 9, 9, 9],
[9, 1, 2, 9, 9],
[9, 3, 4, 9, 9]
])
self.assertEqual(res, expected)
def test_preserves_memory_format(self):
nchw_tensor = torch.rand((1, 2, 5, 3))
nchw_padded = torch.constant_pad_nd(nchw_tensor, [1, 2], 0.5)
self.assertTrue(nchw_padded.is_contiguous(memory_format=torch.contiguous_format))
nhwc_tensor = nchw_tensor.contiguous(memory_format=torch.channels_last)
nhwc_padded = torch.constant_pad_nd(nhwc_tensor, [1, 2], 0.5)
self.assertTrue(nhwc_padded.is_contiguous(memory_format=torch.channels_last))
class TestAddRelu(TestCase):
def test_add_relu(self):
a = torch.rand((7, 11))
b = torch.rand((7, 11))
a = a.float()
b = b.float()
a = a * -10
a = a + 5
add_res = a + b
relu_res = torch.relu(add_res)
add_relu_res = torch._VF._add_relu(a, b)
self.assertEqual(add_relu_res, relu_res)
def test_add_relu_broadcasting(self):
a = torch.rand((1, 32))
b = 1
b_scalar = torch.ones(1, 32)
res = torch._VF._add_relu(a, b)
broadcasted_res = torch._VF._add_relu(a, b_scalar)
self.assertEqual(broadcasted_res, res)
def add_test(test, decorator=None):
def add(test_name, fn):
if hasattr(TestNN, test_name):
raise RuntimeError('Found two tests with the same name: ' + test_name)
if decorator is not None:
fn = decorator(fn)
setattr(TestNN, test_name, fn)
test_name = test.get_name()
if not hasattr(test, 'test_cpu') or test.test_cpu:
add(test_name, lambda self, test=test: test(self))
cuda_test_name = test_name + '_cuda'
# With dtype enable, it's good enough to test against three floating types
kwargs = {}
if 'extra_args' in get_function_arglist(test.test_cuda):
kwargs['extra_args'] = test.extra_args
if 'dtype' in get_function_arglist(test.test_cuda):
if tf32_is_not_fp32() and test.with_tf32:
def with_tf32_off(self, test=test, kwargs=kwargs):
with tf32_off():
test.test_cuda(self, dtype=torch.float, **kwargs)
add(cuda_test_name + '_fp32', with_tf32_off)
def with_tf32_on(self, test=test, kwargs=kwargs):
with tf32_on(self, test.tf32_precision):
test.test_cuda(self, dtype=torch.float, **kwargs)
add(cuda_test_name + '_tf32', with_tf32_on)
else:
add(cuda_test_name + '_float', lambda self,
test=test, kwargs=kwargs: test.test_cuda(self, dtype=torch.float, **kwargs))
add(cuda_test_name + '_double', lambda self,
test=test, kwargs=kwargs: test.test_cuda(self, dtype=torch.double, **kwargs))
def test_half(self, test=test, kwargs=kwargs):
test.test_cuda(self, dtype=torch.half, **kwargs)
if getattr(test, 'check_half', True):
add(cuda_test_name + '_half', test_half)
def test_bfloat16(self, test=test, kwargs=kwargs):
test.test_cuda(self, dtype=torch.bfloat16, **kwargs)
if getattr(test, 'check_bfloat16', True):
add(cuda_test_name + '_bfloat16', test_bfloat16)
def test_cfloat(self, test=test, kwargs=kwargs):
test.test_cuda(self, dtype=torch.cfloat, **kwargs)
def test_cdouble(self, test=test, kwargs=kwargs):
test.test_cuda(self, dtype=torch.cdouble, **kwargs)
if getattr(test, 'check_complex', False):
add(cuda_test_name + '_cfloat', test_cfloat)
add(cuda_test_name + '_cdouble', test_cdouble)
else:
def with_tf32_off(self, test=test, kwargs=kwargs):
with tf32_off():
test.test_cuda(self, **kwargs)
if tf32_is_not_fp32() and test.with_tf32:
add(cuda_test_name + '_fp32', with_tf32_off)
def with_tf32_on(self, test=test, kwargs=kwargs):
with tf32_on(self, test.tf32_precision):
test.test_cuda(self, **kwargs)
add(cuda_test_name + '_tf32', with_tf32_on)
else:
add(cuda_test_name, with_tf32_off)
for test_params in module_tests + new_module_tests:
# TODO: CUDA is not implemented yet
if 'constructor' not in test_params:
name = test_params.pop('module_name')
test_params['constructor'] = getattr(nn, name)
decorator = test_params.pop('decorator', None)
test = NewModuleTest(**test_params)
add_test(test, decorator)
if 'check_eval' in test_params:
# create a new test that is identical but that sets module.training to False
desc = test_params.get('desc', None)
test_params['desc'] = 'eval' if desc is None else desc + '_eval'
def gen_eval_constructor(constructor):
def eval_constructor(*args, **kwargs):
cons = constructor(*args, **kwargs)
cons.training = False
return cons
eval_constructor.__name__ = constructor.__name__
return eval_constructor
test_params['constructor'] = gen_eval_constructor(test_params['constructor'])
test = NewModuleTest(**test_params)
add_test(test, decorator)
if 'check_with_long_tensor' in test_params:
fullname = test_params.get('fullname', None)
if fullname:
test_params['fullname'] = fullname + '_with_long_tensor'
else:
desc = test_params.get('desc', None)
test_params['desc'] = 'with_long_tensor' if desc is None else desc + '_with_long_tensor'
def double_equivalent_of_long_tensor(size):
return torch.randint(-1000, 1000, size=size).double()
def apply_to_cons(t):
if t.is_floating_point():
if isinstance(t, Parameter):
return Parameter(double_equivalent_of_long_tensor(t.size()))
elif isinstance(t, torch.Tensor):
return double_equivalent_of_long_tensor(t.size())
else:
return t
def gen_long_tensor_constructor(constructor):
def long_tensor_constructor(*args, **kwargs):
cons = constructor(*args, **kwargs)
cons._apply(apply_to_cons)
return cons
long_tensor_constructor.__name__ = constructor.__name__
return long_tensor_constructor
def gen_long_tensor_input(input_size):
def input_func():
return double_equivalent_of_long_tensor(input_size)
return input_func
def reference_fn(i, p, m):
# For bad reasons this would create LongTensors that requires gradients
# Remove requires_grad to avoid this
for p in m.parameters():
p.requires_grad_(False)
m._apply(lambda t: t.long())
input = i.long()
out = m.forward(input)
return out
test_params['constructor'] = gen_long_tensor_constructor(test_params['constructor'])
test_params['input_fn'] = gen_long_tensor_input(test_params['input_size'])
test_params['reference_fn'] = reference_fn
test_params['check_forward_only'] = True
# Currently we don't support conv2d/conv3d for LongTensor in CUDA
test_params['test_cuda'] = False
test = NewModuleTest(**test_params)
add_test(test, decorator)
for test_params in criterion_tests:
if 'constructor' not in test_params:
name = test_params.pop('module_name')
test_params['constructor'] = getattr(nn, name)
test = CriterionTest(**test_params)
decorator = test_params.pop('decorator', None)
add_test(test, decorator)
if 'check_sum_reduction' in test_params:
desc = test_params.get('desc', None)
test_params['desc'] = 'sum_reduction' if desc is None else desc + '_sum_reduction'
def gen_sum_reduction_constructor(constructor):
def sum_reduction_constructor(*args, **kwargs):
cons = constructor(*args, reduction='sum', **kwargs)
return cons
sum_reduction_constructor.__name__ = constructor.__name__
return sum_reduction_constructor
test_params['constructor'] = gen_sum_reduction_constructor(test_params['constructor'])
test = CriterionTest(**test_params)
add_test(test, decorator)
class UnpoolingNet(nn.Module):
def __init__(self, pool, unpool):
super().__init__()
self.pool = pool
self.unpool = unpool
def forward(self, input):
return self.unpool(*self.pool(input))
add_test(NewModuleTest(
constructor=lambda: UnpoolingNet(
nn.MaxPool1d(2, return_indices=True),
nn.MaxUnpool1d(2)),
input_size=(1, 1, 4),
fullname='MaxUnpool1d_net',
default_dtype=torch.double,))
add_test(NewModuleTest(
constructor=lambda: UnpoolingNet(
nn.MaxPool2d(2, return_indices=True),
nn.MaxUnpool2d(2)),
input_size=(1, 1, 2, 4),
fullname='MaxUnpool2d_net',
default_dtype=torch.double,))
add_test(NewModuleTest(
constructor=lambda: UnpoolingNet(
nn.MaxPool3d(2, return_indices=True),
nn.MaxUnpool3d(2)),
input_size=(1, 1, 2, 4, 6),
fullname='MaxUnpool3d_net',
check_gradgrad=False,
default_dtype=torch.double,))
add_test(NewModuleTest(
constructor=lambda: UnpoolingNet(
nn.MaxPool1d(2, return_indices=True),
nn.MaxUnpool1d(2)),
input_size=(1, 4),
reference_fn=single_batch_reference_fn,
fullname='MaxUnpool1d_net_no_batch_dim',
default_dtype=torch.double,))
add_test(NewModuleTest(
constructor=lambda: UnpoolingNet(
nn.MaxPool2d(2, return_indices=True),
nn.MaxUnpool2d(2)),
input_size=(1, 2, 4),
reference_fn=single_batch_reference_fn,
fullname='MaxUnpool2d_net_no_batch_dim',
default_dtype=torch.double,))
add_test(NewModuleTest(
constructor=lambda: UnpoolingNet(
nn.MaxPool3d(2, return_indices=True),
nn.MaxUnpool3d(2)),
input_size=(1, 2, 4, 6),
reference_fn=single_batch_reference_fn,
fullname='MaxUnpool3d_net_no_batch_dim',
check_gradgrad=False,
default_dtype=torch.double,))
class _AdaptiveLogSoftmaxWithLoss(nn.AdaptiveLogSoftmaxWithLoss):
def __call__(self, input):
t = torch.tensor([0, 1, 4, 8]).to(input.device)
return nn.AdaptiveLogSoftmaxWithLoss.__call__(self, input, t).output
add_test(NewModuleTest(
constructor=lambda: _AdaptiveLogSoftmaxWithLoss(16, 10, [2, 6]),
input_size=(4, 16),
fullname='AdaptiveLogSoftmax',
with_tf32=True,
tf32_precision=0.005,
default_dtype=torch.double))
# The following are helpers for TestNN.test_affine_*
if torch.cuda.is_available():
def device_():
return ['cpu', 'cuda']
else:
def device_():
return ['cpu']
def angle_rad_():
return [r * math.pi * 2 for r in [0.0, 0.5, 0.25, 0.125, random.random()]]
def axis_vector_():
t = (random.random(), random.random(), random.random())
l = sum(x ** 2 for x in t) ** 0.5
return [(1.0, 0.0, 0.0), (0.0, 1.0, 0.0), (0.0, 0.0, 1.0), tuple(x / l for x in t)]
def input_size2d_():
return [[1, 1, 3, 5], [1, 1, 3, 3], [1, 1, 4, 4], [1, 1, 3, 4]]
def output_size2d_():
return [[1, 1, 5, 3], [1, 1, 3, 5], [1, 1, 4, 3], [1, 1, 5, 5], [1, 1, 6, 6]]
def input_size2dsq_():
return [[1, 1, 2, 2], [1, 1, 3, 3], [1, 1, 4, 4], [1, 1, 6, 6]]
def output_size2dsq_():
return [[1, 1, 2, 2], [1, 1, 3, 3], [1, 1, 4, 4], [1, 1, 5, 5], [1, 1, 6, 6]]
def input_size3d_():
return [[1, 1, 2, 2, 2], [1, 1, 2, 3, 4], [1, 1, 3, 3, 3], [1, 1, 4, 4, 4], [1, 1, 3, 4, 5]]
def input_size3dsq_():
return [[1, 1, 2, 2, 2], [1, 1, 3, 3, 3], [1, 1, 4, 4, 4], [1, 1, 6, 6, 6]]
def output_size3dsq_():
return [[1, 1, 2, 2, 2], [1, 1, 3, 3, 3], [1, 1, 4, 4, 4], [1, 1, 5, 5, 5], [1, 1, 6, 6, 6]]
def output_size3d_():
return [[1, 1, 2, 2, 2], [1, 1, 3, 3, 3], [1, 1, 3, 4, 5], [1, 1, 4, 3, 2], [1, 1, 5, 5, 5], [1, 1, 6, 6, 6]]
def _buildEquivalentAffineTransforms2d(device, input_size, output_size, angle_rad):
input_center = [(x - 1) / 2.0 for x in input_size]
output_center = [(x - 1) / 2.0 for x in output_size]
s = math.sin(angle_rad)
c = math.cos(angle_rad)
intrans_ary = np.array([
[1, 0, input_center[2]],
[0, 1, input_center[3]],
[0, 0, 1],
], dtype=np.float64)
inscale_ary = np.array([
[input_center[2], 0, 0],
[0, input_center[3], 0],
[0, 0, 1],
], dtype=np.float64)
rotation_ary = np.array([
[c, -s, 0],
[s, c, 0],
[0, 0, 1],
], dtype=np.float64)
outscale_ary = np.array([
[1.0 / output_center[2], 0, 0],
[0, 1.0 / output_center[3], 0],
[0, 0, 1],
], dtype=np.float64)
outtrans_ary = np.array([
[1, 0, -output_center[2]],
[0, 1, -output_center[3]],
[0, 0, 1],
], dtype=np.float64)
reorder_ary = np.array([
[0, 1, 0],
[1, 0, 0],
[0, 0, 1],
], dtype=np.float64)
transform_ary = np.dot(np.dot(np.dot(np.dot(
intrans_ary,
inscale_ary),
rotation_ary.T),
outscale_ary),
outtrans_ary)
grid_ary = np.dot(np.dot(np.dot(reorder_ary, rotation_ary.T), outscale_ary), outtrans_ary)
transform_tensor = torch.from_numpy(rotation_ary).to(device, torch.float32)
transform_tensor = transform_tensor[:2].unsqueeze(0)
return transform_tensor, transform_ary, grid_ary
def _buildEquivalentAffineTransforms3d(device, input_size, output_size, angle_rad, axis_vector):
input_center = [(x - 1) / 2.0 for x in input_size]
output_center = [(x - 1) / 2.0 for x in output_size]
s = math.sin(angle_rad)
c = math.cos(angle_rad)
c1 = 1 - c
intrans_ary = np.array([
[1, 0, 0, input_center[2]],
[0, 1, 0, input_center[3]],
[0, 0, 1, input_center[4]],
[0, 0, 0, 1],
], dtype=np.float64)
inscale_ary = np.array([
[input_center[2], 0, 0, 0],
[0, input_center[3], 0, 0],
[0, 0, input_center[4], 0],
[0, 0, 0, 1],
], dtype=np.float64)
l, m, n = axis_vector
scipyRotation_ary = np.array([
[l * l * c1 + c, m * l * c1 - n * s, n * l * c1 + m * s, 0],
[l * m * c1 + n * s, m * m * c1 + c, n * m * c1 - l * s, 0],
[l * n * c1 - m * s, m * n * c1 + l * s, n * n * c1 + c, 0],
[0, 0, 0, 1],
], dtype=np.float64)
z, y, x = axis_vector
torchRotation_ary = np.array([
[x * x * c1 + c, y * x * c1 - z * s, z * x * c1 + y * s, 0],
[x * y * c1 + z * s, y * y * c1 + c, z * y * c1 - x * s, 0],
[x * z * c1 - y * s, y * z * c1 + x * s, z * z * c1 + c, 0],
[0, 0, 0, 1],
], dtype=np.float64)
outscale_ary = np.array([
[1.0 / output_center[2], 0, 0, 0],
[0, 1.0 / output_center[3], 0, 0],
[0, 0, 1.0 / output_center[4], 0],
[0, 0, 0, 1],
], dtype=np.float64)
outtrans_ary = np.array([
[1, 0, 0, -output_center[2]],
[0, 1, 0, -output_center[3]],
[0, 0, 1, -output_center[4]],
[0, 0, 0, 1],
], dtype=np.float64)
reorder_ary = np.array([
[0, 0, 1, 0],
[0, 1, 0, 0],
[1, 0, 0, 0],
[0, 0, 0, 1],
], dtype=np.float64)
transform_ary = np.dot(np.dot(np.dot(np.dot(
intrans_ary,
inscale_ary),
np.linalg.inv(scipyRotation_ary)),
outscale_ary),
outtrans_ary)
grid_ary = np.dot(np.dot(np.dot(reorder_ary, np.linalg.inv(scipyRotation_ary)), outscale_ary), outtrans_ary)
transform_tensor = torch.from_numpy(torchRotation_ary).to(device, torch.float32)
transform_tensor = transform_tensor[:3].unsqueeze(0)
return transform_tensor, transform_ary, grid_ary
# end TestNN.test_affine_* helpers
class TestNNDeviceType(NNTestCase):
def _test_InstanceNorm_general(self, cls, input, device, dtype=torch.float):
# default case track_running_stats=False
b, c = input.size(0), input.size(1)
input_var = input.to(device=device, dtype=dtype).requires_grad_()
IN = cls(c, eps=0).to(device, dtype)
output = IN(input_var)
out_reshaped = output.view(b * c, -1)
mean = out_reshaped.mean(1)
var = out_reshaped.var(1, unbiased=False)
self.assertEqual(torch.abs(mean.data).mean(), 0, atol=1e-5, rtol=0)
self.assertEqual(torch.abs(var.data).mean(), 1, atol=1e-5, rtol=0)
# check that eval mode doesn't change behavior
grad_out = torch.randn_like(output)
res1 = output.data.clone()
output.backward(grad_out)
grad1 = input_var.grad.data.clone()
IN.eval()
output = IN(input_var)
input_var.grad = None
output.backward(grad_out)
res2 = output.data
grad2 = input_var.grad.data
self.assertEqual(res1, res2)
self.assertEqual(grad1, grad2)
# If track_running_stats=True and momentum=1, running_mean/var should be
# equal to mean/var of the input (with unbias correction)
IN = cls(c, momentum=1, eps=0, track_running_stats=True).to(device, dtype)
output = IN(input_var)
input_reshaped = input_var.transpose(1, 0).reshape(c, -1)
mean = input_reshaped.mean(1)
input_reshaped = input_var.transpose(1, 0).reshape(c, b, -1)
var = input_reshaped.var(2, unbiased=True)[:, :]
self.assertEqual(torch.abs(mean.data - IN.running_mean).mean(), 0, atol=1e-5, rtol=0)
self.assertEqual(torch.abs(var.data.mean(1) - IN.running_var).mean(), 0, atol=1e-5, rtol=0)
# in eval mode, adding X * std to a channel in input should make the
# corresponding channel in output have mean X
IN.eval()
delta = IN.running_var.sqrt() * torch.arange(c, device=device, dtype=dtype)
delta = delta.view(-1, *[1 for _ in range(2, input.dim())])
output = IN(input_var + delta)
self.assertEqual(output.transpose(0, 1).reshape(c, -1).mean(1), torch.arange(c, dtype=dtype))
def _test_InstanceNorm_cuda_half(self, cls, input, device):
# THNN
input = input.to(device=device, dtype=torch.half).random_(1, 10).requires_grad_(True)
m = cls(input.size(1), affine=True, track_running_stats=True).to(device, torch.half)
thnn_output = m(input)
thnn_output.sum().backward()
thnn_input_grad = input.grad.data.clone()
self.assertEqualTypeString(thnn_output, input)
# cuDNN
if TEST_CUDNN:
input.grad = None
m = m.float()
cudnn_output = m(input)
cudnn_output.sum().backward()
cudnn_input_grad = input.grad.data.clone()
self.assertEqualTypeString(cudnn_output, input)
self.assertEqual(cudnn_output, thnn_output, atol=1e-4, rtol=0)
self.assertEqual(cudnn_input_grad, thnn_input_grad, atol=1e-3, rtol=0)
def _test_LayerNorm_general(self, device, dtype=torch.float):
for i in range(2, 6):
shape = torch.randint(3, 6, (i,), dtype=torch.long).tolist()
x = torch.empty(*shape, device=device, dtype=dtype).uniform_(0, 10)
normalized_ndim = random.randint(1, i - 1) # inclusive
normalized_shape = shape[-normalized_ndim:]
unnormalized_shape = shape[:-normalized_ndim]
# test that LN normalizes to mean 0 and stddev 1
ln = nn.LayerNorm(normalized_shape, eps=0).to(device, dtype)
ln.weight.data.fill_(1)
ln.bias.data.fill_(0)
output = ln(x)
out_reshaped = output.view(*(unnormalized_shape + [-1]))
mean = out_reshaped.mean(-1)
var = out_reshaped.var(-1, unbiased=False)
delta = 1e-1 if dtype == torch.bfloat16 else 1e-5
self.assertEqual(torch.abs(mean.data).mean(), 0, atol=delta, rtol=0)
self.assertEqual(torch.abs(var.data).mean(), 1, atol=delta, rtol=0)
# test that LN applies weight and bias correctly
scale, bias = torch.empty(2).uniform_(0.2, 2).tolist()
ln.weight.data.fill_(scale)
ln.bias.data.fill_(bias)
output = ln(x)
out_reshaped = output.view(*(unnormalized_shape + [-1]))
mean = out_reshaped.mean(-1)
var = out_reshaped.var(-1, unbiased=False)
self.assertEqual(torch.abs(mean.data).mean(), bias, atol=delta, rtol=0)
self.assertEqual(torch.abs(var.data).mean(), scale ** 2, atol=delta, rtol=0)
bad_norm_shape_input_shape = {
(): (),
(2, 3): (3,),
(2,): (1, 2, 3),
(10,): (2, 3),
10: (2, 3),
}
for norm_shape, input_shape in bad_norm_shape_input_shape.items():
ln = nn.LayerNorm(norm_shape)
input = torch.empty(input_shape, device=device, dtype=dtype).uniform_(0, 10)
self.assertRaises(RuntimeError, lambda: ln(input))
def _test_LayerNorm_cuda_half(self, device):
input = torch.empty(2, 3, 3, 2, device=device, dtype=torch.half).random_(1, 10).requires_grad_(True)
m = nn.LayerNorm([3, 2]).to(device, torch.half)
output = m(input)
output.sum().backward()
self.assertEqualTypeString(output, input)
def _test_LayerNorm_cpu_mixed_dtype(self, device):
for elementwise_affine in [True, False]:
# layer norm input shape is normalized to m x n, cpu vectorized on n,
# so make sure n exceeds vector length
input = torch.empty(2, 3, 11, 3, device=device, dtype=torch.bfloat16).random_(1, 10)
m = nn.LayerNorm([11, 3], elementwise_affine=elementwise_affine).to(device, torch.bfloat16)
# fp32
m_fp32 = deepcopy(m).to(device, torch.float)
x_fp32 = input.clone().detach().float().requires_grad_()
out_fp32 = m_fp32(x_fp32)
out_fp32.sum().backward()
# bf16
m_bf16 = deepcopy(m)
x_bf16 = input.clone().detach().requires_grad_()
out_bf16 = m_bf16(x_bf16)
out_bf16.sum().backward()
# bf16 mixed type
m_mix = deepcopy(m).to(device, torch.float)
x_mix = input.clone().detach().requires_grad_()
out_mix = m_mix(x_mix)
out_mix.sum().backward()
self.assertEqual(out_fp32.bfloat16(), out_bf16)
self.assertEqual(out_fp32.bfloat16(), out_mix)
self.assertEqual(x_fp32.grad.bfloat16(), x_bf16.grad, atol=1e-1, rtol=1e-1)
self.assertEqual(x_fp32.grad.bfloat16(), x_mix.grad, atol=1e-1, rtol=1e-1)
def _test_GroupNorm_general(self, device, dtype=torch.float):
good_shape_g = {
(1, 2, 3, 4): 2,
(2, 3, 10): 3,
(3, 1, 1, 1, 2): 1,
(2, 6, 4, 2, 2): 3,
(1, 256, 1, 1): 32,
}
for shape_g, grad in product(good_shape_g.items(), [True, False]):
shape, g = shape_g
x = torch.empty(*shape, device=device, dtype=dtype).uniform_(0, 10)
x.requires_grad_(grad)
b = shape[0]
c = shape[1]
# test that GN normalizes to mean 0 and stddev 1
gn = nn.GroupNorm(g, c, eps=0).to(device, dtype)
gn.weight.data.fill_(1)
gn.bias.data.fill_(0)
output = gn(x)
out_reshaped = output.view(b, g, -1)
mean = out_reshaped.mean(-1)
var = out_reshaped.var(-1, unbiased=False)
self.assertEqual(torch.abs(mean).mean(), 0, atol=1e-5, rtol=0)
self.assertEqual(torch.abs(var).mean(), 1, atol=1e-5, rtol=0)
output.backward(torch.randn_like(output))
if output.is_cuda:
torch.cuda.synchronize()
# test that GN applies weight and bias correctly
scale = torch.empty(c, device=device, dtype=dtype).uniform_(0.2, 2)
bias = torch.empty(c, device=device, dtype=dtype).uniform_(0.2, 2)
gn.weight.data.copy_(scale)
gn.bias.data.copy_(bias)
output = gn(x)
out_reshaped = output.view(b, c, -1)
out_normed = (out_reshaped - bias.view(c, 1)) / scale.view(c, 1)
out_normed_reshaped = out_normed.view(b, g, -1)
mean = out_normed_reshaped.mean(-1)
var = out_normed_reshaped.var(-1, unbiased=False)
self.assertEqual(torch.abs(mean).mean(), 0, atol=1e-5, rtol=0)
self.assertEqual(torch.abs(var).mean(), 1, atol=1e-5, rtol=0)
bad_shape_g = {
(1, 2, 3, 4): 3,
(2, 3, 10): 2,
(3, 1, 1, 1, 2): 10,
(2, 6, 4, 2, 2): 4,
}
for shape, g in bad_shape_g.items():
with self.assertRaises(ValueError):
gn = nn.GroupNorm(g, shape[1])
def _test_GroupNorm_cuda_half(self):
input = torch.zeros(2, 4, 3, 2, requires_grad=True).cuda().half().random_(1, 10)
m = nn.GroupNorm(2, 4).to("cuda", torch.half)
output = m(input)
output.sum().backward()
self.assertEqualTypeString(output, input)
def _test_GroupNorm_cpu_mixed_dtype(self):
def helper(self, size, groups, memory_format):
channels = size[1]
input = torch.randn(size, dtype=torch.bfloat16).cpu()
input_bf1 = input.contiguous(memory_format=memory_format).detach().requires_grad_(True)
input_bf2 = input_bf1.clone().detach().requires_grad_(True)
input_f = input_bf1.float().detach().requires_grad_(True)
m_bf = nn.GroupNorm(groups, channels).cpu().bfloat16()
m_f = deepcopy(m_bf).float()
m_f2 = deepcopy(m_f)
# bfloat16 input and bfloat16 parameters
out = m_bf(input_bf1)
# bfloat16 input and float parameters
out2 = m_f(input_bf2)
# float input and float parameters
out3 = m_f2(input_f)
self.assertEqual(out, out2, atol=5e-3, rtol=5e-3)
self.assertEqual(out2.float(), out3, atol=5e-3, rtol=5e-3)
grad_out = torch.randn(out2.shape, dtype=torch.bfloat16).cpu()
grad_out_bf1 = grad_out.contiguous(memory_format=memory_format).detach().requires_grad_(True)
grad_out_bf2 = grad_out_bf1.clone().detach().requires_grad_(True)
grad_out_f = grad_out_bf2.clone().float().detach().requires_grad_(True)
# bfloat16 input grad and float parameters
out2.backward(grad_out_bf2, retain_graph=True)
# float input grad and float parameters
out3.backward(grad_out_f, retain_graph=True)
# bfloat16 input grad and bfloat16 parameters
out.backward(grad_out_bf1, retain_graph=True)
self.assertEqual(m_f.weight.grad, m_f2.weight.grad, atol=1e-5, rtol=1e-5)
self.assertEqual(m_f.bias.grad, m_f2.bias.grad, atol=1e-5, rtol=1e-5)
self.assertEqual(input_bf2.grad.float(), input_f.grad, atol=5e-5, rtol=5e-3)
# Full bf16 has lower precision compared with mixed bf16 and fp32 .
# Use Amp to keep module parameters in acc dtype, i.e. float, for better numerical stability
self.assertEqual(m_bf.weight.grad.float(), m_f.weight.grad, atol=1e-3, rtol=1.2e-1)
self.assertEqual(m_bf.bias.grad.float(), m_f.bias.grad, atol=1e-3, rtol=1e-2)
self.assertEqual(input_bf1.grad, input_bf2.grad, atol=1e-2, rtol=1e-2)
helper(self, (1, 8, 4, 3), 2, torch.contiguous_format)
helper(self, (1, 8, 4, 3), 2, torch.channels_last)
helper(self, (1, 8, 3, 4), 4, torch.contiguous_format)
helper(self, (1, 8, 3, 4), 4, torch.channels_last)
helper(self, (4, 8, 40, 40), 4, torch.contiguous_format),
helper(self, (4, 8, 40, 40), 4, torch.channels_last),
helper(self, (4, 40, 40, 40), 2, torch.contiguous_format)
helper(self, (4, 40, 40, 40), 2, torch.channels_last)
helper(self, (1, 8, 40, 40), 4, torch.contiguous_format)
helper(self, (1, 8, 40, 40), 2, torch.channels_last)
helper(self, (1, 8, 40, 40), 2, torch.contiguous_format)
helper(self, (1, 8, 50, 50), 2, torch.channels_last)
helper(self, (1, 8, 50, 50), 4, torch.contiguous_format)
helper(self, (1, 8, 50, 50), 4, torch.channels_last)
helper(self, (1, 40, 50, 50), 2, torch.contiguous_format)
helper(self, (1, 40, 50, 50), 2, torch.channels_last)
helper(self, (1, 9, 3, 4, 5), 3, torch.contiguous_format)
helper(self, (1, 9, 3, 4, 5), 3, torch.channels_last_3d)
helper(self, (1, 60, 10, 10, 10), 3, torch.contiguous_format)
helper(self, (1, 60, 10, 10, 10), 3, torch.channels_last_3d)
helper(self, (1, 9, 10, 50, 50), 3, torch.contiguous_format)
helper(self, (1, 9, 10, 50, 50), 3, torch.channels_last_3d)
helper(self, (1, 60, 10, 50, 50), 3, torch.contiguous_format)
helper(self, (1, 60, 10, 50, 50), 3, torch.channels_last_3d)
def _test_module_empty_inputs(self, module, inputs):
for _inp in inputs:
_inp.requires_grad_(True)
out = module(*inputs)
gO = torch.rand_like(out)
out.backward(gO)
for p in module.parameters():
if p.requires_grad:
self.assertEqual(p.grad, torch.zeros_like(p.grad))
for _inp in inputs:
self.assertEqual(_inp.grad, torch.zeros_like(_inp))
@unittest.skipIf((not TEST_NUMPY) or (not TEST_SCIPY) or (scipy.__version__ < '1.0.0'),
"Scipy v1.0 and/or numpy not found")
@tf32_on_and_off()
def test_affine_2d_rotate0(self, device):
# scipy before 1.0.0 do not support homogeneous coordinate
# scipy.ndimage.affine_transform, so we need to skip.
input_size = [1, 1, 3, 3]
input_ary = np.array(np.random.random(input_size), dtype=np.float32)
output_size = [1, 1, 5, 5]
angle_rad = 0.
transform_tensor, transform_ary, offset = \
_buildEquivalentAffineTransforms2d(device, input_size, output_size, angle_rad)
scipy_ary = torch.from_numpy(scipy.ndimage.affine_transform(
input_ary[0, 0],
transform_ary,
offset=offset,
output_shape=output_size[2:],
order=1,
mode='nearest',
prefilter=False))
affine_tensor = torch.nn.functional.affine_grid(
transform_tensor,
torch.Size(output_size),
align_corners=True
)
gridsample_ary = torch.nn.functional.grid_sample(
torch.tensor(input_ary, device=device).to(device),
affine_tensor,
padding_mode='border',
align_corners=True
).to('cpu')
self.assertEqual(scipy_ary.mean(), gridsample_ary.mean())
self.assertEqual(scipy_ary, gridsample_ary.reshape_as(scipy_ary))
@unittest.skipIf((not TEST_NUMPY) or (not TEST_SCIPY) or (scipy.__version__ < '1.0.0'),
"Scipy v1.0 and/or numpy not found")
@tf32_on_and_off(0.001)
def test_affine_2d_rotate90(self, device):
# scipy before 1.0.0 do not support homogeneous coordinate
# scipy.ndimage.affine_transform, so we need to skip.
for input_size2dsq, output_size2dsq in \
itertools.product(input_size2dsq_(), output_size2dsq_()):
input_size = input_size2dsq
input_ary = np.array(np.random.random(input_size), dtype=np.float32)
output_size = output_size2dsq
angle_rad = 0.25 * math.pi * 2
transform_tensor, transform_ary, offset = \
_buildEquivalentAffineTransforms2d(device, input_size, output_size, angle_rad)
scipy_ary = torch.from_numpy(scipy.ndimage.affine_transform(
input_ary[0, 0],
transform_ary,
offset=offset,
output_shape=output_size[2:],
order=1,
mode='nearest',
prefilter=True))
if input_size2dsq == output_size2dsq:
self.assertEqual(scipy_ary.mean(), input_ary.mean())
self.assertEqual(scipy_ary[0, 0], input_ary[0, 0, 0, -1])
self.assertEqual(scipy_ary[0, -1], input_ary[0, 0, -1, -1])
self.assertEqual(scipy_ary[-1, -1], input_ary[0, 0, -1, 0])
self.assertEqual(scipy_ary[-1, 0], input_ary[0, 0, 0, 0])
affine_tensor = torch.nn.functional.affine_grid(
transform_tensor,
torch.Size(output_size),
align_corners=True
)
gridsample_ary = torch.nn.functional.grid_sample(
torch.tensor(input_ary, device=device).to(device),
affine_tensor,
padding_mode='border',
align_corners=True
).to('cpu')
self.assertEqual(scipy_ary.mean(), gridsample_ary.mean())
self.assertEqual(scipy_ary, gridsample_ary.reshape_as(scipy_ary))
@unittest.skipIf((not TEST_NUMPY) or (not TEST_SCIPY) or (scipy.__version__ < '1.0.0'),
"Scipy v1.0 and/or numpy not found")
@tf32_on_and_off(0.005)
def test_affine_2d_rotate45(self, device):
# scipy before 1.0.0 do not support homogeneous coordinate
# scipy.ndimage.affine_transform, so we need to skip.
input_size = [1, 1, 3, 3]
input_ary = np.array(np.zeros(input_size), dtype=np.float32)
input_ary[0, 0, 0, :] = 0.5
input_ary[0, 0, 2, 2] = 1.0
output_size = [1, 1, 3, 3]
angle_rad = 0.125 * math.pi * 2
transform_tensor, transform_ary, offset = \
_buildEquivalentAffineTransforms2d(device, input_size, output_size, angle_rad)
scipy_ary = torch.from_numpy(scipy.ndimage.affine_transform(
input_ary[0, 0],
transform_ary,
offset=offset,
output_shape=output_size[2:],
order=1,
mode='nearest',
prefilter=False))
affine_tensor = torch.nn.functional.affine_grid(
transform_tensor,
torch.Size(output_size),
align_corners=True
)
gridsample_ary = torch.nn.functional.grid_sample(
torch.tensor(input_ary, device=device).to(device),
affine_tensor,
padding_mode='border',
align_corners=True
).to('cpu')
self.assertEqual(scipy_ary, gridsample_ary.reshape_as(scipy_ary))
@unittest.skipIf((not TEST_NUMPY) or (not TEST_SCIPY) or (scipy.__version__ < '1.0.0'),
"Scipy v1.0 and/or numpy not found")
@tf32_on_and_off(0.005)
def test_affine_2d_rotateRandom(self, device):
# scipy before 1.0.0 do not support homogeneous coordinate
# scipy.ndimage.affine_transform, so we need to skip.
for angle_rad, input_size2d, output_size2d in \
itertools.product(angle_rad_(), input_size2d_(), output_size2d_()):
input_size = input_size2d
input_ary = np.array(np.random.random(input_size), dtype=np.float32).round(3)
output_size = output_size2d
input_ary[0, 0, 0, 0] = 2
input_ary[0, 0, 0, -1] = 4
input_ary[0, 0, -1, 0] = 6
input_ary[0, 0, -1, -1] = 8
transform_tensor, transform_ary, grid_ary = \
_buildEquivalentAffineTransforms2d(device, input_size, output_size, angle_rad)
scipy_ary = torch.from_numpy(scipy.ndimage.affine_transform(
input_ary[0, 0],
transform_ary,
output_shape=output_size[2:],
order=1,
mode='nearest',
prefilter=False))
affine_tensor = torch.nn.functional.affine_grid(
transform_tensor,
torch.Size(output_size),
align_corners=True
)
gridsample_ary = torch.nn.functional.grid_sample(
torch.tensor(input_ary, device=device).to(device),
affine_tensor,
padding_mode='border',
align_corners=True
).to('cpu')
affine_tensor = affine_tensor.to('cpu')
for r in range(affine_tensor.size(1)):
for c in range(affine_tensor.size(2)):
grid_out = np.dot(grid_ary, [r, c, 1])
self.assertEqual(affine_tensor[0, r, c], grid_out[:2], exact_dtype=False)
self.assertEqual(scipy_ary, gridsample_ary.reshape_as(scipy_ary))
@unittest.skipIf((not TEST_NUMPY) or (not TEST_SCIPY) or (scipy.__version__ < '1.0.0'),
"Scipy v1.0 and/or numpy not found")
@tf32_on_and_off(0.005)
def test_affine_3d_rotateRandom(self, device):
# scipy before 1.0.0 do not support homogeneous coordinate
# scipy.ndimage.affine_transform, so we need to skip.
for angle_rad, axis_vector, input_size3d, output_size3d in \
itertools.product(angle_rad_(), axis_vector_(), input_size3d_(), output_size3d_()):
input_size = input_size3d
input_ary = np.array(np.random.random(input_size), dtype=np.float32)
output_size = output_size3d
input_ary[0, 0, 0, 0, 0] = 2
input_ary[0, 0, 0, 0, -1] = 3
input_ary[0, 0, 0, -1, 0] = 4
input_ary[0, 0, 0, -1, -1] = 5
input_ary[0, 0, -1, 0, 0] = 6
input_ary[0, 0, -1, 0, -1] = 7
input_ary[0, 0, -1, -1, 0] = 8
input_ary[0, 0, -1, -1, -1] = 9
transform_tensor, transform_ary, grid_ary = \
_buildEquivalentAffineTransforms3d(device, input_size, output_size, angle_rad, axis_vector)
scipy_ary = torch.from_numpy(scipy.ndimage.affine_transform(
input_ary[0, 0],
transform_ary,
output_shape=output_size[2:],
order=1,
mode='nearest',
prefilter=False))
affine_tensor = torch.nn.functional.affine_grid(
transform_tensor,
torch.Size(output_size),
align_corners=True
)
gridsample_ary = torch.nn.functional.grid_sample(
torch.tensor(input_ary, device=device).to(device),
affine_tensor,
padding_mode='border',
align_corners=True
).to('cpu')
affine_tensor = affine_tensor.to('cpu')
for i in range(affine_tensor.size(1)):
for r in range(affine_tensor.size(2)):
for c in range(affine_tensor.size(3)):
grid_out = np.dot(grid_ary, [i, r, c, 1])
self.assertEqual(affine_tensor[0, i, r, c], grid_out[:3], exact_dtype=False)
self.assertEqual(scipy_ary, gridsample_ary.reshape_as(scipy_ary))
@onlyCUDA
@dtypes(torch.float, torch.half)
def test_batchnorm_large_batch(self, device, dtype):
bn = nn.BatchNorm2d(1).to(device, dtype)
data = torch.rand(880801, 1, 1, 1, device=device, dtype=dtype)
out = bn(data).sum().backward()
@dtypesIfCUDA(torch.float, torch.double, torch.half, torch.complex128)
@dtypes(torch.float, torch.double, torch.bfloat16, torch.complex128)
def test_conv_empty_input(self, device, dtype):
def help(input, conv, memory_format):
ref_out = conv(input)
conv_cl = conv.to(memory_format=memory_format)
out_cl = conv_cl(input)
self.assertEqual(ref_out, out_cl)
input_cl = input.to(memory_format=memory_format)
out_cl2 = conv(input_cl)
self.assertEqual(out_cl, out_cl2)
out_cl3 = conv_cl(input_cl)
self.assertEqual(out_cl, out_cl3)
# channels_last case
input2d = torch.randn((0, 4, 20, 20)).to(device=device, dtype=dtype)
conv2d = torch.nn.Conv2d(4, 4, 3, 1).to(device=device, dtype=dtype)
help(input2d, conv2d, torch.channels_last)
# channels_last_3d case
input3d = torch.randn((0, 4, 20, 20, 20)).to(device=device, dtype=dtype)
conv3d = torch.nn.Conv3d(4, 4, 3, 1).to(device=device, dtype=dtype)
help(input3d, conv3d, torch.channels_last_3d)
# non-contiguous case
weight = torch.rand(4, 8, 3, 3)[:, ::2, :, :].to(device=device, dtype=dtype)
bias = torch.rand(4).to(device=device, dtype=dtype)
out = F.conv2d(input2d, weight, bias, (1, 1), 0, (1, 1), 1)
weight = weight.contiguous()
out_ref = F.conv2d(input2d, weight, bias, (1, 1), 0, (1, 1), 1)
self.assertEqual(out_ref, out)
# sigfpe reported in https://github.com/pytorch/pytorch/issues/94125
with self.assertRaises(RuntimeError):
inp = torch.empty([1, 1, 1, 0], dtype=dtype, device=device)
weight = torch.empty([1, 0, 1], dtype=dtype, device=device)
torch._C._nn.slow_conv3d(inp, weight, 1)
def test_InstanceNorm1d_general(self, device):
b = random.randint(3, 5)
c = random.randint(3, 5)
d = random.randint(8, 10)
input = torch.rand(b, c, d)
self._test_InstanceNorm_general(nn.InstanceNorm1d, input, device)
if self.device_type == 'cuda':
self._test_InstanceNorm_cuda_half(nn.InstanceNorm1d, input, device)
def test_InstanceNorm2d_general(self, device):
b = random.randint(3, 5)
c = random.randint(3, 5)
w = random.randint(3, 6)
h = random.randint(6, 8)
input = torch.rand(b, c, h, w)
self._test_InstanceNorm_general(nn.InstanceNorm2d, input, device)
if self.device_type == 'cuda':
self._test_InstanceNorm_cuda_half(nn.InstanceNorm2d, input, device)
def test_InstanceNorm3d_general(self, device):
b = random.randint(3, 5)
c = random.randint(3, 5)
w = random.randint(2, 5)
h = random.randint(2, 5)
d = random.randint(2, 5)
input = torch.rand(b, c, h, w, d)
self._test_InstanceNorm_general(nn.InstanceNorm3d, input, device)
if self.device_type == 'cuda':
self._test_InstanceNorm_cuda_half(nn.InstanceNorm3d, input, device)
@parametrize_test("instance_norm_cls", [nn.InstanceNorm1d, nn.InstanceNorm2d, nn.InstanceNorm3d], name_fn=lambda c: c.__name__)
@parametrize_test("no_batch_dim", [True, False])
@parametrize_test("affine", [True, False])
def test_instancenorm_raises_error_if_input_channels_is_not_num_features(self, device, instance_norm_cls, no_batch_dim, affine):
inst_norm = instance_norm_cls(4, affine=affine)
size = [2] * inst_norm._get_no_batch_dim()
if not no_batch_dim:
size = [3] + size
t = torch.randn(size)
if affine:
with self.assertRaisesRegex(ValueError, "expected input's size at dim="):
inst_norm(t)
else:
with warnings.catch_warnings(record=True) as w:
inst_norm(t)
self.assertIn("which is not used because affine=False", str(w[0].message))
def test_instancenorm_raises_error_if_less_than_one_value_per_channel(self, device):
x = torch.rand(10)[None, :, None]
with self.assertRaises(ValueError):
torch.nn.InstanceNorm1d(10)(x).to(device)
def test_instancenorm_raises_error_for_single_spatial_element_during_training(self, device):
BATCH_SIZE = 10
NUM_CHANNELS = 3
norms = [torch.nn.InstanceNorm1d, torch.nn.InstanceNorm2d, torch.nn.InstanceNorm3d]
for i, norm in enumerate(norms):
m = norm(NUM_CHANNELS, track_running_stats=True)
m.to(device)
# Create an appropriately-sized input with a single spatial element.
input = torch.randn(BATCH_SIZE, NUM_CHANNELS, *[1 for _ in range(i + 1)],
device=device)
with self.assertRaises(ValueError):
m(input)
# Single spatial element should be fine in eval.
m.eval()
m(input)
def test_LayerNorm_general(self, device):
self._test_LayerNorm_general(device)
if self.device_type == 'cuda' or self.device_type == 'cpu':
self._test_LayerNorm_general(device, dtype=torch.bfloat16)
if self.device_type == 'cuda':
self._test_LayerNorm_cuda_half(device)
if self.device_type == 'cpu':
self._test_LayerNorm_cpu_mixed_dtype(device)
@onlyNativeDeviceTypes
def test_LayerNorm_numeric(self, device):
def layer_norm_ref(X, gamma, beta, normalized_shape, eps):
feature_size = np.prod(normalized_shape)
X_view = X.view(-1, feature_size)
mean = X_view.mean(dim=-1, keepdim=True)
var = X_view.var(dim=-1, unbiased=False, keepdim=True)
Y = (X_view - mean) / torch.sqrt(var + eps)
Y = Y * gamma.view(-1) + beta.view(-1)
return Y.view(*X.size())
normalized_shape = [256, 256, 144]
layer_norm = nn.LayerNorm(normalized_shape).float().to(device)
X = torch.rand(2, *normalized_shape, dtype=torch.float32,
device=device)
Y = layer_norm(X)
Y_ref = layer_norm_ref(X, layer_norm.weight.data, layer_norm.bias.data,
normalized_shape, layer_norm.eps)
self.assertEqual(Y, Y_ref, rtol=0, atol=1e-5)
if self.device_type == 'cuda':
layer_norm.cpu()
Y_cpu = layer_norm(X.cpu())
self.assertEqual(Y_cpu, Y, rtol=0, atol=1e-5)
@onlyCPU
def test_glu_bfloat16(self, device):
def test_dtype(fn, input, dtype):
input = input.detach().clone().to(dtype=dtype).requires_grad_(True)
input2 = input.detach().clone().float().requires_grad_(True)
out = fn(input)
out.sum().backward()
out2 = fn(input2)
out2.sum().backward()
self.assertEqual(out.dtype, dtype)
self.assertEqual(input.grad.dtype, dtype)
self.assertEqual(out, out2, exact_dtype=False)
self.assertEqual(input.grad, input2.grad, atol=1e-2, rtol=0, exact_dtype=False)
def func(device):
return torch.nn.GLU(dim=-1).to(device)
shapes = [[1, 3, 1, 6], [1, 3, 1, 128], [1, 3, 256, 256]]
for shape in shapes:
x = torch.randn(shape, device=device)
test_dtype(func(device), x, torch.bfloat16)
@onlyNativeDeviceTypes
def test_GroupNorm_general(self, device):
self._test_GroupNorm_general(device)
if self.device_type == 'cuda':
self._test_GroupNorm_cuda_half()
if self.device_type == 'cpu':
self._test_GroupNorm_cpu_mixed_dtype()
def test_GroupNorm_raises_error_if_one_value_per_group(self, device):
x = torch.rand(10)[None, :, None]
with self.assertRaises(ValueError):
torch.nn.GroupNorm(10, 10)(x).to(device)
def test_GroupNorm_empty(self, device):
mod = torch.nn.GroupNorm(2, 4).to(device)
inp = torch.randn(0, 4, 2, 2, device=device)
_test_module_empty_input(self, mod, inp)
if self.device_type == 'cuda' and self.has_cudnn():
with torch.backends.cudnn.flags(enabled=False):
_test_module_empty_input(self, mod, inp)
@onlyCPU
@dtypes(torch.float, torch.double, torch.bfloat16)
def test_groupnorm_nhwc(self, device, dtype):
def helper(self, size, groups, memory_format, is_mixed):
channels = size[1]
input = torch.randn(size, dtype=dtype, device=device, requires_grad=True)
input = input.contiguous(memory_format=memory_format)
input.retain_grad()
grad = torch.randn(size, dtype=dtype, device=device)
grad = grad.contiguous(memory_format=memory_format)
if dtype == torch.bfloat16 and is_mixed:
gn = nn.GroupNorm(groups, channels).to(device).to(torch.float)
else:
gn = nn.GroupNorm(groups, channels).to(device).to(dtype)
gn.weight.data.uniform_()
gn.bias.data.uniform_()
ref_input = input.detach().clone().contiguous(memory_format=torch.contiguous_format).requires_grad_(True)
ref_grad = grad.detach().clone().contiguous(memory_format=torch.contiguous_format)
if dtype == torch.bfloat16 and is_mixed:
ref_gn = nn.GroupNorm(groups, channels).to(device).to(torch.float)
else:
ref_gn = nn.GroupNorm(groups, channels).to(device).to(dtype)
ref_gn.load_state_dict(gn.state_dict())
out = gn(input)
out.backward(grad)
ref_out = ref_gn(ref_input)
ref_out.backward(ref_grad)
self.assertTrue(out.is_contiguous(memory_format=memory_format))
self.assertTrue(ref_out.is_contiguous(memory_format=torch.contiguous_format))
self.assertEqual(out, ref_out)
# parameters in bfloat16 is not recommended
self.assertEqual(gn.weight.grad, ref_gn.weight.grad, atol=5e-4, rtol=5e-4)
self.assertEqual(gn.bias.grad, ref_gn.bias.grad, atol=5e-4, rtol=5e-4)
self.assertEqual(input.grad, ref_input.grad, atol=5e-4, rtol=8e-3)
helper(self, (4, 8, 10, 10), 4, torch.channels_last, False)
helper(self, (2, 30, 9, 9), 3, torch.channels_last, False)
helper(self, (4, 8, 40, 40), 4, torch.channels_last, False)
helper(self, (4, 40, 40, 40), 2, torch.channels_last, False)
helper(self, (2, 30, 50, 50), 3, torch.channels_last, False)
helper(self, (2, 60, 50, 50), 3, torch.channels_last, False)
helper(self, (2, 9, 7, 11, 15), 3, torch.channels_last_3d, False)
helper(self, (2, 9, 7, 200, 15), 3, torch.channels_last_3d, False)
helper(self, (2, 60, 7, 200, 15), 3, torch.channels_last_3d, False)
helper(self, (4, 8, 10, 10), 4, torch.channels_last, True)
helper(self, (2, 30, 9, 9), 3, torch.channels_last, True)
helper(self, (4, 8, 40, 40), 4, torch.channels_last, True)
helper(self, (4, 40, 40, 40), 2, torch.channels_last, True)
helper(self, (2, 30, 50, 50), 3, torch.channels_last, True)
helper(self, (2, 60, 50, 50), 3, torch.channels_last, True)
helper(self, (2, 9, 7, 11, 15), 3, torch.channels_last_3d, True)
helper(self, (2, 9, 7, 200, 15), 3, torch.channels_last_3d, True)
helper(self, (2, 60, 7, 200, 15), 3, torch.channels_last_3d, True)
@onlyNativeDeviceTypes
def test_GroupNorm_memory_format(self, device):
# Tests for regression reported in https://github.com/pytorch/pytorch/issues/92166
def helper(input_format, grad_format, B=2, C=4, W=4, H=4):
import copy
net_orig = torch.nn.GroupNorm(B, C).to(device=device)
net = copy.deepcopy(net_orig)
x_orig = torch.rand(B, C, W, H, device=device, requires_grad=True)
grad_orig = torch.rand(B, C, W, H, device=device)
x = x_orig.clone().detach().to(memory_format=input_format).requires_grad_(True)
grad = grad_orig.detach().to(memory_format=grad_format)
y = net(x)
y.backward(grad)
y_orig = net_orig(x_orig)
y_orig.backward(grad_orig)
self.assertEqual(y, y_orig)
self.assertEqual(x.grad, x_orig.grad)
for input_format in [torch.contiguous_format, torch.channels_last]:
for grad_format in [torch.contiguous_format, torch.channels_last]:
helper(input_format, grad_format)
@onlyNativeDeviceTypes
def test_GroupNorm_numeric(self, device):
def group_norm_ref(X, gamma, beta, groups, channels, eps):
batch_size = X.size()[0]
X_view = X.view(batch_size, groups, -1)
mean = X_view.mean(dim=-1, keepdim=True)
var = X_view.var(dim=-1, unbiased=False, keepdim=True)
Y = ((X_view - mean) / torch.sqrt(var + eps)).view(
batch_size, channels, -1)
Y = Y * gamma.view(channels, 1) + beta.view(channels, 1)
return Y.view(*X.size())
batch_size = 1
groups = 2
channels = 8
group_norm = nn.GroupNorm(groups, channels).float().to(device)
X = torch.rand(batch_size, channels, 256, 256, 72,
dtype=torch.float32, device=device)
Y = group_norm(X)
Y_ref = group_norm_ref(
X, group_norm.weight.data, group_norm.bias.data, groups,
channels, group_norm.eps)
self.assertEqual(Y, Y_ref, rtol=0, atol=1e-5)
if self.device_type == 'cuda':
group_norm.cpu()
Y_cpu = group_norm(X.cpu())
self.assertEqual(Y_cpu, Y, rtol=0, atol=1e-5)
@onlyNativeDeviceTypes
@dtypes(torch.float64, torch.complex128)
def test_pad(self, device, dtype):
# Assert assertion errors are raised for invalid circular padding values
inputs = torch.randn(1, 1, 4, device=device, dtype=dtype, requires_grad=True)
# Should raise error when trying to wrap around more than once
self.assertRaises(RuntimeError, lambda: F.pad(inputs, (5, 4), mode='circular'))
self.assertRaises(RuntimeError, lambda: F.pad(inputs, (3, 6), mode='circular'))
# Should raise error when negative padding results in negative output shape
self.assertRaises(RuntimeError, lambda: F.pad(inputs, (-3, -2), mode='circular'))
# assert that relfection padding errors when pad >= input size
expected_err_msg = r"Padding size should be less than the corresponding input dimension"
inputs = torch.randn(1, 1, 2, 3, device=device, dtype=dtype)
self.assertRaisesRegex(RuntimeError, expected_err_msg,
lambda: F.pad(inputs, (1, 1, 3, 0), mode='reflect'))
inputs = torch.randn(1, 1, 2, device=device, dtype=dtype)
self.assertRaisesRegex(RuntimeError, expected_err_msg,
lambda: F.pad(inputs, (2, 1), mode='reflect'))
inputs = torch.rand(1, 3, 4, 4, device=device, dtype=dtype)
# assert that pad doesn't return a view into the input tensor
for mode in 'constant', 'reflect', 'replicate', 'circular':
out = F.pad(inputs, (0, 0, 0, 0), mode=mode)
out.fill_(4)
self.assertTrue(torch.all(torch.abs(inputs) < 2))
out = F.pad(inputs, (0, 0, -1, -1), mode=mode)
out.fill_(4)
self.assertTrue(torch.all(torch.abs(inputs) < 2))
@onlyNativeDeviceTypes
@dtypes(torch.float64, torch.complex128)
def test_ReplicationPad_empty(self, device, dtype):
for mod, inp in [
(torch.nn.ReplicationPad1d(3), torch.randn(0, 3, 10, device=device, dtype=dtype)),
(torch.nn.ReplicationPad2d(3), torch.randn(0, 3, 10, 10, device=device, dtype=dtype)),
(torch.nn.ReplicationPad3d(3), torch.randn(0, 3, 10, 10, 10, device=device, dtype=dtype))]:
_test_module_empty_input(self, mod, inp, check_size=False)
with self.assertRaisesRegex(RuntimeError, 'Expected 2D or 3D'):
mod = torch.nn.ReplicationPad1d(2)
inp = torch.randn(3, 0, 10, device=device, dtype=dtype)
mod(inp)
with self.assertRaisesRegex(RuntimeError, 'Expected 3D or 4D'):
mod = torch.nn.ReplicationPad2d((2, 2, 2, 2))
inp = torch.randn(43, 0, 10, 10, device=device, dtype=dtype)
mod(inp)
with self.assertRaisesRegex(RuntimeError, 'Expected 4D or 5D'):
mod = torch.nn.ReplicationPad3d((2, 2, 2, 2, 2, 2))
inp = torch.randn(3, 0, 10, 10, 10, device=device, dtype=dtype)
mod(inp)
def test_ReplicationPad1d_large(self, device):
shapes = ([2, 65736, 4], [65736, 2, 4])
pl, pr = 3, 4
for shape in shapes:
x = torch.randn(shape, device=device, requires_grad=True)
model = torch.nn.ReplicationPad1d((pl, pr))
# forward
out = model(x)
self.assertEqual(out[:, :, pl : -pr], x)
left_padding = out[:, :, : pl]
self.assertEqual(left_padding, x[:, :, :1].expand_as(left_padding))
right_padding = out[:, :, -pr :]
self.assertEqual(right_padding, x[:, :, -1:].expand_as(right_padding))
# backward
g = torch.randn_like(out)
out.backward(g)
self.assertEqual(x.grad[:, :, 1 : -1], g[:, :, pl + 1 : -pr - 1])
self.assertEqual(x.grad[:, :, 0], g[:, :, : pl + 1].sum(-1))
self.assertEqual(x.grad[:, :, -1], g[:, :, -pr - 1:].sum(-1))
def test_ReplicationPad2d_large(self, device):
shapes = ([2, 65736, 4, 4], [65736, 2, 4, 4])
pl, pr, pt, pb = 3, 4, 5, 6
for shape in shapes:
x = torch.randn(shape, device=device, requires_grad=True)
model = torch.nn.ReplicationPad2d((pl, pr, pt, pb))
# forward center, edge
out = model(x)
self.assertEqual(out[:, :, pt : -pb, pl : -pr], x)
left_padding = out[:, :, pt : -pb, : pl]
self.assertEqual(left_padding, x[:, :, :, :1].expand_as(left_padding))
right_padding = out[:, :, pt : -pb, -pr :]
self.assertEqual(right_padding, x[:, :, :, -1:].expand_as(right_padding))
top_padding = out[:, :, : pt, pl : -pr]
self.assertEqual(top_padding, x[:, :, :1, :].expand_as(top_padding))
bottom_padding = out[:, :, -pb : , pl : -pr]
self.assertEqual(bottom_padding, x[:, :, -1:, :].expand_as(bottom_padding))
# forward corner
tl_padding = out[:, :, : pt + 1, : pl + 1]
self.assertEqual(tl_padding, x[:, :, :1, :1].expand_as(tl_padding))
tr_padding = out[:, :, : pt + 1, -pr - 1:]
self.assertEqual(tr_padding, x[:, :, :1, -1:].expand_as(tr_padding))
bl_padding = out[:, :, -pb - 1:, : pl + 1]
self.assertEqual(bl_padding, x[:, :, -1:, :1].expand_as(bl_padding))
br_padding = out[:, :, -pb - 1:, -pr - 1:]
self.assertEqual(br_padding, x[:, :, -1:, -1:].expand_as(br_padding))
# backward center, edge
g = torch.randn_like(out)
out.backward(g)
self.assertEqual(x.grad[:, :, 1:-1, 1:-1], g[:, :, pt + 1 : -pb - 1, pl + 1 : -pr - 1])
self.assertEqual(x.grad[:, :, 1:-1, 0], g[:, :, pt + 1 : -pb - 1, : pl + 1].sum(-1))
self.assertEqual(x.grad[:, :, 1:-1, -1], g[:, :, pt + 1 : -pb - 1, -pr - 1 :].sum(-1))
self.assertEqual(x.grad[:, :, 0, 1:-1], g[:, :, : pt + 1, pl + 1 : -pr - 1].sum(-2))
self.assertEqual(x.grad[:, :, -1, 1:-1], g[:, :, -pb - 1 :, pl + 1 : -pr - 1].sum(-2))
# backward corner
self.assertEqual(x.grad[:, :, 0, 0], g[:, :, : pt + 1, : pl + 1].sum((-2, -1)))
self.assertEqual(x.grad[:, :, 0, -1], g[:, :, : pt + 1, -pr - 1 :].sum((-2, -1)))
self.assertEqual(x.grad[:, :, -1, 0], g[:, :, -pb - 1 :, : pl + 1].sum((-2, -1)))
self.assertEqual(x.grad[:, :, -1, -1], g[:, :, -pb - 1 :, -pr - 1 :].sum((-2, -1)))
@largeTensorTest("6GB")
def test_ReplicationPad3d_large(self, device):
shapes = ([1, 65736, 2, 2, 2], [65736, 1, 2, 2, 2])
pl, pr, pt, pbt, pf, pbk = 3, 4, 5, 6, 7, 8
for shape in shapes:
x = torch.randn(shape, device=device, requires_grad=True)
model = torch.nn.ReplicationPad3d((pl, pr, pt, pbt, pf, pbk))
# forward center
out = model(x)
self.assertEqual(out[:, :, pf : -pbk, pt : -pbt, pl : -pr], x)
# backward center
g = torch.randn_like(out)
out.backward(g)
self.assertEqual(x.grad[:, :, 1:-1, 1:-1, 1:-1], g[:, :, pf + 1 : -pbk - 1, pt + 1 : -pbt - 1, pl + 1 : -pr - 1])
@onlyNativeDeviceTypes
def test_Bilinear_empty(self, device):
mod = torch.nn.Bilinear(20, 30, 40).to(device)
inp1 = torch.randn(0, 10, 20, requires_grad=True, device=device)
inp2 = torch.randn(0, 10, 30, requires_grad=True, device=device)
output = mod(inp1, inp2)
output.sum().backward()
self.assertEqual(inp1, torch.zeros_like(inp1))
self.assertEqual(inp2, torch.zeros_like(inp2))
self.assertEqual(inp1.grad, torch.zeros_like(inp1))
self.assertEqual(inp2.grad, torch.zeros_like(inp2))
@expectedFailureMeta # RuntimeError: cannot reshape tensor of 0 elements into shape [1, 0, -1]
@onlyNativeDeviceTypes
def test_TransformerEncoderLayer_empty(self, device):
for training in (True, False):
for batch_first, input_shape in [(True, (0, 10, 512)),
(False, (10, 0, 512))]:
input = torch.rand(*input_shape, device=device, dtype=torch.double)
encoder_layer = nn.TransformerEncoderLayer(
d_model=512, nhead=8, batch_first=batch_first, dtype=torch.double).to(device)
if not training:
encoder_layer = encoder_layer.eval()
with torch.no_grad():
_test_module_empty_input(self, encoder_layer, input, check_size=False, inference=True)
if batch_first and not TEST_WITH_CROSSREF:
with torch.no_grad():
# A NestedTensor with no tensors inside it doesn't have dim 3 (or dim
# 2, for that matter) so it can't hit the fast path, nor can we give a
# result.
with self.assertRaisesRegex(
AssertionError, 'MultiheadAttention does not support NestedTensor outside'):
nt = torch.nested.nested_tensor([], device=device)
_test_module_empty_input(self, encoder_layer, nt, check_size=False, inference=True)
nt = torch.nested.nested_tensor([torch.rand(0, 512, device=device, dtype=torch.double)], device=device)
_test_module_empty_input(self, encoder_layer, nt, check_size=False, inference=True)
else:
_test_module_empty_input(self, encoder_layer, input, check_size=False)
@expectedFailureMeta # RuntimeError: cannot reshape tensor of 0 elements into shape [1, 0, -1]
@onlyNativeDeviceTypes
def test_TransformerEncoder_empty(self, device):
for batch_first, input_shape in [(True, (0, 10, 512)),
(False, (10, 0, 512))]:
input = torch.rand(*input_shape, device=device, dtype=torch.double)
encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8, batch_first=batch_first, dtype=torch.double).to(device)
transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=6).to(device)
_test_module_empty_input(self, transformer_encoder, input, check_size=False)
@expectedFailureMeta # RuntimeError: cannot reshape tensor of 0 elements into shape [1, 0, -1]
@onlyNativeDeviceTypes
def test_TransformerDecoderLayer_empty(self, device):
for batch_first, memory_shape, tgt_shape in [(True, (0, 10, 512), (0, 20, 512)),
(False, (10, 0, 512), (20, 0, 512))]:
memory = torch.rand(*memory_shape, device=device, dtype=torch.double)
tgt = torch.rand(*tgt_shape, requires_grad=True, device=device, dtype=torch.double)
decoder_layer = nn.TransformerDecoderLayer(d_model=512, nhead=8, batch_first=batch_first, dtype=torch.double).to(device)
self._test_module_empty_inputs(decoder_layer, [tgt, memory])
@expectedFailureMeta # RuntimeError: cannot reshape tensor of 0 elements into shape [1, 0, -1]
@onlyNativeDeviceTypes
def test_TransformerDecoder_empty(self, device):
for batch_first, memory_shape, tgt_shape in [(True, (0, 10, 512), (0, 20, 512)),
(False, (10, 0, 512), (20, 0, 512))]:
memory = torch.rand(*memory_shape, device=device, dtype=torch.double)
tgt = torch.rand(*tgt_shape, requires_grad=True, device=device, dtype=torch.double)
decoder_layer = nn.TransformerDecoderLayer(d_model=512, nhead=8, batch_first=batch_first, dtype=torch.double).to(device)
transformer_decoder = nn.TransformerDecoder(decoder_layer, num_layers=6).to(device)
self._test_module_empty_inputs(transformer_decoder, [tgt, memory])
@expectedFailureMeta # RuntimeError: cannot reshape tensor of 0 elements into shape [1, 0, -1]
@onlyNativeDeviceTypes
def test_Transformer_empty(self, device):
for batch_first, src_shape, tgt_shape in [(True, (10, 0, 512), (20, 0, 512))]:
transformer_model = nn.Transformer(nhead=16, num_encoder_layers=12, dtype=torch.double).to(device)
src = torch.rand(*src_shape, requires_grad=True, device=device, dtype=torch.double)
tgt = torch.rand(*tgt_shape, requires_grad=True, device=device, dtype=torch.double)
self._test_module_empty_inputs(transformer_model, [src, tgt])
@onlyNativeDeviceTypes
@dtypes(torch.float32, torch.complex64)
def test_ReflectionPad_empty(self, device, dtype):
for mod, inp in [
(torch.nn.ReflectionPad1d(2), torch.randn(0, 3, 10, device=device, dtype=dtype)),
(torch.nn.ReflectionPad2d(2), torch.randn(0, 3, 10, 10, device=device, dtype=dtype)),
(torch.nn.ReflectionPad3d(3), torch.randn(0, 3, 10, 10, 10, device=device, dtype=dtype))]:
_test_module_empty_input(self, mod, inp, check_size=False)
with self.assertRaisesRegex(RuntimeError, '2D or 3D'):
mod = torch.nn.ReflectionPad1d(2)
inp = torch.randn(3, 0, 10, device=device, dtype=dtype)
mod(inp)
with self.assertRaisesRegex(RuntimeError, '3D or 4D'):
mod = torch.nn.ReflectionPad2d(2)
inp = torch.randn(3, 0, 10, 10, device=device, dtype=dtype)
mod(inp)
with self.assertRaisesRegex(RuntimeError, '4D or 5D'):
mod = torch.nn.ReflectionPad3d(3)
inp = torch.randn(3, 0, 10, 10, 10, device=device, dtype=dtype)
mod(inp)
@onlyCUDA # Test if CPU and GPU results match
def test_ReflectionPad2d_large(self, device):
shapes = ([2, 65736, 6, 6], [65736, 2, 6, 6])
pad = (1, 2, 3, 4)
for shape in shapes:
x = torch.randn(shape, device=device, requires_grad=True)
ref_x = x.detach().cpu().requires_grad_()
out = F.pad(x, pad, mode='reflect')
ref_out = F.pad(ref_x, pad, mode='reflect')
self.assertEqual(out, ref_out)
g = torch.randn_like(out)
ref_g = g.cpu()
out.backward(g)
ref_out.backward(ref_g)
self.assertEqual(x.grad, ref_x.grad)
@onlyNativeDeviceTypes
def test_LocalResponseNorm_empty(self, device):
mod = torch.nn.LocalResponseNorm(2).to(device)
inp = torch.ones(0, 5, 24, 24, device=device)
_test_module_empty_input(self, mod, inp, check_size=False)
@onlyCUDA # Test if CPU and GPU results match
def test_ReflectionPad3d_large(self, device):
shapes = ([2, 1000, 7, 7, 7], [1000, 2, 7, 7, 7])
pad = (1, 2, 3, 4, 5, 6)
for shape in shapes:
x = torch.randn(shape, device=device, requires_grad=True)
ref_x = x.detach().cpu().requires_grad_()
out = F.pad(x, pad, mode='reflect')
ref_out = F.pad(ref_x, pad, mode='reflect')
self.assertEqual(out, ref_out)
g = torch.randn_like(out)
ref_g = g.cpu()
out.backward(g)
ref_out.backward(ref_g)
self.assertEqual(x.grad, ref_x.grad)
@onlyNativeDeviceTypes
@dtypes(torch.float, torch.double)
def test_MarginLoss_empty(self, device, dtype):
for mod, x, y in [
(torch.nn.MultiMarginLoss().to(device),
torch.randn(0, 10, requires_grad=True, device=device, dtype=dtype),
torch.ones(0, device=device).type(torch.long)),
(torch.nn.MultiLabelMarginLoss().to(device),
torch.randn(0, 10, requires_grad=True, device=device, dtype=dtype),
torch.ones(0, 10, device=device).type(torch.long))]:
out = mod(x, y)
out.sum().backward()
self.assertEqual(x, torch.zeros_like(x))
self.assertEqual(x.grad, torch.zeros_like(x))
with self.assertRaisesRegex(RuntimeError, 'Expected'):
x = torch.randn(0, requires_grad=True, device=device, dtype=dtype)
y = torch.ones(10, device=device).type(torch.long)
mod(x, y)
with self.assertRaisesRegex(RuntimeError, 'Expected'):
x = torch.randn(10, 0, requires_grad=True, device=device, dtype=dtype)
y = torch.ones(10, 0, device=device).type(torch.long)
mod(x, y)
@onlyCUDA
def test_MarginLoss_warnings(self, device):
model = torch.nn.Linear(128, 22, device=device)
loss = torch.nn.MultiMarginLoss()
x = torch.rand((56, 128), device=device)
targets = torch.randint(22, (56,), device=device)
f = io.StringIO()
with contextlib.redirect_stderr(f):
out = model(x)
l = loss(out, targets)
l.backward()
self.assertTrue(len(f.getvalue()) == 0)
@onlyNativeDeviceTypes
def test_Unfold_empty(self, device):
inp = torch.randn(0, 3, 3, 4, device=device)
unfold = torch.nn.Unfold(kernel_size=(2, 3)).to(device)
_test_module_empty_input(self, unfold, inp, check_size=False)
with self.assertRaisesRegex(RuntimeError, 'Expected 3D or 4D'):
inp = torch.randn(3, 0, 3, 4, device=device)
unfold = torch.nn.Unfold(kernel_size=(2, 3)).to(device)
unfold(inp)
@onlyCUDA
@dtypes(torch.float, torch.double)
@tf32_on_and_off(0.005)
def test_rnn_fused(self, device, dtype):
def copy_rnn(rnn1, rnn2):
for x_layer, y_layer in zip(rnn1.all_weights, rnn2.all_weights):
for x, y in zip(x_layer, y_layer):
x.data.copy_(y.data)
def check_rnn_grads(rnn1, rnn2):
for x_layer, y_layer in zip(rnn1.all_weights, rnn2.all_weights):
for x, y in zip(x_layer, y_layer):
self.assertEqual(x.grad, y.grad, atol=5e-5, rtol=0)
input_size = 10
hidden_size = 6
num_layers = 2
seq_length = 7
batch = 6
input_val = torch.randn(seq_length, batch, input_size, dtype=dtype)
grad_output = torch.randn(seq_length, batch, hidden_size, dtype=dtype)
hx_val = torch.randn(num_layers, batch, hidden_size, dtype=dtype)
grad_hy = torch.randn(num_layers, batch, hidden_size, dtype=dtype)
with torch.backends.cudnn.flags(enabled=False, allow_tf32=None):
for module in (nn.GRU, nn.LSTM):
for bias in (True, False):
rnn = module(input_size, hidden_size, num_layers, bias=bias).to(dtype)
rnn_device = module(input_size, hidden_size, num_layers, bias=bias).to(device, dtype)
copy_rnn(rnn, rnn_device)
is_lstm = isinstance(rnn, nn.LSTM)
if is_lstm:
hx = (hx_val.clone().requires_grad_(True),
hx_val.clone().add(1).requires_grad_(True))
hx_device = (hx_val.clone().to(device).requires_grad_(True),
hx_val.clone().to(device).add(1).requires_grad_(True))
else:
hx = hx_val.clone().requires_grad_(True)
hx_device = hx_val.clone().to(device).requires_grad_(True)
inp = input_val.clone().requires_grad_(True)
inp_cu = input_val.clone().to(device).requires_grad_(True)
output1, hy1 = rnn(inp, hx)
output2, hy2 = rnn_device(inp_cu, hx_device)
if is_lstm:
torch.autograd.backward(
[output1, hy1[0], hy1[1]], [grad_output, grad_hy, grad_hy + 1]
)
torch.autograd.backward(
[output2, hy2[0], hy2[1]],
[grad_output.to(device), grad_hy.to(device), (grad_hy + 1).to(device)]
)
else:
torch.autograd.backward([output1, hy1], [grad_output, grad_hy])
torch.autograd.backward([output2, hy2], [grad_output.to(device), grad_hy.to(device)])
self.assertEqual(output1, output2)
self.assertEqual(hy1, hy2)
check_rnn_grads(rnn, rnn_device)
self.assertEqual(inp.grad, inp_cu.grad)
if is_lstm:
self.assertEqual(hx[0].grad, hx_device[0].grad)
self.assertEqual(hx[1].grad, hx_device[1].grad)
else:
self.assertEqual(hx.grad, hx_device.grad)
def test_BatchNorm_empty(self, device):
mod = torch.nn.BatchNorm2d(3).to(device)
inp = torch.randn(0, 3, 2, 2, device=device)
_test_module_empty_input(self, mod, inp)
if self.device_type == 'cuda' and self.has_cudnn():
with torch.backends.cudnn.flags(enabled=False):
_test_module_empty_input(self, mod, inp)
self.assertEqual(mod.running_mean, torch.tensor([0., 0, 0], device=device))
self.assertEqual(mod.running_var, torch.tensor([1., 1, 1], device=device))
self.assertEqual(mod.weight.grad, torch.tensor([0., 0, 0], device=device))
self.assertEqual(mod.bias.grad, torch.tensor([0., 0, 0], device=device))
@onlyCUDA
@largeTensorTest('16GB')
def test_prelu_backward_32bit_indexing(self, device):
m = torch.nn.PReLU().cuda().half()
input_ = torch.ones((1024, 1024, 1024, 2), dtype=torch.half, device=device)
output = m(input_)
output.backward(input_)
def test_linear_empty(self, device):
mod = torch.nn.Linear(7, 7).to(device)
inp = torch.randn(0, 7, device=device)
_test_module_empty_input(self, mod, inp)
def test_one_hot(self, device):
if self.device_type != 'cuda': # cuda throws device assert for invalid data
with self.assertRaises(RuntimeError):
torch.nn.functional.one_hot(torch.tensor([3, 4, -1, 0], device=device), -1)
with self.assertRaises(RuntimeError):
torch.nn.functional.one_hot(torch.tensor([3, 4, 1, 0], device=device), 3)
t = torch.nn.functional.one_hot(torch.tensor([3, 4, 1, 0], device=device))
expected = torch.tensor([[0, 0, 0, 1, 0],
[0, 0, 0, 0, 1],
[0, 1, 0, 0, 0],
[1, 0, 0, 0, 0]], device=device)
self.assertEqual(t, expected)
t = torch.nn.functional.one_hot(torch.tensor([3, 4, 1, 0], device=device), -1)
expected = torch.tensor([[0, 0, 0, 1, 0],
[0, 0, 0, 0, 1],
[0, 1, 0, 0, 0],
[1, 0, 0, 0, 0]], device=device)
self.assertEqual(t, expected)
t = torch.nn.functional.one_hot(torch.tensor([3, 4, 1, 0], device=device), 6)
expected = torch.tensor([[0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0]], device=device)
self.assertEqual(t, expected)
t = torch.nn.functional.one_hot(torch.tensor([[3, 4], [1, 0]], device=device))
expected = torch.tensor([[[0, 0, 0, 1, 0],
[0, 0, 0, 0, 1]],
[[0, 1, 0, 0, 0],
[1, 0, 0, 0, 0]]], device=device)
self.assertEqual(t, expected)
t = torch.nn.functional.one_hot(torch.tensor(4, device=device))
expected = torch.tensor([0, 0, 0, 0, 1], device=device)
self.assertEqual(t, expected)
t = torch.nn.functional.one_hot(torch.empty([4, 0], dtype=torch.long, device=device), 100)
expected = torch.empty([4, 0, 100], dtype=torch.long)
self.assertEqual(t, expected)
with self.assertRaises(RuntimeError):
torch.nn.functional.one_hot(torch.empty([4, 0], dtype=torch.long, device=device))
with self.assertRaises(RuntimeError):
torch.nn.functional.one_hot(torch.tensor([3, 4, 1, 0], device=device), -2)
def test_nn_empty(self, device):
# One off tests to ensure scalars from nn.yaml are properly applied
def verify_scalars(input, output):
self.assertEqual(input.shape, output.shape)
self.assertEqual(0, output.numel())
for input_shape in [(0), (0, 2)]:
for module in [torch.nn.ELU, torch.nn.Hardtanh, torch.nn.LeakyReLU, torch.nn.LogSigmoid,
torch.nn.RReLU, torch.nn.Softshrink, torch.nn.Softplus, torch.nn.Sigmoid,
torch.nn.Tanh]:
input = torch.randn(input_shape, device=device, requires_grad=True)
m = module()
output = m(input)
verify_scalars(input, output)
def test_nn_scalars(self, device):
# One off tests to ensure scalars from nn.yaml are properly applied
def verify_scalars(input, output):
if input.dim() == 0:
self.assertEqual((), output.shape)
else:
self.assertNotEqual((), output.shape)
output.sum().backward()
self.assertEqual(input.shape, input.grad.shape)
for input_shape in [(5, 6), ()]:
for module in [torch.nn.ELU, torch.nn.Hardtanh, torch.nn.LeakyReLU, torch.nn.LogSigmoid,
torch.nn.RReLU, torch.nn.Softshrink, torch.nn.Softplus, torch.nn.Sigmoid,
torch.nn.Tanh]:
input = torch.randn(input_shape, device=device, requires_grad=True)
m = module()
output = m(input)
verify_scalars(input, output)
def test_nn_scalars_reductions(self, device):
# One off tests to ensure scalars from nn.yaml are properly applied
def verify_reduction_scalars(input, reduction, output):
if reduction != 'none' or input.dim() == 0:
self.assertEqual((), output.shape)
else:
self.assertNotEqual((), output.shape)
output.sum().backward()
self.assertEqual(input.shape, input.grad.shape)
for input_shape in [(5, 6), ()]:
for reduction in ['none', 'mean', 'sum']:
for module in [torch.nn.BCELoss, torch.nn.L1Loss, torch.nn.MSELoss,
torch.nn.SmoothL1Loss, torch.nn.SoftMarginLoss]:
input = torch.randn(input_shape, device=device, requires_grad=True)
target = torch.empty(input_shape, device=device).random_(2)
sigmoid = nn.Sigmoid()
input = torch.randn(input_shape, device=device, requires_grad=True)
m = module(reduction=reduction)
output = m(sigmoid(input), target)
verify_reduction_scalars(input, reduction, output)
# verify that bogus reduction strings are errors
@onlyNativeDeviceTypes
def test_invalid_reduction_strings(self, device):
input = torch.randn(3, 5, requires_grad=True, device=device)
cinput = torch.randn(3, 5, requires_grad=True, device=device, dtype=torch.cfloat)
target = torch.tensor([1, 0, 4], device=device)
var = torch.ones(size=input.size(), requires_grad=True, device=device)
for reduction in ['none', 'invalid']:
def v(fn):
if reduction == 'invalid':
self.assertRaises(ValueError, lambda: fn())
else:
fn()
v(lambda: F.nll_loss(input, target, reduction=reduction))
v(lambda: F.cross_entropy(input, target, reduction=reduction))
v(lambda: F.kl_div(input, input, reduction=reduction))
v(lambda: F.huber_loss(input, input, reduction=reduction))
v(lambda: F.smooth_l1_loss(input, input, reduction=reduction))
v(lambda: F.l1_loss(input, input, reduction=reduction))
v(lambda: F.l1_loss(cinput, cinput, reduction=reduction))
v(lambda: F.mse_loss(input, input, reduction=reduction))
v(lambda: F.hinge_embedding_loss(input, input, reduction=reduction))
v(lambda: F.poisson_nll_loss(input, input, reduction=reduction))
v(lambda: F.gaussian_nll_loss(input, input, var, reduction=reduction))
v(lambda: F.binary_cross_entropy(torch.sigmoid(input), input.gt(0).to(torch.get_default_dtype()), reduction=reduction))
v(lambda: F.binary_cross_entropy_with_logits(input, input, reduction=reduction))
zeros = torch.zeros_like(input).to(torch.int64)
v(lambda: F.multilabel_soft_margin_loss(input, zeros, reduction=reduction))
v(lambda: F.triplet_margin_loss(input, input, input, reduction=reduction))
v(lambda: F.triplet_margin_with_distance_loss(input, input, input, reduction=reduction))
v(lambda: F.margin_ranking_loss(input, input, input.sign(), reduction=reduction))
v(lambda: F.cosine_embedding_loss(input, input, input[:, 0].sign(), reduction=reduction))
log_probs = torch.randn(50, 16, 20, requires_grad=True, device=device).log_softmax(2)
targets = torch.randint(1, 20, (16, 30), dtype=torch.long, device=device)
input_lengths = torch.full((16,), 50, dtype=torch.long, device=device)
target_lengths = torch.randint(10, 30, (16,), dtype=torch.long, device=device)
v(lambda: F.ctc_loss(log_probs, targets, input_lengths, target_lengths, reduction=reduction))
# FIXME: should we allow derivatives on these?
v(lambda: F.soft_margin_loss(input, input.sign().detach(), reduction=reduction))
@onlyNativeDeviceTypes
def test_smooth_l1_loss_vs_huber_loss(self, device):
def _make_test_tensor(shape, contiguous=True):
if contiguous:
test_tensor = torch.randn(shape, device=device)
else:
# Select every other element in the innermost dimension to
# make it non-contiguous.
doubled_shape = list(shape)
doubled_shape[-1] *= 2
test_tensor = torch.randn(doubled_shape, device=device)
test_tensor = test_tensor[..., ::2]
return test_tensor
def _test_smooth_l1_loss_vs_huber_loss_helper(input, target, beta, require_equal):
for reduction in ['mean', 'sum', 'none']:
smooth_l1 = torch.nn.SmoothL1Loss(beta=beta, reduction=reduction)
# beta hyper-parameter is called delta for Huber
huber = torch.nn.HuberLoss(delta=beta, reduction=reduction)
smooth_l1_loss = smooth_l1(input, target)
huber_loss = huber(input, target)
if require_equal:
self.assertEqual(smooth_l1_loss, huber_loss)
else:
# Huber loss should be larger than smooth L1 loss by a factor of beta.
self.assertEqual(smooth_l1_loss * beta, huber_loss)
def _test_smooth_l1_loss_vs_huber_loss_multi_input_helper(beta, require_equal):
# Test the non-vectorized case.
shape = (2, 2)
_test_smooth_l1_loss_vs_huber_loss_helper(input=_make_test_tensor(shape),
target=_make_test_tensor(shape),
beta=beta,
require_equal=require_equal)
# Test the vectorized case (innermost dim > 32).
shape = (64, 64)
_test_smooth_l1_loss_vs_huber_loss_helper(input=_make_test_tensor(shape),
target=_make_test_tensor(shape),
beta=beta,
require_equal=require_equal)
# Test the non-contiguous case.
_test_smooth_l1_loss_vs_huber_loss_helper(input=_make_test_tensor(shape, contiguous=False),
target=_make_test_tensor(shape, contiguous=False),
beta=beta,
require_equal=require_equal)
def test_equal_when_beta_is_one():
_test_smooth_l1_loss_vs_huber_loss_multi_input_helper(beta=1.0, require_equal=True)
def test_unequal_when_beta_is_less_than_one():
_test_smooth_l1_loss_vs_huber_loss_multi_input_helper(beta=0.5, require_equal=False)
def test_unequal_when_beta_is_greater_than_one():
_test_smooth_l1_loss_vs_huber_loss_multi_input_helper(beta=1.5, require_equal=False)
test_equal_when_beta_is_one()
test_unequal_when_beta_is_less_than_one()
test_unequal_when_beta_is_greater_than_one()
@onlyCPU
def test_smooth_l1_loss_bfloat16(self, device):
def test_dtype(fn, input, target, dtype):
input = input.detach().clone().to(dtype=dtype).requires_grad_(True)
input2 = input.detach().clone().float().requires_grad_(True)
target = target.detach().clone().to(dtype=dtype)
target2 = target.detach().clone().float()
out = fn(input, target)
out.sum().backward()
out2 = fn(input2, target2)
out2.sum().backward()
self.assertEqual(out.dtype, dtype)
self.assertEqual(input.grad.dtype, dtype)
self.assertEqual(out, out2, exact_dtype=False)
self.assertEqual(input.grad, input2.grad, exact_dtype=False)
def func(device):
return nn.SmoothL1Loss().to(device=device)
shapes = [[1, 3, 1, 6], [1, 3, 1, 128], [1, 3, 128, 128]]
for shape in shapes:
x = torch.randn(shape, device=device, requires_grad=True)
t = torch.randn(shape, device=device)
test_dtype(func(device), x, t, torch.bfloat16)
# We don't want to make propagating NaN a hard requirement on ops, but for
# these easy ones, we should make them do so.
def test_nonlinearity_propagate_nan(self, device):
def test(nonlinearity, *args, **kwargs):
x = torch.tensor([nan], device=device)
fn = getattr(F, nonlinearity)
try:
self.assertTrue(math.isnan(fn(x, *args, **kwargs).item()))
except Exception as e:
if 'not implemented' not in str(e):
raise
test('relu')
test('relu', inplace=True)
test('relu6')
test('elu')
test('selu')
test('celu')
test('rrelu')
test('rrelu', inplace=True)
test('hardtanh')
test('tanh')
test('sigmoid')
test('logsigmoid')
test('hardshrink')
test('tanhshrink')
test('softsign')
test('softmin', 0)
test('softmax', 0)
test('log_softmax', 0)
test('leaky_relu', 0.2)
test('threshold', 3, 2)
test('threshold', 3, 2, inplace=True)
@parametrize_test("mode", ["nearest-exact", "nearest"])
def test_upsamplingNearest1d(self, device, mode):
# Forward AD does not support XLA because XLA tensors don't have storage
check_forward_ad = torch.device(device).type != 'xla'
m = nn.Upsample(size=4, mode=mode)
in_t = torch.ones(1, 1, 2, device=device, dtype=torch.double)
in_uint8_t = torch.ones(1, 1, 2, dtype=torch.uint8, device=device)
with warnings.catch_warnings(record=True) as w:
out_t = m(in_t)
out_uint8_t = m(in_uint8_t)
self.assertEqual(torch.ones(1, 1, 4, device=device, dtype=torch.double), out_t.data)
self.assertEqual(torch.ones(1, 1, 4, dtype=torch.uint8, device=device), out_uint8_t.data)
# Checks upsampling
input = torch.randn(1, 1, 2, requires_grad=True, device=device, dtype=torch.double)
gradcheck(lambda x: F.interpolate(x, 4, mode=mode), [input], check_forward_ad=check_forward_ad)
gradgradcheck(lambda x: F.interpolate(x, 4, mode=mode), [input], check_fwd_over_rev=check_forward_ad)
# Checks downsampling
input = torch.randn(1, 1, 20, requires_grad=True, device=device, dtype=torch.double)
gradcheck(lambda x: F.interpolate(x, 11, mode=mode), [input], check_forward_ad=check_forward_ad)
gradgradcheck(lambda x: F.interpolate(x, 4, mode=mode), [input], check_fwd_over_rev=check_forward_ad)
# consistency CUDA/CPU check
if torch.device(device).type == 'cuda':
input_cuda = torch.randn(1, 1, 20, device=device, dtype=torch.double)
input_cpu = input_cuda.cpu()
output_cuda = F.interpolate(input_cuda, 4, mode=mode)
output_cpu = F.interpolate(input_cpu, 4, mode=mode)
self.assertEqual(output_cuda.cpu(), output_cpu)
output_cuda = F.interpolate(input_cuda, 24, mode=mode)
output_cpu = F.interpolate(input_cpu, 24, mode=mode)
self.assertEqual(output_cuda.cpu(), output_cpu)
@parametrize_test("isize, osize", [(20, 11), (10, 15)])
def test_upsamplingNearest1d_correctness(self, device, isize, osize):
# Here we check if output matches OpenCV's INTER_NEAREST-like result
in_t = torch.arange(isize, dtype=torch.float, device=device).unsqueeze(0).unsqueeze(0)
out_t = F.interpolate(
in_t, size=(osize, ), recompute_scale_factor=False, mode="nearest"
)
# compute expected output as OpenCV
expected_out = torch.zeros(osize, dtype=torch.float).unsqueeze(0).unsqueeze(0)
scale = 1.0 * isize / osize
for o in range(osize):
i_f32 = o * scale
i = int(i_f32)
expected_out[0, 0, o] = in_t[0, 0, i]
expected_out = expected_out.to(device=device)
self.assertEqual(out_t, expected_out)
def test_upsamplingNearestExact1d_rescale(self, device):
# Checks https://github.com/pytorch/pytorch/issues/62237
isize = 20
in_t = torch.arange(isize, dtype=torch.float, device=device).unsqueeze(0).unsqueeze(0)
# for s in [1.00001, 0.99999]: # 0.9999 case is broken
# See issue: https://github.com/pytorch/pytorch/issues/62396
for s in [1.00001, ]:
out_t = F.interpolate(
in_t, scale_factor=s, recompute_scale_factor=False, mode="nearest-exact"
)
expected_out = in_t
self.assertEqual(out_t, expected_out, msg=f"scale: {s}")
# checks data duplication if output_size == 2 * input_size
# for s in [2.00001, 1.99999]: # 1.99999 case is broken
# See issue: https://github.com/pytorch/pytorch/issues/62396
for s in [2.00001, ]:
out_t = F.interpolate(
in_t, scale_factor=s, recompute_scale_factor=False, mode="nearest-exact"
)
# input is [[[0, 1, 2, 3, ..., 9]]]
# expected out is [[[0, 0, 1, 1, 2, 2, ..., 9, 9]]]
expected_out = in_t.repeat_interleave(2, dim=-1)
self.assertEqual(out_t, expected_out)
@parametrize_test("isize, osize", [(20, 11), (10, 15)])
def test_upsamplingNearestExact1d_correctness(self, device, isize, osize):
# Here we check if output matches Scikit-Image/Scipy-like result
# Checks https://github.com/pytorch/pytorch/issues/34808
in_t = torch.arange(isize, dtype=torch.float, device=device).unsqueeze(0).unsqueeze(0)
out_t = F.interpolate(
in_t, size=(osize, ), recompute_scale_factor=False, mode="nearest-exact"
)
# compute expected output as scikit-image/scipy
expected_out = torch.zeros(osize, dtype=torch.float).unsqueeze(0).unsqueeze(0)
scale = 1.0 * isize / osize
for o in range(osize):
i_f32 = (o + 0.5) * scale
i = int(i_f32)
expected_out[0, 0, o] = in_t[0, 0, i]
expected_out = expected_out.to(device=device)
self.assertEqual(out_t, expected_out)
@parametrize_test("memory_format", [torch.contiguous_format, torch.channels_last])
@parametrize_test("mode", ["nearest", "nearest-exact"])
def test_upsamplingNearest2d(self, device, memory_format, mode):
# Forward AD does not support XLA because XLA tensors don't have storage
check_forward_ad = torch.device(device).type != 'xla'
in_t = torch.ones(1, 2, 2, 2, device=device, dtype=torch.double).contiguous(memory_format=memory_format)
in_uint8_t = torch.ones(1, 2, 2, 2, dtype=torch.uint8, device=device).contiguous(memory_format=memory_format)
with warnings.catch_warnings(record=True) as w:
out_t = F.interpolate(in_t, size=4, mode=mode)
out_uint8_t = F.interpolate(in_uint8_t, size=4, mode=mode)
self.assertEqual(len(w), 0)
self.assertEqual(torch.ones(1, 2, 4, 4, device=device, dtype=torch.double), out_t)
self.assertEqual(torch.ones(1, 2, 4, 4, dtype=torch.uint8, device=device), out_uint8_t)
# Assert that memory format is carried through to the output
self.assertTrue(out_t.is_contiguous(memory_format=memory_format))
# test forward when input's height is not same as width
in_t = torch.ones(1, 2, 2, 1, device=device, dtype=torch.double).contiguous(memory_format=memory_format).requires_grad_()
out_t = F.interpolate(in_t, size=(4, 2), mode=mode)
self.assertEqual(torch.ones(1, 2, 4, 2, device=device, dtype=torch.double), out_t)
self.assertTrue(out_t.is_contiguous(memory_format=memory_format))
out_t.backward(torch.randn_like(out_t))
self.assertTrue(in_t.grad.is_contiguous(memory_format=memory_format))
# test backward when input's height is not same as width
input = torch.ones(
1, 2, 2, 1, requires_grad=True, device=device,
dtype=torch.double).contiguous(memory_format=memory_format)
gradcheck(lambda x: F.interpolate(x, size=(4, 2), mode=mode), [input], check_forward_ad=check_forward_ad)
gradgradcheck(lambda x: F.interpolate(x, size=(4, 2), mode=mode), [input], check_fwd_over_rev=check_forward_ad)
input = torch.randn(
1, 2, 2, 2, requires_grad=True, device=device,
dtype=torch.double).contiguous(memory_format=memory_format)
self.assertEqual(
F.interpolate(input, 4, mode=mode),
F.interpolate(input, scale_factor=2, mode=mode))
gradcheck(lambda x: F.interpolate(x, 4, mode=mode), [input], check_forward_ad=check_forward_ad)
gradgradcheck(lambda x: F.interpolate(x, 4, mode=mode), [input], check_fwd_over_rev=check_forward_ad)
# Assert that cpu and cuda handle channels_last memory format in the same way
# https://github.com/pytorch/pytorch/issues/54590
if torch.device(device).type == 'cuda':
for shapes, scale_factor in product([
(2, 2, 3, 4), (2, 3, 4, 5), (3, 1, 2, 2), (1, 5, 3, 2)
], [0.5, 1.5, 2]):
a_cuda = torch.randn(
*shapes, device=device,
dtype=torch.double).contiguous(memory_format=memory_format).requires_grad_()
a_cpu = a_cuda.detach().cpu().requires_grad_()
out_cuda = F.interpolate(a_cuda, scale_factor=scale_factor, mode=mode)
out_cpu = F.interpolate(a_cpu, scale_factor=scale_factor, mode=mode)
self.assertEqual(out_cpu.cuda(), out_cuda)
g_cuda = torch.randn_like(out_cuda)
g_cpu = g_cuda.cpu()
out_cuda.backward(g_cuda)
out_cpu.backward(g_cpu)
self.assertEqual(a_cuda.grad, a_cpu.grad)
@parametrize_test("memory_format", [torch.contiguous_format, torch.channels_last])
@parametrize_test("isize, osize", [(20, 11), (10, 15)])
def test_upsamplingNearest2d_correctness(self, device, memory_format, isize, osize):
# Here we check if output matches OpenCV's INTER_NEAREST-like result
in_t = torch.arange(isize * isize, dtype=torch.float, device=device).reshape(1, 1, isize, isize)
in_t = in_t.contiguous(memory_format=memory_format)
out_t = F.interpolate(
in_t, size=(osize, osize), recompute_scale_factor=False, mode="nearest"
)
# compute expected output as OpenCV
expected_out = torch.zeros(1, 1, osize, osize, dtype=torch.float)
scale = 1.0 * isize / osize
for o1 in range(osize):
i1_f32 = o1 * scale
i1 = int(i1_f32)
for o2 in range(osize):
i2_f32 = o2 * scale
i2 = int(i2_f32)
expected_out[0, 0, o1, o2] = in_t[0, 0, i1, i2]
expected_out = expected_out.to(device=device)
self.assertEqual(out_t, expected_out)
@parametrize_test("memory_format", [torch.contiguous_format, torch.channels_last])
@parametrize_test("isize, osize", [(20, 11), (10, 15)])
def test_upsamplingNearestExact2d_correctness(self, device, memory_format, isize, osize):
# Here we check if output matches Scikit-Image/Scipy-like result
# Checks https://github.com/pytorch/pytorch/issues/34808
in_t = torch.arange(isize * isize, dtype=torch.float, device=device).reshape(1, 1, isize, isize)
in_t = in_t.contiguous(memory_format=memory_format)
out_t = F.interpolate(
in_t, size=(osize, osize), recompute_scale_factor=False, mode="nearest-exact"
)
# compute expected output as Scikit-Image/Scipy
expected_out = torch.zeros(1, 1, osize, osize, dtype=torch.float)
scale = 1.0 * isize / osize
for o1 in range(osize):
i1_f32 = (o1 + 0.5) * scale
i1 = int(i1_f32)
for o2 in range(osize):
i2_f32 = (o2 + 0.5) * scale
i2 = int(i2_f32)
expected_out[0, 0, o1, o2] = in_t[0, 0, i1, i2]
expected_out = expected_out.to(device=device)
self.assertEqual(out_t, expected_out)
@parametrize_test("memory_format", [torch.contiguous_format, torch.channels_last_3d])
@parametrize_test("mode", ["nearest", "nearest-exact"])
def test_upsamplingNearest3d(self, device, memory_format, mode):
# Forward AD does not support XLA because XLA tensors don't have storage
check_forward_ad = torch.device(device).type != 'xla'
m = nn.Upsample(size=4, mode=mode)
in_t = torch.ones(1, 2, 2, 2, 2, device=device, dtype=torch.double).contiguous(memory_format=memory_format).requires_grad_()
in_uint8_t = torch.ones(
1, 2, 2, 2, 2, dtype=torch.uint8, device=device
).contiguous(memory_format=memory_format)
with warnings.catch_warnings(record=True) as w:
out_t = m(in_t)
out_uint8_t = m(in_uint8_t)
expected_output = torch.ones(1, 2, 4, 4, 4, device=device, dtype=torch.double)
self.assertEqual(expected_output, out_t)
self.assertEqual(expected_output.to(torch.uint8), out_uint8_t)
# Assert that memory format is carried through to the output
self.assertTrue(out_t.is_contiguous(memory_format=memory_format))
out_t.backward(torch.randn_like(out_t))
self.assertTrue(in_t.grad.is_contiguous(memory_format=memory_format))
input = torch.randn(
1, 2, 2, 2, 2, requires_grad=True, device=device, dtype=torch.double
).contiguous(memory_format=memory_format)
gradcheck(lambda x: F.interpolate(x, 4, mode=mode), [input], check_forward_ad=check_forward_ad)
gradgradcheck(lambda x: F.interpolate(x, 4, mode=mode), [input], check_fwd_over_rev=check_forward_ad)
# Assert that cpu and cuda handle channels_last memory format in the same way
# https://github.com/pytorch/pytorch/issues/54590
if torch.device(device).type == 'cuda':
a = torch.ones(
2, 2, 2, 3, 4, device=device, requires_grad=True, dtype=torch.double
).contiguous(memory_format=torch.channels_last_3d)
# make the data asymmetric; ensure that cuda/cpu handle channels_last appropriately.
a[1][1][1][2][2] = a[1][1][1][2][3] = 0
out_cuda = torch.nn.functional.interpolate(a, scale_factor=2, mode=mode)
out_cpu = torch.nn.functional.interpolate(a.to('cpu'), scale_factor=2, mode=mode)
self.assertEqual(out_cpu, out_cuda.to('cpu'))
gradcheck(lambda x: F.interpolate(x, 4, mode=mode), [a], check_forward_ad=check_forward_ad)
gradgradcheck(lambda x: F.interpolate(x, 4, mode=mode), [a], check_fwd_over_rev=check_forward_ad)
gradcheck(lambda x: F.interpolate(x, 4, mode=mode), [a.to('cuda')], check_forward_ad=check_forward_ad)
gradgradcheck(lambda x: F.interpolate(x, 4, mode=mode), [a.to('cuda')], check_fwd_over_rev=check_forward_ad)
@parametrize_test("memory_format", [torch.contiguous_format, torch.channels_last_3d])
@parametrize_test("isize, osize", [(20, 11), (10, 15)])
def test_upsamplingNearest3d_correctness(self, device, memory_format, isize, osize):
# Here we check if output matches OpenCV's INTER_NEAREST-like result
in_t = torch.arange(isize * isize * isize, dtype=torch.float, device=device)
in_t = in_t.reshape(1, 1, isize, isize, isize)
in_t = in_t.contiguous(memory_format=memory_format)
out_t = F.interpolate(
in_t, size=(osize, osize, osize), recompute_scale_factor=False, mode="nearest"
)
# compute expected output as OpenCV
expected_out = torch.zeros(1, 1, osize, osize, osize, dtype=torch.float)
scale = 1.0 * isize / osize
for o1 in range(osize):
i1_f32 = o1 * scale
i1 = int(i1_f32)
for o2 in range(osize):
i2_f32 = o2 * scale
i2 = int(i2_f32)
for o3 in range(osize):
i3_f32 = o3 * scale
i3 = int(i3_f32)
expected_out[0, 0, o1, o2, o3] = in_t[0, 0, i1, i2, i3]
expected_out = expected_out.to(device=device)
self.assertEqual(out_t, expected_out)
@parametrize_test("memory_format", [torch.contiguous_format, torch.channels_last_3d])
@parametrize_test("isize, osize", [(20, 11), (10, 15)])
def test_upsamplingNearestExact3d_correctness(self, device, memory_format, isize, osize):
# Here we check if output matches Scikit-Image/Scipy-like result
# Checks https://github.com/pytorch/pytorch/issues/34808
in_t = torch.arange(isize * isize * isize, dtype=torch.float, device=device)
in_t = in_t.reshape(1, 1, isize, isize, isize)
in_t = in_t.contiguous(memory_format=memory_format)
out_t = F.interpolate(
in_t, size=(osize, osize, osize), recompute_scale_factor=False, mode="nearest-exact"
)
# compute expected output as Scikit-Image/Scipy
expected_out = torch.zeros(1, 1, osize, osize, osize, dtype=torch.float)
scale = 1.0 * isize / osize
for o1 in range(osize):
i1_f32 = (o1 + 0.5) * scale
i1 = int(i1_f32)
for o2 in range(osize):
i2_f32 = (o2 + 0.5) * scale
i2 = int(i2_f32)
for o3 in range(osize):
i3_f32 = (o3 + 0.5) * scale
i3 = int(i3_f32)
expected_out[0, 0, o1, o2, o3] = in_t[0, 0, i1, i2, i3]
expected_out = expected_out.to(device=device)
self.assertEqual(out_t, expected_out)
@parametrize_test("antialias", [True, False])
@parametrize_test("align_corners", [True, False])
@parametrize_test("mode", ["bilinear", "bicubic"])
@parametrize_test("memory_format", [torch.contiguous_format, torch.channels_last])
@onlyNativeDeviceTypes
def test_upsamplingBiMode2d(self, device, antialias, align_corners, mode, memory_format):
# Forward AD does not support XLA because XLA tensors don't have storage
check_forward_ad = torch.device(device).type != 'xla'
kwargs = dict(mode=mode, align_corners=align_corners, antialias=antialias)
# test float scale factor up & downsampling
for scale_factor in [0.5, 1.5, 2]:
in_t = torch.ones(
2, 3, 8, 8, device=device,
dtype=torch.double).contiguous(memory_format=memory_format).requires_grad_()
out_size = int(math.floor(in_t.shape[-1] * scale_factor))
with warnings.catch_warnings(record=True) as w:
out_t = F.interpolate(in_t, scale_factor=scale_factor, **kwargs)
expected_out = torch.ones(2, 3, out_size, out_size, device=device, dtype=torch.double)
self.assertEqual(expected_out, out_t)
# Assert that memory format is carried through to the output
self.assertTrue(out_t.is_contiguous(memory_format=memory_format))
out_t.backward(torch.randn_like(out_t))
self.assertTrue(in_t.grad.is_contiguous(memory_format=memory_format))
if torch.device(device).type == 'cuda':
# Bilinear backward is nondeterministic because of atomicAdd usage
nondet_tol = 1e-5
else:
nondet_tol = 0.0
input = torch.randn(
2, 3, 8, 8, device=device,
dtype=torch.double).contiguous(memory_format=memory_format).requires_grad_()
gradcheck(
lambda x: F.interpolate(x, out_size, **kwargs),
[input],
check_forward_ad=check_forward_ad, nondet_tol=nondet_tol
)
gradgradcheck(
lambda x: F.interpolate(x, out_size, **kwargs),
[input],
check_fwd_over_rev=check_forward_ad, nondet_tol=nondet_tol
)
# Assert that cpu and cuda give same results
if torch.device(device).type == 'cuda':
for shapes in [
(2, 2, 3, 4), (2, 3, 4, 5), (3, 1, 2, 2), (1, 5, 3, 2)
]:
a_cuda = torch.randn(
*shapes, device=device, dtype=torch.double
).contiguous(memory_format=memory_format).requires_grad_()
a_cpu = a_cuda.detach().cpu().requires_grad_()
with warnings.catch_warnings(record=True):
out_cuda = F.interpolate(a_cuda, scale_factor=scale_factor, **kwargs)
out_cpu = F.interpolate(a_cpu, scale_factor=scale_factor, **kwargs)
self.assertEqual(out_cpu, out_cuda.cpu())
g_cuda = torch.randn_like(out_cuda)
g_cpu = g_cuda.cpu()
out_cuda.backward(g_cuda)
out_cpu.backward(g_cpu)
self.assertEqual(a_cuda.grad, a_cpu.grad)
@parametrize_test("antialias", [True, False])
@parametrize_test("num_channels", [3, 5])
@parametrize_test("mode", ["nearest", "nearest-exact", "bilinear", "bicubic"])
@parametrize_test("dtype", integral_types() + floating_types())
@onlyNativeDeviceTypes
def test_upsamplingBiMode2d_nonsupported_dtypes(self, device, antialias, num_channels, mode, dtype):
x = torch.ones(1, num_channels, 32, 32, dtype=dtype, device=device)
should_raise_runtime_error = True
if "nearest" in mode:
if antialias:
raise SkipTest("Nearest mode does not have antialiasing")
if dtype in (torch.uint8, ) + floating_types():
should_raise_runtime_error = False
elif mode in ("bilinear", "bicubic"):
if dtype in floating_types() or (device == "cpu" and dtype == torch.uint8):
should_raise_runtime_error = False
if should_raise_runtime_error:
with self.assertRaisesRegex(RuntimeError, "not implemented for"):
F.interpolate(x, (12, 12), mode=mode, antialias=antialias)
else:
_ = F.interpolate(x, (12, 12), mode=mode, antialias=antialias)
@parametrize_test("memory_format", [torch.contiguous_format, torch.channels_last])
def test_upsamplingBilinear2d_aa_correctness(self, device, memory_format):
t_in = torch.arange(3 * 8 * 8, dtype=torch.float, device=device).reshape(1, 3, 8, 8)
t_in = t_in.contiguous(memory_format=memory_format)
# This expected result is obtain using PIL.Image.resize
# for c in range(3):
# a_in = t_in.numpy()[0, c, ...]
# pil_in = Image.fromarray(a_in)
# pil_out = pil_in.resize((2, 2), resample=Image.LINEAR)
expected_out = torch.tensor([
17.035713, 20.25, 42.75, 45.964287, 81.03572, 84.25,
106.75, 109.96428, 145.0357, 148.25, 170.75, 173.9643
], device=device, dtype=t_in.dtype).reshape(1, 3, 2, 2)
t_out = F.interpolate(t_in, size=(2, 2), mode="bilinear", align_corners=False, antialias=True)
self.assertEqual(expected_out, t_out)
@parametrize_test("memory_format", [torch.contiguous_format, torch.channels_last])
@parametrize_test("mode", ["bilinear", "bicubic"])
@parametrize_test("antialias", [True, False])
@parametrize_test("align_corners", [True, False])
@parametrize_test("num_channels", [3, 5])
@parametrize_test("output_size", [32, 600])
@parametrize_test("check_as_unsqueezed_3d_tensor", [True, False])
@parametrize_test("non_contig", [False, "sliced", "restrided"])
@parametrize_test("batch_size", [1, 5])
def test_upsamplingBiMode2d_consistency(
self,
device,
memory_format,
mode,
antialias,
align_corners,
num_channels,
output_size,
check_as_unsqueezed_3d_tensor,
non_contig,
batch_size,
):
# Check output value consistency between resized_input_uint8 and resized input_float
if torch.device(device).type == "cuda":
raise SkipTest("CUDA implementation is not yet supporting uint8")
torch.manual_seed(0)
input_ui8 = torch.randint(0, 256, size=(batch_size, num_channels, 400, 400), dtype=torch.uint8, device=device)
input_ui8 = input_ui8.contiguous(memory_format=memory_format)
if non_contig == "sliced":
input_ui8 = input_ui8[:, :, 10:-10, 10:-10]
elif non_contig == "restrided":
input_ui8 = input_ui8[:, :, ::2, ::2]
if batch_size == 1 and check_as_unsqueezed_3d_tensor:
input_ui8 = input_ui8[0, ...]
input_ui8 = input_ui8[None, ...]
input_f32 = input_ui8.float()
output_f32 = F.interpolate(
input_f32, size=(output_size, output_size), mode=mode, align_corners=align_corners, antialias=antialias
).round().clip(0, 255)
output_ui8 = F.interpolate(
input_ui8, size=(output_size, output_size), mode=mode, align_corners=align_corners, antialias=antialias
)
if non_contig is False:
self.assertTrue(input_ui8.is_contiguous(memory_format=memory_format))
# FIXME if-clause shows the current behaviour which is definitely unexpected.
# Ideally we want to fix it such that both the ui8 and f32 outputs are also channels_last
# See for more details: https://github.com/pytorch/pytorch/pull/100373
if batch_size == 1 and check_as_unsqueezed_3d_tensor and memory_format == torch.channels_last:
self.assertTrue(output_ui8.is_contiguous())
self.assertTrue(output_f32.is_contiguous())
else:
self.assertTrue(output_ui8.is_contiguous(memory_format=memory_format))
self.assertTrue(output_f32.is_contiguous(memory_format=memory_format))
diff = (output_f32 - output_ui8.float()).abs()
if mode == "bilinear":
torch.testing.assert_close(output_f32, output_ui8.float(), rtol=0, atol=1)
else:
# - tolerances for bicubic mode are in general higher than for
# bilinear mode, because the bicubic kernel may create
# [intermediate] values outside of the [0, 255] range, which need
# to be clipped in uint8 path, but not in float path. This isn't
# an issue with bilinear kernel.
# - Also in bicubic mode, when antialias=False, we have to use
# bigger tolerances than when antialias=True. This is partially
# due to the fact that when False, the float path uses the -0.75
# constant while the uint8 path uses the -0.5 constant in the
# bicubic kernel (when True, they both use -0.5). This difference
# in constants exists for historical reasons. Should both paths
# use the -0.5 constant, we would have closer results and we would
# be able to lower the tolerances.
max_diff = 30 if antialias else 44
assert diff.max() < max_diff
threshold = 2
percent = 3 if antialias else 40
assert (diff > threshold).float().mean() < (percent / 100)
threshold = 5
percent = 1 if antialias else 20
assert (diff > threshold).float().mean() < (percent / 100)
mae = .4 if antialias else 3
assert diff.mean() < mae
@parametrize_test("memory_format", [torch.contiguous_format, torch.channels_last])
@parametrize_test("align_corners", [True, False])
@parametrize_test("input_size, output_size", [(399, 437), (403, 377)])
def test_upsamplingBiLinear2d_consistency_interp_size_bug(self, device, memory_format, align_corners, input_size, output_size):
# Non-regression test for https://github.com/pytorch/pytorch/pull/101403
if torch.device(device).type == "cuda":
raise SkipTest("CUDA implementation is not yet supporting uint8")
mode = "bilinear"
input_ui8 = torch.randint(0, 256, size=(1, 3, input_size, input_size), dtype=torch.uint8, device=device)
input_ui8 = input_ui8.contiguous(memory_format=memory_format)
input_f32 = input_ui8.float()
output_f32 = F.interpolate(
input_f32, size=(output_size, output_size), mode=mode, align_corners=align_corners, antialias=False
).round().to(torch.uint8)
output_ui8 = F.interpolate(
input_ui8, size=(output_size, output_size), mode=mode, align_corners=align_corners, antialias=False
)
torch.testing.assert_close(output_f32, output_ui8, atol=1, rtol=0)
def test_upsamplingBicubic2d_correctness(self, device):
# test output against known input: align_corners=False result must match opencv
in_t = torch.arange(8., device=device).view(1, 2, 2, 2)
expected_out_t = torch.tensor(
[[[[-0.31641, 0.01562, 0.56250, 0.89453],
[0.34766, 0.67969, 1.22656, 1.55859],
[1.44141, 1.77344, 2.32031, 2.65234],
[2.10547, 2.43750, 2.98438, 3.31641]],
[[3.68359, 4.01562, 4.56250, 4.89453],
[4.34766, 4.67969, 5.22656, 5.55859],
[5.44141, 5.77344, 6.32031, 6.65234],
[6.10547, 6.43750, 6.98438, 7.31641]]]], device=device)
out_t = F.interpolate(in_t, scale_factor=2, mode='bicubic', align_corners=False)
torch.set_printoptions(precision=5)
self.assertEqual(out_t, expected_out_t, atol=1e-5, rtol=0)
@parametrize_test("memory_format", [torch.contiguous_format, torch.channels_last])
def test_upsamplingBicubic2d_aa_correctness(self, device, memory_format):
t_in = torch.arange(3 * 8 * 8, dtype=torch.float, device=device).reshape(1, 3, 8, 8)
t_in = t_in.contiguous(memory_format=memory_format)
# This expected result is obtain using PIL.Image.resize
# for c in range(3):
# a_in = t_in.numpy()[0, c, ...]
# pil_in = Image.fromarray(a_in)
# pil_out = pil_in.resize((2, 2), resample=Image.BICUBIC)
expected_out = torch.tensor([
15.1205635, 18.760439, 44.23956, 47.879436, 79.12056, 82.76044,
108.23956, 111.87944, 143.12057, 146.76044, 172.23956, 175.87943
], device=device, dtype=t_in.dtype).reshape(1, 3, 2, 2)
t_out = F.interpolate(t_in, size=(2, 2), mode="bicubic", align_corners=False, antialias=True)
self.assertEqual(expected_out, t_out)
@parametrize_test("align_corners", [True, False])
def test_upsamplingTrilinear3d(self, device, align_corners):
kwargs = dict(mode='trilinear', align_corners=align_corners)
for memory_format in [torch.contiguous_format, torch.channels_last_3d]:
# test float scale factor up & downsampling
for scale_factor in [0.5, 1.5, 2]:
m = nn.Upsample(scale_factor=scale_factor, **kwargs)
in_t = torch.ones(1, 2, 2, 2, 2, device=device, dtype=torch.double)
in_t = in_t.contiguous(memory_format=memory_format).requires_grad_()
out_size = int(math.floor(in_t.shape[-1] * scale_factor))
with warnings.catch_warnings(record=True) as w:
out_t = m(in_t)
expected_out = torch.ones(1, 2, out_size, out_size, out_size, device=device, dtype=torch.double)
self.assertEqual(expected_out, out_t)
# Assert that memory format is carried through to the output
self.assertTrue(out_t.is_contiguous(memory_format=memory_format))
out_t.backward(torch.randn_like(out_t))
self.assertTrue(in_t.grad.is_contiguous(memory_format=memory_format))
input = torch.randn(1, 2, 2, 2, 2, requires_grad=True, dtype=torch.double)
self.assertEqual(
F.interpolate(input, (out_size, out_size, out_size), **kwargs),
F.interpolate(input, scale_factor=scale_factor, **kwargs))
gradcheck(lambda x: F.interpolate(x, out_size, **kwargs), [input])
gradgradcheck(lambda x: F.interpolate(x, out_size, **kwargs), [input])
@onlyCUDA
@dtypes(torch.half)
@largeTensorTest('40GB')
def test_upsampling_64bit_indexing_channels_last(self, device, dtype):
x = torch.rand((32, 64, 512, 512), dtype=dtype, device=device)
out = torch.nn.functional.interpolate(x.to(memory_format=torch.channels_last), scale_factor=2, mode='nearest')
out_ref = torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')
del x
self.assertTrue(torch.allclose(out, out_ref))
def _slow_masked_softmax(self, input, mask):
exp = torch.exp(input)
exp = exp * mask
s = exp.sum(dim=3, keepdim=True).expand(exp.size())
return exp / s
def test_masked_softmax_mask_types(self, device):
# Test that mask type 0 (LxL attention mask), mask type 1 (BxL padding mask),
# and mask type 2 (generic BxHxLxL mask) are processed correctly on the
# fast path and the results match explicit slow calculation.
sizes = [(1, 1, 32), (3, 16, 310), (12, 4, 1024), (4, 2, 1200)]
for (B, num_heads, L) in sizes:
# mask_type == 0 => attention mask of shape LxL
src_mask_orig = torch.randint(0, 2, (L, L)).bool()
src_mask = src_mask_orig.reshape(1, 1, L, L).expand(B, num_heads, L, L).bool()
# mask_type == 1 => padding mask of shape BxL
src_key_padding_mask_orig = torch.randint(0, 2, (B, L)).bool()
src_key_padding_mask = src_key_padding_mask_orig.reshape(B, 1, 1, L).expand(B, num_heads, L, L).bool()
# mask_type == 2 => shape BxHxLxL
generic_mask = torch.randint(0, 2, (B, num_heads, L, L)).bool()
masks = [(src_mask_orig, src_mask, 0),
(src_key_padding_mask_orig, src_key_padding_mask, 1),
(generic_mask, generic_mask, 2)
]
for dim in [0, 3]:
for mask_orig, mask, mask_type in masks:
if (self.device_type == "cuda") and (num_heads % 2) and (mask_type == 1):
# CUDA path doesn't support padding mask when the number of heads is odd
continue
input = torch.randn((B, num_heads, L, L))
if (self.device_type == "cuda"):
input = input.cuda()
mask = mask.cuda()
mask_orig = mask_orig.cuda()
native_res = torch._masked_softmax(input, mask_orig, dim, mask_type)
mask = ~mask
def slow_masked_softmax(input, mask):
exp = torch.exp(input)
exp = exp * mask
s = exp.sum(dim=dim, keepdim=True).expand(exp.size())
return exp / s
pt_res = slow_masked_softmax(input, mask)
pt_res = torch.nan_to_num(pt_res)
mask_not = mask.logical_not()
# In result, should only fill the entirely masked out rows since those are non-deterministic (*may* be 0)
# Converts rows with all True's to False
mask_out = mask_not.all(dim, keepdim=True).expand(mask_not.shape)
self.assertEqual(
pt_res.masked_fill(mask_out, 0),
native_res.masked_fill(mask_out, 0),
exact_dtype=True
)
@onlyCUDA
@gcIfJetson
def test_masked_softmax_devices_parity(self):
# Test that softmax with mask type 0 (LxL attention mask), mask type 1 (BxL padding mask),
# and mask type 2 (BxHxLxL generic mask) gives the same result on CPU and on CUDA.
sizes = [(1, 1, 32), (3, 16, 310), (12, 4, 1024), (4, 2, 1200)]
for (B, num_heads, L) in sizes:
# mask_type == 0 => attention mask of shape LxL
src_mask = torch.randint(0, 2, (L, L)).bool()
# mask_type == 1 => padding mask of shape BxL
src_key_padding_mask = torch.randint(0, 2, (B, L)).bool()
# mask_type == 2 => generic mask of shape BxHxLxL
generic_mask = torch.randint(0, 2, (B, num_heads, L, L)).bool()
masks = [(src_mask, 0), (src_key_padding_mask, 1), (generic_mask, 2)]
input = torch.randn((B, num_heads, L, L))
for dim in [0, 3]:
for mask, mask_type in masks:
if (num_heads % 2) and (mask_type == 1):
# CUDA path doesn't support padding mask when the number of heads is odd
continue
def softmax_on_device(mask, input, device):
# Compute softmax on a given device
input_device = input.to(device)
mask_device = mask.to(device)
softmax_res = torch._masked_softmax(input_device, mask_device, dim, mask_type)
if mask_type == 0:
mask_expanded = mask_device.reshape(1, 1, L, L).expand(B, num_heads, L, L).bool()
elif mask_type == 1:
mask_expanded = mask_device.reshape(B, 1, 1, L).expand(B, num_heads, L, L).bool()
else:
mask_expanded = mask_device
# In result, should only fill the entirely masked out rows since those are non-deterministic (*may* be 0)
# Fill rows with all True's with 0
mask_out = mask_expanded.all(dim, keepdim=True).expand(mask_expanded.shape)
softmax_res = softmax_res.masked_fill(mask_out, 0)
return softmax_res
cpu_res = softmax_on_device(mask, input, "cpu")
cuda_res = softmax_on_device(mask, input, "cuda")
self.assertEqual(cpu_res, cuda_res, exact_dtype=True)
def test_masked_softmax(self, device):
sizes = [(1, 1, 32), (3, 16, 310), (12, 4, 1024), (4, 2, 1200)]
for (B, num_heads, L) in sizes:
for dim in [0, 3]:
input = torch.randn((B, num_heads, L, L))
mask = torch.randint(0, 2, (B, L))
mask = mask.reshape(B, 1, 1, L).expand(B, num_heads, L, L).bool()
mask_type = 1 # BxL => src_key_padding_mask
if (self.device_type == "cuda"):
input = input.cuda()
mask = mask.cuda()
native_res = torch._masked_softmax(input, mask, dim, mask_type)
mask = ~mask
def slow_masked_softmax(input, mask):
exp = torch.exp(input)
exp = exp * mask
s = exp.sum(dim=dim, keepdim=True).expand(exp.size())
return exp / s
pt_res = slow_masked_softmax(input, mask)
pt_res = torch.nan_to_num(pt_res)
mask_not = mask.logical_not()
# In result, should only fill the entirely masked out rows since those are non-deterministic (*may* be 0)
# Converts rows with all True's to False
mask_out = mask_not.all(dim, keepdim=True).expand(mask_not.shape)
self.assertEqual(
pt_res.masked_fill(mask_out, 0),
native_res.masked_fill(mask_out, 0),
exact_dtype=True
)
def _test_masked_softmax_helper(self, input, dim, mask, mask_type):
input_ref = input.detach().clone().requires_grad_()
result = torch._masked_softmax(input, mask, dim, mask_type)
expected = torch._softmax(input_ref.masked_fill(mask, float('-inf')), dim, False)
grad = torch.randn_like(expected).to(dtype=expected.dtype)
result.backward(grad)
expected.backward(grad)
# Make sure the optional argument works as well
if dim == input.dim() - 1:
input_ref_default = input.detach().clone().requires_grad_()
result_default = torch._masked_softmax(input_ref_default, mask, None, mask_type)
result_default.backward(grad)
self.assertEqual(result, result_default)
self.assertEqual(input.grad, input_ref_default.grad)
# In result, should only fill the entirely masked out rows since those are non-deterministic (*may* be 0)
# Converts rows with all True's to False
mask_out = mask.all(dim, keepdim=True).expand(mask.shape)
self.assertEqual(result.masked_fill(mask_out, 0), expected.masked_fill(mask_out, 0))
self.assertEqual(input.grad, torch.nan_to_num(input_ref.grad))
self.assertEqual(input.grad, input.grad.masked_fill(mask, 0.0))
def test_masked_softmax_grad(self, device):
shapes = [(1, 1, 32), (3, 16, 310), (12, 4, 1024), (4, 2, 1200)]
for shape in shapes:
dims = [0, len(shape) - 1] if len(shape) > 0 else [0]
for dim in dims:
for mask_type in [1, 2]: # 1 = BxL => src_key_padding_mask
input = torch.randn(shape, requires_grad=True)
mask = torch.randint(0, 2, shape).bool()
if (self.device_type == "cuda"):
input = input.cuda().detach().requires_grad_()
mask = mask.cuda()
self._test_masked_softmax_helper(input, dim, mask, mask_type)
# In this test, the forward pass is expected to produce nan's because when dim=0, we only have unspecified values
def test_masked_softmax_forward_with_nans(self, device):
dim = 0
shapes = [(4, 5), (50, 100), (1500, 1200)]
for (x, y) in shapes:
for mask_type in [1, 2]: # 1 = BxL => src_key_padding_mask
input = torch.randn((x, y), requires_grad=True)
mask = torch.tensor([i % 2 for i in range(y)]).expand((x, y)).bool()
if (self.device_type == "cuda"):
input = input.cuda().detach().requires_grad_()
mask = mask.cuda()
self._test_masked_softmax_helper(input, dim, mask, mask_type)
@onlyCUDA
def test_masked_softmax_transformer_layout(self, device):
B = 211
num_heads = 16
L = 42
input = torch.randn((B, num_heads, L, L))
dim = input.dim() - 1
mask = torch.randint(0, 2, (B, L))
mask_type = 1 # BxL => src_key_padding_mask
if (self.device_type == "cuda"):
input = input.cuda()
mask = mask.cuda()
mask = mask.bool()
native_res = torch._masked_softmax(input, mask, dim, mask_type)
mask = mask.reshape(B, 1, 1, L).expand(B, num_heads, L, L)
mask = ~mask
mask = mask.float()
pt_res = self._slow_masked_softmax(input, mask)
self.assertEqual(pt_res, native_res, exact_dtype=True)
@onlyCUDA
def test_masked_softmax_TxT_layout(self, device):
B = 211
num_heads = 16
L = 42
input = torch.randn((B, num_heads, L, L))
dim = input.dim() - 1
mask = torch.randint(0, 2, (L, L))
mask_type = 0 # LxL => src_mask
if (self.device_type == "cuda"):
input = input.cuda()
mask = mask.cuda()
mask = mask.bool()
native_res = torch._masked_softmax(input, mask, dim, mask_type)
mask = mask.expand(B, num_heads, L, L)
mask = ~mask
mask = mask.float()
pt_res = self._slow_masked_softmax(input, mask)
self.assertEqual(pt_res, native_res, exact_dtype=True)
@dtypesIfCUDA(torch.half, torch.float)
@dtypes(torch.float)
def test_softmax_results(self, device, dtype):
# Non-even sizes and non-zero shifts test fallback paths in vectorized kernel
# Note: dim1 > 1024 is needed to exercise the vectorized (non-persistent) path, (16, 30576) is BERT-esque
sizes = [(0, 10), (32, 20), (10, 0), (31, 20), (32, 21), (31, 23), (32, 1536), (31, 2048), (33, 2049), (16, 30576)]
shifts = [(0, 0), (1, 0), (0, 1), (1, 1)]
for fn in [F.softmax, F.log_softmax]:
for size in sizes:
for shift in shifts:
input = torch.rand(size, device=device, dtype=dtype)
# Note: With the largest tests we can hit upper limit of fp16 when we
# sum, so scale the input down to stay in a nicer range.
if dtype == torch.float16:
input = input / 100.
input = input[shift[0]:, shift[1]:]
# Note; Don't want to bprop back through slice op
input = input.detach().requires_grad_(True)
ref_input = input.clone().cpu().detach().requires_grad_(True)
for dim in [0, 1]:
ref_output = fn(ref_input, dtype=torch.float, dim=dim)
output = fn(input, dtype=torch.float, dim=dim)
grad_output = torch.rand(size, device=device, dtype=dtype)
grad_output = grad_output[shift[0]:, shift[1]:]
ref_grad_output = grad_output.clone().cpu().detach()
grad_input, = torch.autograd.grad(output, input, grad_outputs=(grad_output), create_graph=True)
ref_grad_input, = torch.autograd.grad(ref_output, ref_input,
grad_outputs=(ref_grad_output), create_graph=True)
grad_input.sum().backward()
ref_grad_input.sum().backward()
self.assertEqual(output, ref_output)
self.assertEqual(grad_input, ref_grad_input)
self.assertEqual(input.grad, ref_input.grad)
@onlyCUDA
@dtypes(torch.float, torch.half)
@largeTensorTest("20GB")
@largeTensorTest("64GB", "cpu")
def test_warp_softmax_64bit_indexing(self, device, dtype):
def run_test(*shape):
x = torch.randn(shape, device="cuda", dtype=torch.float16, requires_grad=True)
y = F.log_softmax(x, dim=-1, dtype=dtype)
y.backward(y)
with torch.no_grad():
xx = x.cpu().requires_grad_()
yy = F.log_softmax(xx.float(), dim=-1).to(dtype)
yy.backward(yy)
# workaround to reduce memory usage vs. self.assertEqual, see #84944
rtol, atol = torch.testing._comparison.get_tolerances(dtype, rtol=None, atol=None)
self.assertTrue(torch.allclose(y.cpu(), yy, rtol=rtol, atol=atol))
# x is half
rtol, _ = torch.testing._comparison.get_tolerances(torch.half, rtol=None, atol=None)
self.assertTrue(torch.allclose(x.grad.cpu(), xx.grad, rtol=rtol, atol=1e-3))
run_test(1100000000, 2) # Illegal memory access https://github.com/pytorch/pytorch/issues/52715
run_test(2200000000, 1) # invalid configuration argument https://github.com/pytorch/pytorch/issues/52716
@onlyCUDA
@dtypes(torch.half)
@largeTensorTest("20GB")
@largeTensorTest("2GB", "cpu")
@precisionOverride({torch.half: 0.001})
def test_softmax_64bit_indexing(self, device, dtype):
def run_test(*shape):
x = torch.ones(shape, device=device, dtype=dtype, requires_grad=True)
y = F.log_softmax(x, dim=-1, dtype=dtype)
y.backward(y)
self.assertEqual(y[0], y[-1])
self.assertEqual(x.grad[0], x.grad[-1])
run_test(1024 * 256 + 1, 8192) # https://github.com/pytorch/pytorch/issues/84144
@dtypes(torch.float)
@dtypesIfCUDA(torch.float, torch.half)
def test_log_softmax_big(self, device, dtype):
def _test_helper(shape):
# generate a tensor with big numbers that are exactly representable in dtype
# and are at a constant offset from tensor with small numbers
# the logsoftmax of a small and big tensors should be equal
x_small = torch.randint(100, shape, dtype=dtype, device=device)
offset = 1.5e3 if dtype == torch.half else 1e7
x_big = x_small + offset
self.assertEqual(F.log_softmax(x_small, -1), F.log_softmax(x_big, -1))
_test_helper((16, 4))
if self.device_type == 'cuda':
# test non-persistent softmax kernel
_test_helper((4, 1536))
def test_save_lstm_compatibility(self, device):
# Test that saving an LSTM in PyTorch 1.7 and older can still be
# loaded in newer versions of PyTorch.
model = nn.LSTM(2, 3)
x = torch.randn(32, 5, 2)
expected = model(x)
# Get a state dict for PyTorch 1.7 LSTM. Before PyTorch 1.8, proj_size
# didn't exist.
assert model.proj_size == 0
state_dict = model.__dict__
del state_dict['proj_size']
# load a model
loaded_model = nn.LSTM(2, 3)
loaded_model.__setstate__(state_dict)
result = loaded_model(x)
self.assertEqual(result, expected)
@onlyCUDA
@tf32_on_and_off(0.005)
def test_grid_sample_large(self, device):
def issue_35202():
input_tensor = torch.rand(1, 1, 480, 640, dtype=torch.float, device=device, requires_grad=True)
coords = torch.tensor([[-10059144, 67680944], [67680944, 67680944]], dtype=torch.float, device=device)
coords = coords.unsqueeze(0).unsqueeze(0).repeat(1, 1, 1, 1)
result = torch.nn.functional.grid_sample(input_tensor, coords)
self.assertEqual(result, torch.tensor([[[[0., 0.]]]], dtype=torch.float, device=device))
result.backward(torch.ones_like(result))
torch.cuda.synchronize()
issue_35202()
def issue_24823_1(dtype):
image = torch.arange(27, 0, -1, dtype=dtype, device=device).view(1, 1, 3, 3, 3)
image.requires_grad_()
grid = torch.nn.functional.affine_grid(
torch.tensor([[[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]]], dtype=dtype, device=device),
(1, 1, 3, 3, 3))
grid[:, 1, 1, 1, 0] = float('inf')
result = torch.nn.functional.grid_sample(image, grid, padding_mode='zeros')
tol_override = {'atol': 0.005, 'rtol': 0} if dtype == torch.half else {}
self.assertEqual(result, torch.tensor([[[[[27., 26., 25.], [24., 23., 22.], [21., 20., 19.]],
[[18., 17., 16.], [15., 0., 13.], [12., 11., 10.]],
[[9., 8., 7.], [6., 5., 4.], [3., 2., 1.]]]]],
device=device, dtype=dtype), **tol_override)
result.backward(torch.ones_like(result))
expected_grad = torch.ones_like(image)
expected_grad[0, 0, 1, 1, 1] = 0
self.assertEqual(image.grad, expected_grad, atol=0.005, rtol=0)
issue_24823_1(torch.half)
issue_24823_1(torch.float)
issue_24823_1(torch.double)
def issue_24823_2():
param = torch.tensor([[[-1.0e+20, 0.0, 0.0], [0.0, -1.0e+20, 0.0]]], dtype=torch.float, device=device)
img = torch.zeros((1, 1, 4, 4), dtype=torch.float, device=device, requires_grad=True)
grid = torch.nn.functional.affine_grid(param, img.size())
result = torch.nn.functional.grid_sample(img, grid)
self.assertEqual(result, torch.zeros(1, 1, 4, 4, device=device, dtype=torch.float))
result.backward(torch.ones_like(result))
torch.cuda.synchronize()
issue_24823_2()
@dtypes(torch.float, torch.double)
@largeTensorTest(lambda self, device, dtype:
# Compute sum of the large tensor sizes:
# (im.numel() + small_image.numel() + small_image.grad.numel() +
# large_view.grad.numel()) * sizeof(dtype)
32769 * (65536 + 3 * 65536 / 128) *
torch.tensor([], dtype=dtype).element_size())
def test_grid_sample_large_index_2d(self, device, dtype):
# Test 64-bit indexing with grid_sample (gh-41656)
# Try accessing the corners, there should be no segfault
coords = torch.tensor([[[-1., -1.],
[+1., -1.]],
[[-1., +1.],
[+1., +1.]]], device=device, dtype=dtype)
coords = coords.expand(1, 2, 2, 2)
im = torch.zeros([1, 1, 32769, 65536], device=device, dtype=dtype)
# Compare sampling with large strides to the same op on a contiguous tensor
coords = torch.rand(1, 4, 4, 2, device=device, dtype=dtype)
large_view = im[..., 127::128]
small_image = torch.rand_like(large_view)
large_view[...] = small_image
large_view.requires_grad, small_image.requires_grad = True, True
self.assertTrue(
sum(i * s for i, s in zip(large_view.size(), large_view.stride())) >= 2 ** 31,
msg="View must use 64-bit indexing")
for mode, padding_mode, align_corners in itertools.product(
('nearest', 'bilinear', 'bicubic'), ('zeros', 'border', 'reflection'), (True, False)):
a = F.grid_sample(
small_image, coords, mode=mode,
padding_mode=padding_mode, align_corners=align_corners)
a.sum().backward()
b = F.grid_sample(
large_view, coords, mode=mode,
padding_mode=padding_mode, align_corners=align_corners)
b.sum().backward()
self.assertEqual(a, b)
self.assertEqual(small_image.grad, large_view.grad)
small_image.grad.zero_()
large_view.grad.zero_()
@dtypes(torch.float, torch.double)
@largeTensorTest(lambda self, device, dtype:
# Compute sum of the large tensor sizes:
# (im.numel() + small_image.numel() + small_image.grad.numel() +
# large_view.grad.numel()) * sizeof(dtype)
2 * 32769 * (32768 + 3 * 32768 / 128) *
torch.tensor([], dtype=dtype).element_size())
def test_grid_sample_large_index_3d(self, device, dtype):
# Test 64-bit indexing with grid_sample (gh-41656)
# Try accessing the corners, there should be no segfault
coords = torch.full((1, 2, 2, 2, 3), 1., device=device, dtype=dtype)
im = torch.zeros([1, 1, 2, 32769, 32768], device=device, dtype=dtype)
result = F.grid_sample(im, coords, align_corners=False)
self.assertEqual(result, torch.zeros((1, 1, 2, 2, 2), device=device, dtype=dtype))
# Compare sampling with large strides to the same op on a contiguous tensor
coords = torch.rand(1, 1, 4, 4, 3, device=device, dtype=dtype)
large_view = im[..., 127::128]
small_image = torch.rand_like(large_view)
large_view[...] = small_image
small_image.requires_grad, large_view.requires_grad = True, True
self.assertTrue(
sum(i * s for i, s in zip(large_view.size(), large_view.stride())) >= 2 ** 31,
msg="View must use 64-bit indexing")
for mode, padding_mode, align_corners in itertools.product(
('nearest', 'bilinear'), ('zeros', 'border', 'reflection'), (True, False)):
a = F.grid_sample(
small_image, coords, mode=mode,
padding_mode=padding_mode, align_corners=align_corners)
a.sum().backward()
b = F.grid_sample(
large_view, coords, mode=mode,
padding_mode=padding_mode, align_corners=align_corners)
b.sum().backward()
self.assertEqual(a, b)
self.assertEqual(small_image.grad, large_view.grad)
small_image.grad.zero_()
large_view.grad.zero_()
@onlyCUDA
def test_grid_sample_half_precision(self):
def helper(shape_in, shape_out, align_corners):
for mode in ('bilinear', 'nearest', 'bicubic'):
if len(shape_in) != 4 and mode == 'bicubic':
continue
data = torch.randn(shape_in, device='cuda', dtype=torch.half)
grid = torch.rand(shape_out, device='cuda', dtype=torch.half) * 2.0 - 1.0
out_half = F.grid_sample(data, grid, mode=mode, padding_mode='zeros', align_corners=align_corners)
out_double = F.grid_sample(data.double(), grid.double(), mode=mode, padding_mode='zeros',
align_corners=align_corners)
self.assertEqual(out_half, out_double.half(), msg=f"grid_sample with mode = {mode} doesn't match")
helper((32, 64, 16, 16), (32, 8, 8, 2), True)
helper((32, 64, 16, 16, 16), (32, 8, 8, 8, 3), True)
helper((32, 64, 16, 16), (32, 8, 8, 2), False)
helper((32, 64, 16, 16, 16), (32, 8, 8, 8, 3), False)
def _test_gumbel_softmax_st_shapes(self, device, dtype, shape, dim, count_expected):
logits = torch.randn(shape, dtype=torch.float, device=device)
logits = logits.to(dtype)
y_draw = F.gumbel_softmax(logits, hard=True, dim=dim)
# All values positive
self.assertGreaterEqual(y_draw.min(), 0)
# Shape unchanged
self.assertTrue(y_draw.shape == logits.shape)
# One choice per draw
self.assertEqual(y_draw.sum(), count_expected, atol=torch.finfo(y_draw.dtype).eps, rtol=0)
def _test_gumbel_softmax_straight_through(self, device, dtype):
num_draws = 100
logits = torch.tensor([[0.2, 0.8, 0.1]], device=device)
logits = logits.reshape([1, 3])
logits = logits.to(dtype).requires_grad_()
probs = logits.softmax(dim=-1)
counts = torch.zeros_like(logits)
for _ in range(num_draws):
y_draw = F.gumbel_softmax(logits, hard=True)
counts = counts + y_draw
# All values positive
self.assertGreaterEqual(y_draw.min(), 0)
# Each experiment should result in 1 draw.
self.assertEqual(counts.sum(), num_draws, atol=torch.finfo(counts.dtype).eps, rtol=0)
# check results is asymptotically as expected.
expected = probs * num_draws
# ~z is approximately N(0,1) for unbiased count
z = (counts - expected) / (expected * (1 - probs)).sqrt()
# A (lazy) approximate 99% two-sided test:
# occurs with prob alpha~>=0.01 if unbiased
self.assertLess(z.abs().max().item(), 2.58)
def _test_gumbel_softmax_grad(self, device, dtype):
# "hard" and "not hard" should propagate same gradient.
logits_soft = torch.zeros(10, 10, dtype=dtype, device=device, requires_grad=True)
logits_hard = torch.zeros(10, 10, dtype=dtype, device=device, requires_grad=True)
seed = torch.random.get_rng_state()
y_soft = F.gumbel_softmax(logits_soft, hard=False)
torch.random.set_rng_state(seed)
y_hard = F.gumbel_softmax(logits_hard, hard=True)
y_soft.sum().backward()
y_hard.sum().backward()
# 2eps = 1x addition + 1x subtraction.
tol = 2 * torch.finfo(dtype).eps
self.assertEqual(logits_soft.grad, logits_hard.grad, atol=tol, rtol=0)
@skipIfMps
@dtypesIfCUDA(torch.half, torch.float, torch.double)
@dtypes(torch.float, torch.double)
def test_gumbel_softmax(self, device, dtype):
self._test_gumbel_softmax_st_shapes(device, dtype, shape=[5], dim=0, count_expected=1)
self._test_gumbel_softmax_st_shapes(device, dtype, shape=[5], dim=-1, count_expected=1)
self._test_gumbel_softmax_st_shapes(device, dtype, shape=[5, 4], dim=1, count_expected=5)
self._test_gumbel_softmax_st_shapes(device, dtype, shape=[5, 4, 3], dim=1, count_expected=5 * 3)
self._test_gumbel_softmax_st_shapes(device, dtype, shape=[5, 4, 3], dim=-1, count_expected=5 * 4)
self._test_gumbel_softmax_straight_through(device, dtype)
self._test_gumbel_softmax_grad(device, dtype)
def _test_rnn_retain_variables(self, device, dtype):
rnns = [nn.LSTM(10, 20, num_layers=2).to(device, dtype),
nn.GRU(10, 20, num_layers=2).to(device, dtype),
nn.RNN(10, 20, num_layers=2).to(device, dtype)]
for rnn in rnns:
input = torch.randn(5, 6, 10, device=device, dtype=dtype, requires_grad=True)
output = rnn(input)
output[0].sum().backward(retain_graph=True)
grads = [input.grad.data.clone()] + [p.grad.data.clone() for p in rnn.parameters()]
for _ in range(4):
rnn.zero_grad()
input.grad.data.zero_()
output[0].sum().backward(retain_graph=True)
grads2 = [input.grad.data] + [p.grad.data for p in rnn.parameters()]
self.assertEqual(grads, grads2)
@dtypesIfCUDA(torch.half, torch.float, torch.double)
@dtypes(torch.double)
def test_rnn_retain_variables(self, device, dtype):
self._test_rnn_retain_variables(device, dtype)
if self.device_type == 'cuda' and self.has_cudnn():
with torch.backends.cudnn.flags(enabled=False):
self._test_rnn_retain_variables(device, dtype)
@onlyCUDA
@dtypes(torch.double)
def test_lstmcell_backward_only_one_output_grad(self, device, dtype):
# checks that undefined gradients doen't hamper the backward
# see #11872
l = torch.nn.LSTMCell(2, 3).to(device).to(dtype=dtype)
s = torch.randn(1, 2, device=device, dtype=dtype, requires_grad=True)
for i in range(2):
out = l(s)[i]
out.sum().backward()
self.assertFalse(s.grad is None or s.grad.abs().sum().item() == 0)
def _test_rnn_mod(self, mod, inp):
def flatten_out(mod, inp):
out = mod(inp)
return tuple([t if isinstance(t, torch.Tensor) else tt for t in out for tt in t])
gradcheckfunc = partial(flatten_out, mod)
with torch.backends.cudnn.flags(enabled=False):
gradcheck(gradcheckfunc, inp, check_batched_grad=False)
gradgradcheck(gradcheckfunc, inp, check_batched_grad=False)
if inp.is_cuda and not TEST_WITH_ROCM:
# Assert that we have good error message around unsupported CuDNN double backward
# NB: we trigger double backward using .backward() instead of autograd.grad due to
# https://github.com/pytorch/pytorch/issues/37874
with torch.backends.cudnn.flags(enabled=True):
result = gradcheckfunc(inp)
result[0].sum().backward(create_graph=True)
grad0 = next(mod.parameters()).grad
with self.assertRaisesRegex(RuntimeError,
"please disable the CuDNN backend temporarily"):
grad0.sum().backward()
# Here we avoid the backward(create_graph=True) memory leak
# described in https://github.com/pytorch/pytorch/issues/7343
for param in mod.parameters():
param.grad = None
inp.grad = None
# Merge into OpInfo?
@skipMeta # LSTM cell reuses output which was resized
@dtypes(torch.double)
def test_LSTM_grad_and_gradgrad(self, device, dtype):
hsize = 4
inp = torch.rand(1, 3, hsize, device=device, dtype=dtype, requires_grad=True)
for bias in [True, False]:
mod = torch.nn.LSTM(hsize, hsize, bias=bias).to(device).to(dtype)
self._test_rnn_mod(mod, inp)
@skipMeta # GRU cell reuses output which was resized
@dtypes(torch.double)
def test_GRU_grad_and_gradgrad(self, device, dtype):
hsize = 4
inp = torch.rand(1, 3, hsize, device=device, dtype=dtype, requires_grad=True)
for bias in [True, False]:
mod = torch.nn.GRU(hsize, hsize, bias=bias).to(device).to(dtype)
self._test_rnn_mod(mod, inp)
@skipMeta
@dtypes(torch.float32, torch.bfloat16)
@onlyCPU
def test_LSTM_differentiable_backward_using_oneDNN(self, dtype):
batch = 10
seq_len = 12
input = 3
Net = nn.LSTM(input, 3, 20, batch_first=True)
import copy
Net_clone = copy.deepcopy(Net)
x = torch.rand(batch, seq_len, input)
x1 = x.clone().requires_grad_(True)
x2 = x.clone().requires_grad_(True)
torch._C._set_mkldnn_enabled(False)
out1, _ = Net(x1)
der_out1 = torch.autograd.grad(out1, x1,
grad_outputs=torch.ones_like(out1),
retain_graph=True,
create_graph=True)[0]
loss1 = der_out1.sum()
loss1.backward(retain_graph=True)
torch._C._set_mkldnn_enabled(True)
out2, _ = Net(x2)
der_out2 = torch.autograd.grad(out2, x2,
grad_outputs=torch.ones_like(out2),
retain_graph=True,
create_graph=True)[0]
loss2 = der_out2.sum()
loss2.backward(retain_graph=True)
assert torch.allclose(der_out1, der_out2)
assert torch.allclose(x1.grad, x2.grad)
@onlyCUDA
def test_upsamplingNearest1d_launch_config(self, device):
m = nn.Upsample(scale_factor=2)
inp = torch.rand(2**25, 1, 1, device=device)
out = m(inp)
inp_ref = inp.cpu()
out_ref = m(inp_ref)
self.assertEqual(out_ref, out)
@onlyCUDA
def test_upsamplingNearest2d_launch_config(self, device):
m = nn.Upsample(scale_factor=2)
inp = torch.rand(2**25, 1, 1, 1, device=device)
out = m(inp)
inp_ref = inp.cpu()
out_ref = m(inp_ref)
self.assertEqual(out_ref, out)
@onlyCUDA
@gcIfJetson
def test_upsamplingNearest3d_launch_config(self, device):
m = nn.Upsample(scale_factor=2)
inp = torch.rand(2**25, 1, 1, 1, 1, device=device)
out = m(inp)
inp_ref = inp.cpu()
out_ref = m(inp_ref)
self.assertEqual(out_ref, out)
@unittest.expectedFailure
@skipIfRocm
@onlyCUDA
def test_upsamplingNearest2d_launch_fail(self, device):
m = nn.Upsample(scale_factor=2)
# launch grid_y == 2**16 (larger than maximum y-dimension limit 65535)
inp = torch.rand(1, 1, 2**15, 2**8, device=device)
out = m(inp)
@onlyCUDA
@skipCUDAIfNotRocm
def test_upsamplingNearest2d_launch_rocm(self, device):
# test_upsamplingNearest2d_launch_fail should run OK on ROCm
m = nn.Upsample(scale_factor=2)
inp = torch.rand(1, 1, 2**15, 2**8, device=device)
out = m(inp)
@onlyCUDA
@skipCUDAIfCudnnVersionLessThan(7600)
def test_CTCLoss_cudnn(self, device):
def _helper(zero_infinity):
target_lengths = [30, 25, 20]
input_lengths = [50, 50, 50]
targets = torch.randint(1, 15, (sum(target_lengths),), dtype=torch.int)
log_probs = torch.randn(50, 3, 15, dtype=torch.float, device=device).log_softmax(2).requires_grad_()
log_probs_ref = log_probs.detach().clone().requires_grad_()
with torch.backends.cudnn.flags(enabled=True):
res = torch.nn.functional.ctc_loss(log_probs, targets, input_lengths, target_lengths, zero_infinity=zero_infinity)
res.backward()
expected = ctcloss_reference(log_probs, targets.cuda(), input_lengths, target_lengths).float()
with torch.backends.cudnn.flags(enabled=False):
res2 = torch.nn.functional.ctc_loss(log_probs_ref, targets.cuda().long(), input_lengths, target_lengths,
zero_infinity=zero_infinity)
res2.backward()
self.assertEqual(res, expected)
self.assertEqual(res2, res)
self.assertEqual(log_probs.grad, log_probs_ref.grad)
_helper(zero_infinity=True)
_helper(zero_infinity=False)
def _CTCLoss_gen_losses(self, device, input_length, vocab_size, target_length, reduction, use_module_form):
batch_size = 1
log_probs = torch.randn(input_length, batch_size, vocab_size, dtype=torch.float, device=device) \
.log_softmax(2).requires_grad_()
targets = torch.randint(low=1, high=vocab_size - 1, size=(batch_size, target_length),
dtype=torch.int, device=device)
input_lengths = batch_size * [input_length]
target_lengths = batch_size * [target_length]
log_probs_no_bd = log_probs.squeeze(1).detach().clone().requires_grad_()
targets_no_bd = targets.squeeze(0).detach().clone()
input_lengths_no_bd = torch.tensor(input_length)
target_lengths_no_bd = torch.tensor(target_length)
# currently only length 2 and 1 right now, but left flexible for additional potential cases
log_probs_refs = [log_probs.detach().clone().requires_grad_() for _ in range(2)]
log_probs_no_bd_refs = [log_probs_no_bd.detach().clone().requires_grad_() for _ in range(1)]
losses = []
losses_no_bd = []
has_cuda = torch.cuda.is_available()
has_cudnn = has_cuda and 'cuda' in device and self.has_cudnn()
# cudnn requires a cpu target
if has_cuda and has_cudnn:
targets = targets.cpu()
targets_no_bd = targets_no_bd.cpu()
ctc_loss = (
nn.CTCLoss(reduction=reduction, zero_infinity=True)
if use_module_form
else partial(torch.nn.functional.ctc_loss, reduction=reduction, zero_infinity=True)
)
with torch.backends.cudnn.flags(enabled=has_cudnn):
# batched case. log_probs.shape = (T, N, C), targets = (N, S), input_lengths/target_lengths = (N,)
losses.append(ctc_loss(log_probs_refs[0], targets, input_lengths, target_lengths))
# batched case. input.shape = (T, N, C), targets = (S,), input_lengths/target_lengths = (N,)
losses.append(ctc_loss(log_probs_refs[1], targets_no_bd, input_lengths, target_lengths))
# unbatched case. input.shape = (T, C), targets = (S,), input_lengths/target_lengths = (N,)
losses_no_bd.append(ctc_loss(log_probs_no_bd_refs[0], targets_no_bd,
input_lengths_no_bd, target_lengths_no_bd))
for loss in losses + losses_no_bd:
loss.backward()
return losses, losses_no_bd, log_probs_refs, log_probs_no_bd_refs
def _assertEqual_list(self, expected, list_to_compare, atol=None, rtol=None):
for ele in list_to_compare:
self.assertEqual(expected, ele, atol=atol, rtol=rtol)
@parametrize_test("reduction", ['none', 'mean', 'sum'])
@parametrize_test("use_module_form", [True, False])
def test_CTCLoss_no_batch_dim(self, device, reduction, use_module_form):
input_length = 40
vocab_size = 3
target_length = 12
args = self._CTCLoss_gen_losses(device, input_length, vocab_size, target_length, reduction, use_module_form)
losses, losses_no_bd, log_probs_refs, log_probs_no_bd_refs = args
# test output values
self._assertEqual_list(losses[0], losses[1:], atol=1e-4, rtol=0)
self._assertEqual_list(losses[0].squeeze(0), losses_no_bd, atol=1e-4, rtol=0)
# test gradient values
self._assertEqual_list(log_probs_refs[0].grad, [t.grad for t in log_probs_refs[1:]], atol=1e-4, rtol=0)
self._assertEqual_list(
log_probs_refs[0].grad.squeeze(1),
[t.grad for t in log_probs_no_bd_refs],
atol=1e-4,
rtol=0,
)
# checking the output's shape
# batch dim case should be (N,). no batch dim case should be ()
self._assertEqual_list((1,) if reduction == 'none' else (), [loss.shape for loss in losses])
self._assertEqual_list((), [loss.shape for loss in losses_no_bd])
# checking the gradient's shape
# batch dim case should have shape (T, N, C). no batch dim case should have shape (T, C)
self._assertEqual_list((input_length, 1, vocab_size), [t.grad.shape for t in log_probs_refs])
self._assertEqual_list((input_length, vocab_size), [t.grad.shape for t in log_probs_no_bd_refs])
def _ordered_sequence(self, device, dtype):
"""Create ordered list of random sequences"""
seqs = [torch.empty(random.randint(1, 6), device=device, dtype=dtype)
for _ in range(5)]
seqs = [s.random_(-128, 128) for s in seqs]
ordered = sorted(seqs, key=len, reverse=True)
return ordered
def _padded_sequence(self, device, dtype):
"""Create Tensor of random padded sequences"""
ordered = self._ordered_sequence(device, dtype)
lengths = [len(i) for i in ordered]
padded_tensor = rnn_utils.pad_sequence(ordered)
return padded_tensor, lengths
@onlyCUDA
def test_device_mask(self, device):
for enforce_sorted in [True, False]:
padded, lengths = self._padded_sequence('cpu', torch.float)
packed = rnn_utils.pack_padded_sequence(
padded, lengths, enforce_sorted=enforce_sorted)
self.assertFalse(packed.is_cuda)
packed = packed.to(device)
self.assertTrue(packed.is_cuda)
unpacked, _ = rnn_utils.pad_packed_sequence(packed)
self.assertTrue(unpacked.is_cuda)
self.assertEqual(unpacked.dtype, torch.float)
@onlyCUDA
def test_overwrite_module_params_on_conversion_cpu_device(self, device):
# Test that under the current default settings
# (`torch.__future__.get_overwrite_module_params_on_conversion() == False`),
# a view to a module's parameters is not pointing to the same storage as
# its base variable after converting the module to a different device.
m = nn.Linear(20, 10)
mw = m.weight[:]
m.to(device)
with torch.no_grad():
# Without using `torch.no_grad()`, this will leak CUDA memory.
# (Issue is filed at https://github.com/pytorch/pytorch/issues/21875)
mw[0][0] = 5
self.assertTrue(mw[0][0].device.type == "cpu")
self.assertTrue(mw._base[0][0].device.type == "cuda")
try:
torch.__future__.set_overwrite_module_params_on_conversion(True)
# Test that if `torch.__future__.get_overwrite_module_params_on_conversion() == True`,
# a view to a module's parameters is still pointing to the same storage as
# its base variable after converting the module to a different device.
m = nn.Linear(20, 10)
mw = m.weight[:]
m.to(device)
with torch.no_grad():
mw[0][0] = 5
self.assertTrue(mw[0][0] == mw._base[0][0])
# Test that if `torch.__future__.get_overwrite_module_params_on_conversion() == True`,
# `cpu_module.to("cuda")` doesn't preserve previous references to
# `cpu_module`'s parameters or gradients.
m = nn.Linear(20, 10)
m.weight.grad = torch.randn(10, 20)
weight_ref = m.weight
weight_grad_ref = m.weight.grad
m.to(device)
self.assertNotEqual(weight_ref.device, m.weight.device)
self.assertNotEqual(weight_grad_ref.device, m.weight.grad.device)
finally:
torch.__future__.set_overwrite_module_params_on_conversion(False)
@onlyCUDA
@dtypes(torch.half, torch.float)
def test_softmax(self, device, dtype):
input = torch.rand(32, 100, device=device, dtype=dtype, requires_grad=True)
inputf = input.to(torch.float).detach().requires_grad_(True)
out = F.softmax(input, dim=-1, dtype=torch.float)
outf = F.softmax(inputf, dim=-1)
# should be bitwise equal
self.assertEqual(out, outf, atol=0, rtol=0)
gO = torch.empty_like(outf).uniform_()
out.backward(gO)
outf.backward(gO)
# should be bitwise equal
self.assertEqual(input.grad, inputf.grad.to(dtype), atol=0, rtol=0)
def _test_batchnorm_grad(self, device, dtype=torch.double):
bs, n_feat, size_feat = 4, 5, 6
input = torch.arange(bs * n_feat * size_feat, device=device,
requires_grad=True, dtype=dtype).view(bs, n_feat, size_feat)
weight = torch.arange(1, n_feat + 1, device=device, requires_grad=True, dtype=dtype)
bias = torch.arange(n_feat, device=device, requires_grad=True, dtype=dtype)
running_mean = 1 - torch.arange(n_feat, device=device, dtype=dtype)
running_var = 2 * torch.arange(n_feat, device=device, dtype=dtype)
for training in [False, True]:
_assertGradAndGradgradChecks(self, F.batch_norm, (input, running_mean, running_var, weight, bias,
training, 0.1, 0.0001))
def test_batchnorm_grad(self, device):
self._test_batchnorm_grad(device)
if self.device_type == 'cuda' and self.has_cudnn():
with torch.backends.cudnn.flags(enabled=False):
self._test_batchnorm_grad(device)
@onlyCUDA
def test_layernorm_half_precision(self):
width = 128
input = torch.rand(1, 5, width, device="cuda", dtype=torch.half) * 0.1
normalized_shape = (width,)
weight = torch.ones(width, device="cuda", dtype=torch.half)
bias = torch.zeros(width, device="cuda", dtype=torch.half)
eps = 1e-5
output_fp16 = torch.layer_norm(input, normalized_shape, weight, bias, eps)
output_fp32 = torch.layer_norm(input.float(), normalized_shape, weight.float(), bias.float(), eps).half()
self.assertEqual(output_fp16, output_fp32, atol=0, rtol=0)
@onlyCUDA
def test_layernorm_weight_bias(self):
width = 128
input = torch.rand(1, 5, width, device="cuda", dtype=torch.float32) * 0.1
normalized_shape = (width,)
data = torch.randn(width, device="cuda", dtype=torch.float32)
weight = torch.ones(width, device="cuda", dtype=torch.float32)
bias = torch.zeros(width, device="cuda", dtype=torch.float32)
eps = 1e-5
out_none_weight = torch.layer_norm(input, normalized_shape, None, data, eps)
out_one_weight = torch.layer_norm(input, normalized_shape, weight, data, eps)
self.assertEqual(out_none_weight, out_one_weight)
out_none_bias = torch.layer_norm(input, normalized_shape, data, None, eps)
out_zero_bias = torch.layer_norm(input, normalized_shape, data, bias, eps)
self.assertEqual(out_none_bias, out_zero_bias)
def test_hardsigmoid_grad(self, device):
inputs = (torch.randn(4, 16, 16, device=device, dtype=torch.double) - 0.5) * 10
inputs.requires_grad = True
self.assertTrue(gradcheck(F.hardsigmoid, (inputs,)))
# currently fails on XLA
@onlyNativeDeviceTypes
def test_hardswish_grad(self, device):
inputs = (torch.randn(4, 16, 16, device=device, dtype=torch.double) - 0.5) * 10
inputs.requires_grad = True
self.assertTrue(gradcheck(F.hardswish, (inputs,)))
def _test_batchnorm_eval(self, ndim, device, dtype, module_dtype=None):
module_dtype = module_dtype or dtype
module = nn.BatchNorm1d(3).to(device, module_dtype)
module.eval()
data = torch.rand([3] * ndim, device=device, dtype=dtype, requires_grad=True)
grad = torch.rand([3] * ndim, device=device, dtype=dtype)
# 1st pass
res1 = module(data)
res1.backward(grad)
grad1 = data.grad.clone()
# 2nd pass
if data.grad is not None:
data.grad.data.zero_()
res2 = module(data)
res2.backward(grad)
grad2 = data.grad.clone()
self.assertEqual(res1, res2)
self.assertEqual(grad1, grad2)
# track_running_stats=False
module = nn.BatchNorm1d(3, track_running_stats=False).to(device, module_dtype)
data = torch.rand(4, 3, device=device, dtype=dtype, requires_grad=True)
grad = torch.rand(4, 3, device=device, dtype=dtype)
# 1st pass
res1 = module(data)
res1.backward(grad)
grad1 = data.grad.clone()
# set eval
module.eval()
# 2nd pass
if data.grad is not None:
data.grad.data.zero_()
res2 = module(data)
res2.backward(grad)
grad2 = data.grad.clone()
self.assertEqual(res1, res2)
self.assertEqual(grad1, grad2)
@dtypes(torch.float)
@dtypesIfCUDA(torch.float, torch.bfloat16)
def test_batchnorm_eval(self, device, dtype):
self._test_batchnorm_eval(2, device, dtype)
self._test_batchnorm_eval(3, device, dtype)
if self.device_type == 'cuda' and self.has_cudnn():
with torch.backends.cudnn.flags(enabled=False):
self._test_batchnorm_eval(2, device, dtype)
self._test_batchnorm_eval(3, device, dtype)
@onlyCUDA
@dtypes(torch.bfloat16, torch.half)
def test_batchnorm_eval_mixed(self, device, dtype):
# Test bfloat16 input with float module
self._test_batchnorm_eval(2, device, dtype, torch.float)
self._test_batchnorm_eval(3, device, dtype, torch.float)
if self.device_type == 'cuda' and self.has_cudnn():
with torch.backends.cudnn.flags(enabled=False):
self._test_batchnorm_eval(2, device, dtype, torch.float)
self._test_batchnorm_eval(3, device, dtype, torch.float)
def _test_batchnorm_affine(self, ndim, device, dtype, module_dtype=None):
# Compare affine against no-op weights and bias
module_dtype = module_dtype or dtype
module = nn.BatchNorm1d(3, affine=False).to(device, module_dtype)
module_affine = nn.BatchNorm1d(3, affine=True).to(device, module_dtype)
with torch.no_grad():
module_affine.weight.fill_(1.0)
module_affine.bias.zero_()
data = torch.rand([3] * ndim, device=device, dtype=dtype, requires_grad=True)
grad = torch.ones_like(data, requires_grad=False)
# With weights all ones and bias all zeros
res1 = module_affine(data)
res1.backward(grad)
grad1 = data.grad.clone()
data.grad.zero_()
# Without any weights or bias
res2 = module(data)
res2.backward(grad)
grad2 = data.grad
self.assertEqual(res1, res2)
self.assertEqual(grad1, grad2)
@dtypes(torch.float)
@dtypesIfCUDA(torch.float, torch.bfloat16)
def test_batchnorm_affine(self, device, dtype):
self._test_batchnorm_affine(2, device, dtype)
self._test_batchnorm_affine(3, device, dtype)
if self.device_type == 'cuda' and self.has_cudnn():
with torch.backends.cudnn.flags(enabled=False):
self._test_batchnorm_affine(2, device, dtype)
self._test_batchnorm_affine(3, device, dtype)
@onlyCUDA
@dtypes(torch.bfloat16, torch.half)
def test_batchnorm_affine_mixed(self, device, dtype):
cudnn_enabled = [False]
if self.device_type == 'cuda' and self.has_cudnn():
# TODO: Test fails with cudnn, see gh-62034
# cudnn_enabled = [False, True]
pass
# Test bfloat16 input with float module
for enabled in cudnn_enabled:
with torch.backends.cudnn.flags(enabled=enabled):
self._test_batchnorm_affine(2, device, dtype, torch.float)
self._test_batchnorm_affine(3, device, dtype, torch.float)
def _test_batchnorm_simple_average(self, device, dtype, module_dtype=None):
module_dtype = module_dtype or dtype
module = nn.BatchNorm1d(3, momentum=None).to(dtype=module_dtype, device=device)
zeros = torch.zeros(3, dtype=module_dtype, device=device)
ones = torch.ones(3, dtype=module_dtype, device=device)
self.assertEqual(module.running_mean, zeros)
self.assertEqual(module.running_var, ones)
data1 = torch.rand(4, 3, dtype=dtype, device=device)
data2 = torch.rand(4, 3, dtype=dtype, device=device)
# 1st pass
res1 = module(data1)
running_mean1 = module.running_mean.clone()
running_var1 = module.running_var.clone()
self.assertNotEqual(running_mean1, zeros)
self.assertNotEqual(running_var1, ones)
# reset stats
module.reset_running_stats()
self.assertEqual(module.running_mean, zeros)
self.assertEqual(module.running_var, ones)
# 2nd pass
res2 = module(data2)
running_mean2 = module.running_mean.clone()
running_var2 = module.running_var.clone()
self.assertNotEqual(running_mean2, zeros)
self.assertNotEqual(running_var2, ones)
# reset stats
module.reset_running_stats()
self.assertEqual(module.running_mean, zeros)
self.assertEqual(module.running_var, ones)
# 3rd (combined) pass
res3 = module(data1)
res4 = module(data2)
self.assertEqual(res3, res1)
self.assertEqual(res4, res2)
self.assertEqual(module.running_mean, (running_mean1 + running_mean2) / 2)
self.assertEqual(module.running_var, (running_var1 + running_var2) / 2)
@dtypes(torch.float)
@dtypesIfCUDA(torch.float, torch.bfloat16)
def test_batchnorm_simple_average(self, device, dtype):
self._test_batchnorm_simple_average(device, dtype)
if self.device_type == 'cuda' and self.has_cudnn():
with torch.backends.cudnn.flags(enabled=False):
self._test_batchnorm_simple_average(device, dtype)
@onlyCUDA
@dtypes(torch.bfloat16, torch.half)
def test_batchnorm_simple_average_mixed(self, device, dtype):
self._test_batchnorm_simple_average(device, dtype, torch.float)
if self.device_type == 'cuda' and self.has_cudnn():
with torch.backends.cudnn.flags(enabled=False):
self._test_batchnorm_simple_average(device, dtype, torch.float)
@onlyNativeDeviceTypes
@dtypes(torch.float, torch.double)
def test_grid_sample_nan_inf(self, device, dtype):
input = torch.zeros([1, 1, 3, 3], device=device, dtype=dtype)
grid = torch.tensor([[[[nan, 0], [0, inf]]]], device=device, dtype=dtype)
for padding_mode in ('reflection', 'border', 'zeros'):
sample = torch.nn.functional.grid_sample(input=input, grid=grid, mode='nearest',
padding_mode=padding_mode, align_corners=False)
self.assertEqual(sample, torch.zeros([1, 1, 1, 2], device=device, dtype=dtype))
def test_CTCLoss_empty_target(self, device):
target_lengths = [0, 0, 0]
input_lengths = [50, 50, 50]
targets = torch.randint(1, 15, (0,), dtype=torch.long, device=device)
log_probs = torch.randn(50, 3, 15, dtype=torch.double, device=device).log_softmax(2)
loss = torch.nn.functional.ctc_loss(log_probs, targets, input_lengths, target_lengths, reduction='none')
self.assertTrue((loss >= 0).all().item())
self.assertEqual(-log_probs.sum(0)[:, 0], loss)
target_lengths = [0, 9, 0]
input_lengths = [50, 50, 50]
targets = torch.randint(1, 15, (9,), dtype=torch.long, device=device)
log_probs = torch.randn(50, 3, 15, dtype=torch.double, device=device).log_softmax(2)
loss = torch.nn.functional.ctc_loss(log_probs, targets, input_lengths, target_lengths, reduction='none')
self.assertTrue((loss >= 0).all().item())
self.assertEqual(-log_probs.sum(0)[[0, 2], 0], loss[[0, 2]])
# Merge into OpInfo?
@skipCUDAIf(True, """Test is flaky on Linux and Windows, typical error message:
https://github.com/pytorch/pytorch/issues/34870""")
def test_ctc_loss(self, device):
batch_size = 64
num_labels = 101
target_length = 15
gradcheck_input_size = 10
ZERO_NONE = 0
ZERO_SOME = 1
ZERO_ALL = 2
# input_length, vary_lengths, zero_lengths
tests = [(150, False, ZERO_NONE),
(150, True, ZERO_NONE),
(50, True, ZERO_SOME),
(50, True, ZERO_ALL)]
if 'cuda' in device:
tests += [(50, False, ZERO_NONE),
(50, True, ZERO_NONE),
(150, True, ZERO_SOME),
(150, True, ZERO_ALL)]
for input_length, vary_lengths, zero_mode in tests:
targets = torch.randint(1, num_labels, (batch_size, target_length),
device=device, dtype=torch.long)
x = torch.randn(gradcheck_input_size, dtype=torch.double, device=device, requires_grad=True)
tile_factors = torch.randn(input_length * batch_size * num_labels // gradcheck_input_size + 1,
device=device)
input_lengths = [(torch.randint(input_length // 2, input_length + 1, ()).item()
if vary_lengths or i == 0 else input_length) for i in range(batch_size)]
if zero_mode == ZERO_ALL:
target_lengths = [0 for _ in range(batch_size)]
else:
target_lengths = [(torch.randint(target_length // 2, target_length + 1, ()).item()
if vary_lengths else target_length) for _ in range(batch_size)]
if zero_mode == ZERO_SOME:
idxes = torch.randint(0, batch_size, (10,))
for i in idxes:
target_lengths[i] = 0
def ctc_after_softmax(x):
x_full = ((x[:, None] * tile_factors[None, :]).view(-1)[:input_length * batch_size * num_labels]
.view(input_length, batch_size, num_labels))
log_probs = torch.log_softmax(x_full, 2)
return torch.nn.functional.ctc_loss(log_probs, targets, input_lengths, target_lengths)
gradcheck(ctc_after_softmax, [x])
@onlyCUDA
@skipCUDAIfRocm(msg="skipped Cudnn test on ROCm")
@skipCUDAIfCudnnVersionLessThan(7600)
def test_ctc_loss_cudnn(self, device):
batch_size = 16
input_length = 30
num_labels = 101
target_length = 15
targets = torch.randint(1, num_labels, (batch_size * target_length,),
device='cuda', dtype=torch.long)
log_probs = torch.log_softmax(torch.randn(input_length, batch_size, num_labels, device='cuda', dtype=torch.float), 2)
log_probs.requires_grad_()
input_lengths = batch_size * [input_length]
target_lengths = batch_size * [target_length]
grad_out = torch.randn(batch_size, device='cuda', dtype=torch.float)
with torch.backends.cudnn.flags(enabled=False):
loss_native = torch.nn.functional.ctc_loss(log_probs, targets, input_lengths, target_lengths, reduction='none')
grad_native, = torch.autograd.grad(loss_native, log_probs, grad_out)
loss_cudnn = torch.nn.functional.ctc_loss(log_probs, targets.to('cpu', torch.int32),
input_lengths, target_lengths, reduction='none')
self.assertTrue("Cudnn" in str(loss_cudnn.grad_fn))
grad_cudnn, = torch.autograd.grad(loss_cudnn, log_probs, grad_out)
self.assertEqual(grad_cudnn, grad_native, atol=1e-4, rtol=0)
@dtypesIfCUDA(torch.half, torch.float, torch.double)
@dtypes(torch.float)
@tf32_on_and_off(0.005)
@skipIfTorchDynamo("TorchDynamo fails here for unknown reasons")
def test_variable_sequence(self, device, dtype):
def pad(var, length):
if var.size(0) == length:
return var
return torch.cat([var, var.new_zeros(length - var.size(0), *var.size()[1:])])
def maybe_index_tuple(maybe_tuple_of_tensors, index):
if maybe_tuple_of_tensors is None:
return None
return tuple(maybe_tuple_of_tensors[j][:, index:index + 1, :].contiguous()
for j in range(2))
def check_lengths(lengths, enforce_sorted, use_default_hiddens, proj_size):
input_size = 3
hidden_size = 4
num_layers = 2
bidirectional = True
max_length = max(lengths)
x_leaf = torch.randn(max_length, len(lengths), input_size, device=device,
dtype=dtype, requires_grad=True)
num_directions = 2 if bidirectional else 1
lstm = nn.LSTM(input_size, hidden_size, bidirectional=bidirectional,
num_layers=num_layers, proj_size=proj_size).to(device, dtype)
lstm2 = deepcopy(lstm).to(device, dtype)
x = x_leaf
hidden0 = None
if not use_default_hiddens:
real_hidden_size = hidden_size if proj_size == 0 else proj_size
hidden0 = (torch.randn(num_directions * num_layers, len(lengths), real_hidden_size,
device=device, dtype=dtype),
torch.randn(num_directions * num_layers, len(lengths), hidden_size,
device=device, dtype=dtype))
# Compute sequences separately
seq_outs = []
seq_hiddens = []
for i, l in enumerate(lengths):
hidden_i = maybe_index_tuple(hidden0, i)
out, hid = lstm2(x[:l, i:i + 1], hidden_i)
out_pad = pad(out, max_length)
seq_outs.append(out_pad)
seq_hiddens.append(hid)
seq_out = torch.cat(seq_outs, 1)
seq_hidden = tuple(torch.cat(hids, 1) for hids in zip(*seq_hiddens))
# Use packed format
packed = rnn_utils.pack_padded_sequence(x, lengths, enforce_sorted=enforce_sorted)
packed_out, packed_hidden = lstm(packed, hidden0)
unpacked, unpacked_len = rnn_utils.pad_packed_sequence(packed_out)
# Check forward
prec = dtype2prec_DONTUSE[dtype]
self.assertEqual(packed_hidden, seq_hidden, atol=prec, rtol=0)
self.assertEqual(unpacked, seq_out, atol=prec, rtol=0)
self.assertEqual(unpacked_len, lengths, atol=prec, rtol=0)
# Check backward
seq_out.sum().backward()
grad_x = x_leaf.grad.data.clone()
x_leaf.grad.data.zero_()
unpacked.sum().backward()
self.assertEqual(x_leaf.grad, grad_x, atol=dtype2prec_DONTUSE[dtype], rtol=0)
for p1, p2 in zip(lstm.parameters(), lstm2.parameters()):
prec = dtype2prec_DONTUSE[dtype]
if dtype == torch.float16:
prec = 4e-2
self.assertEqual(p1.grad, p2.grad, atol=prec, rtol=0)
tests = [
# enforce_sorted, lengths
[True, [5]],
[False, [5]],
[True, [10, 10, 6, 2, 2, 1, 1]],
[False, [10, 10, 6, 2, 2, 1, 1]],
[False, [2, 1, 3, 2, 10, 5, 3]],
]
for enforce_sorted, seq_lens, in tests:
for use_default_hiddens in (True, False):
for proj_size in [0, 2]:
check_lengths(seq_lens, enforce_sorted, use_default_hiddens, proj_size)
def _test_batchnorm_update_stats(self, device, dtype=torch.float):
module = nn.BatchNorm1d(3).to(device, dtype)
data = torch.rand(4, 3, device=device, dtype=dtype)
# training pass
old_running_mean = module.running_mean.clone()
old_running_var = module.running_var.clone()
old_num_batches_tracked = module.num_batches_tracked.clone()
module(data)
self.assertNotEqual(old_running_mean, module.running_mean)
self.assertNotEqual(old_running_var, module.running_var)
self.assertEqual(old_num_batches_tracked + 1, module.num_batches_tracked)
# eval pass
module.eval()
old_running_mean = module.running_mean.clone()
old_running_var = module.running_var.clone()
old_num_batches_tracked = module.num_batches_tracked.clone()
module(data)
self.assertEqual(old_running_mean, module.running_mean)
self.assertEqual(old_running_var, module.running_var)
self.assertEqual(old_num_batches_tracked, module.num_batches_tracked)
def test_batchnorm_update_stats(self, device):
self._test_batchnorm_update_stats(device)
if self.device_type == 'cuda' and self.has_cudnn():
with torch.backends.cudnn.flags(enabled=False):
self._test_batchnorm_update_stats(device)
@onlyCPU
@dtypes(torch.bfloat16, torch.float16)
def test_activations_bfloat16_half_cpu(self, device, dtype):
def test_helper(fn, device, inp_dims, prec=None):
torch.manual_seed(37)
# bfloat16/half compute
fn = fn.to(dtype=dtype)
input = torch.randn(inp_dims, dtype=dtype, device=device, requires_grad=True)
out = fn(input)
grad_input = torch.randn_like(out, dtype=dtype, device=device)
out.backward(grad_input)
# fp32 compute
input2 = input.detach().clone().float().requires_grad_(True)
out2 = fn.float()(input2)
grad_input2 = grad_input.detach().clone().float()
out2.backward(grad_input2)
self.assertEqual(out.dtype, dtype)
self.assertEqual(input.grad.dtype, dtype)
self.assertEqual(out, out2.to(dtype=dtype), atol=prec, rtol=prec)
self.assertEqual(input.grad.data, input2.grad.data.to(dtype=dtype), atol=prec, rtol=prec)
shapes = [[1, 3, 1, 6], [1, 3, 1, 128], [1, 3, 256, 256]]
for shape in shapes:
test_helper(torch.nn.LogSigmoid(), device, shape)
test_helper(torch.nn.Hardsigmoid(), device, shape)
test_helper(torch.nn.Hardshrink(), device, shape)
test_helper(torch.nn.Softshrink(), device, shape)
test_helper(torch.nn.Hardswish(), device, shape)
test_helper(torch.nn.Softplus(), device, shape)
test_helper(torch.nn.SiLU(), device, shape)
test_helper(torch.nn.Hardtanh(), device, shape)
test_helper(torch.nn.Mish(), device, shape)
test_helper(torch.nn.ELU(), device, shape)
test_helper(torch.nn.PReLU(), device, shape)
test_helper(torch.nn.GLU(), device, shape, prec=1e-2)
test_helper(torch.nn.Threshold(0.1, 20), device, shape)
test_helper(torch.nn.GELU(), device, shape)
test_helper(torch.nn.Hardtanh(), device, shape)
test_helper(torch.nn.LeakyReLU(), device, shape)
@onlyCUDA
def test_activations_bfloat16(self, device):
_test_bfloat16_ops(self, torch.nn.ReLU(), device, inp_dims=(5), prec=1e-2)
_test_bfloat16_ops(self, torch.nn.Threshold(0.1, 20), device, inp_dims=(5), prec=1e-2)
_test_bfloat16_ops(self, torch.nn.ELU(), device, inp_dims=(5), prec=1e-2)
_test_bfloat16_ops(self, torch.nn.Softplus(), device, inp_dims=(5), prec=1e-2)
_test_bfloat16_ops(self, torch.nn.Hardshrink(), device, inp_dims=(5), prec=1e-2)
_test_bfloat16_ops(self, torch.nn.Softshrink(), device, inp_dims=(5), prec=1e-2)
_test_bfloat16_ops(self, torch.nn.LeakyReLU(), device, inp_dims=(5), prec=1e-2)
@onlyNativeDeviceTypes
def test_softmax_bfloat16(self, device):
for dim in [0, 1, 2, 3]:
_test_bfloat16_ops(self, torch.nn.Softmax(dim=dim), device, inp_dims=(16, 33, 15, 16), prec=1e-2)
# test softmax with large input value which casues exp() to overflow
_test_bfloat16_ops(self, torch.nn.Softmax(dim=dim), device, inp_dims=(16, 33, 15, 16), prec=0.05, scale_factor=1000.0)
def test_nll_loss_mismatched_batch(self, device):
x = torch.randn((10, 3), requires_grad=True, device=device)
# t should have size (10,)
t = torch.zeros((3,), dtype=torch.int64, device=device)
with self.assertRaisesRegex(ValueError, 'Expected.*batch_size'):
F.nll_loss(x, t)
def test_nll_loss_out_of_bounds_ignore_index(self, device):
x = torch.randn(6, 3, requires_grad=True, device=device)
t = torch.tensor([0, 1, 255, 0, 1, 2], dtype=torch.int64, device=device)
for reduction in ['mean', 'none']:
F.nll_loss(x, t, ignore_index=255, reduction=reduction).sum().backward()
def test_nll_loss_invalid_target_dim(self, device):
x = torch.randn((10, 3), device=device)
t = torch.zeros((10, 2), dtype=torch.int64, device=device)
with self.assertRaisesRegex(RuntimeError, "1D target tensor expected"):
F.nll_loss(x, t)
def test_nll_loss_invalid_weights(self, device):
x = torch.randn((10, 3), device=device)
t = torch.empty(10, dtype=torch.int64, device=device).random_(0, 3)
invalid_weights = [
torch.randn(4, device=device),
torch.randn(1, 3, device=device),
]
msg = "weight tensor should be defined either for all 3 classes or no classes"
for weight in invalid_weights:
with self.assertRaisesRegex(RuntimeError, msg):
F.nll_loss(x, t, weight=weight)
# Ref: https://github.com/pytorch/pytorch/issue/85005
@onlyCUDA
@largeTensorTest("45GB", "cpu")
@largeTensorTest("45GB", "cuda")
@parametrize_test("reduction", ("none", "mean", "sum"))
def test_nll_loss_large_tensor(self, device, reduction):
shape = [int(2 ** 16), int(2 ** 16) + 1]
input = torch.randn(shape, device=device, dtype=torch.float32, requires_grad=True)
labels = torch.randint(shape[0], (shape[0],), dtype=torch.long, device=device)
out = F.nll_loss(input, labels, reduction=reduction)
with torch.no_grad():
input_cpu = input.cpu().float().requires_grad_()
labels_cpu = labels.cpu()
out_cpu = F.nll_loss(input_cpu, labels_cpu, reduction=reduction)
# workaround to reduce memory usage vs. self.assertEqual, see #84944
rtol, atol = torch.testing._comparison.get_tolerances(torch.float32, rtol=None, atol=None)
if reduction == "sum":
orig_rtol, orig_atol = rtol, atol
rtol, atol = 7 * rtol, 3 * atol
with torch.no_grad():
self.assertTrue(torch.allclose(out.cpu(), out_cpu, rtol=rtol, atol=atol))
if reduction == "sum":
rtol, atol = orig_rtol, orig_atol
if reduction != "none":
out.backward()
out_cpu.backward()
with torch.no_grad():
self.assertTrue(torch.allclose(input.grad.cpu(), input_cpu.grad, rtol=rtol, atol=atol))
def _nll_loss_helper(self, input_size, reduction, expected, device):
input = torch.rand(input_size, requires_grad=True, device=device)
num_channels = input_size[1]
target_size = (input_size[0], ) + tuple(input_size[2:])
target = torch.randint(num_channels, target_size, device=device)
output = F.nll_loss(input, target, reduction=reduction)
self.assertEqual(output, expected, exact_dtype=False)
output.sum().backward()
self.assertEqual(input.grad.size(), input.size())
def test_nll_loss_empty_tensor_reduction_none(self, device):
self._nll_loss_helper([0, 3], "none", torch.empty([0], device=device), device)
self._nll_loss_helper([0, 3, 5, 7], "none", torch.empty([0, 5, 7], device=device), device)
self._nll_loss_helper([2, 3, 0, 7], "none", torch.empty([2, 0, 7], device=device), device)
self._nll_loss_helper([2, 3, 5, 0], "none", torch.empty([2, 5, 0], device=device), device)
self._nll_loss_helper([2, 3, 5, 7, 0], "none", torch.empty([2, 5, 7, 0], device=device), device)
def test_nll_loss_empty_tensor_reduction_mean(self, device):
nan = torch.tensor(float('nan'), device=device)
self._nll_loss_helper([0, 3], "mean", nan, device)
self._nll_loss_helper([0, 3, 5, 7], "mean", nan, device)
self._nll_loss_helper([2, 3, 0, 7], "mean", nan, device)
self._nll_loss_helper([2, 3, 5, 0], "mean", nan, device)
self._nll_loss_helper([2, 3, 5, 7, 0], "mean", nan, device)
def test_nll_loss_empty_tensor_reduction_sum(self, device):
zero = torch.tensor(0, device=device)
self._nll_loss_helper([0, 3], "sum", zero, device)
self._nll_loss_helper([0, 3, 5, 7], "sum", zero, device)
self._nll_loss_helper([2, 3, 0, 7], "sum", zero, device)
self._nll_loss_helper([2, 3, 5, 0], "sum", zero, device)
self._nll_loss_helper([2, 3, 5, 7, 0], "sum", zero, device)
def test_nll_loss_total_weight_is_zero(self, device):
def helper(input_size):
input = torch.ones(input_size, requires_grad=True, device=device)
num_channels = input_size[1]
target_size = (input_size[0], ) + tuple(input_size[2:])
target = torch.zeros(target_size, dtype=torch.long, device=device)
weight = torch.zeros([num_channels], device=device)
self.assertEqual(F.nll_loss(input, target, weight, reduction="sum").item(), 0.)
self.assertEqual(F.nll_loss(input, target, weight, reduction="mean").item(), float("nan"))
self.assertEqual(F.nll_loss(input, target, weight, reduction="none"), torch.zeros(target.shape, device=device))
helper([2, 3])
helper([2, 3, 5, 7])
helper([2, 3, 5, 7, 9])
def test_nll_loss_all_ignored(self, device):
def helper(input_size):
input = torch.ones(input_size, device=device)
num_channels = input_size[1]
target_size = (input_size[0], ) + tuple(input_size[2:])
target = torch.zeros(target_size, dtype=torch.long, device=device)
self.assertEqual(F.nll_loss(input, target, ignore_index=0, reduction="sum").item(), 0)
self.assertEqual(F.nll_loss(input, target, ignore_index=0, reduction="mean").item(), float("nan"))
self.assertEqual(F.nll_loss(input, target, ignore_index=0, reduction="none"), torch.zeros(target.shape, device=device))
helper([2, 3])
helper([2, 3, 5, 7])
helper([2, 3, 5, 7, 9])
def test_nll_loss_byte_target_matches_long(self, device):
N, C = 10, 4
input = torch.randn(N, C, device=device, requires_grad=True)
target = torch.empty(N, dtype=torch.long, device=device).random_(0, C)
def compute_result_and_gradient(reduction, target_dtype):
input_ = input.detach()
input_.requires_grad_()
prob = F.log_softmax(input_, dim=-1)
loss = nn.NLLLoss(reduction=reduction)
result = loss(prob, target.to(target_dtype))
result.sum().backward()
return result, input_.grad
for reduction in ["none", "mean", "sum"]:
result_long, grad_long = compute_result_and_gradient(reduction, torch.long)
result_byte, grad_byte = compute_result_and_gradient(reduction, torch.uint8)
self.assertEqual(result_long, result_byte)
self.assertEqual(grad_long, grad_byte)
def test_cross_entropy_loss_prob_target_all_reductions(self, device):
# Test with k-dimensional loss.
for k in range(5):
N, C = 5, 4
other_dims = [torch.randint(2, 5, size=(1,)).item() for _ in range(k)]
input = torch.randn(N, C, *other_dims, device=device, requires_grad=True)
target = torch.randn(N, C, *other_dims, device=device, requires_grad=True)
weight = torch.randn(C, device=device).abs()
for reduction, w in product(['none', 'mean', 'sum'], [None, weight]):
m = torch.nn.CrossEntropyLoss(weight=w, reduction=reduction)
output = m(input, target)
output_ref = loss_reference_fns['CrossEntropyLoss'](
input, target, reduction=reduction, weight=w)
self.assertEqual(output, output_ref)
def test_cross_entropy_loss_prob_target_unit_weights(self, device):
# Test with k-dimensional loss.
for k in range(5):
N, C = 5, 4
other_dims = [torch.randint(2, 5, size=(1,)).item() for _ in range(k)]
input = torch.randn(N, C, *other_dims, device=device, requires_grad=True)
target = torch.randn(N, C, *other_dims, device=device, requires_grad=True)
for reduction in ['none', 'mean', 'sum']:
# Ensure result with unit weights is equivalent to result without weights.
m = torch.nn.CrossEntropyLoss(reduction=reduction)
unit_weight = torch.ones(C, device=device, dtype=target.dtype)
m_unit = torch.nn.CrossEntropyLoss(weight=unit_weight, reduction=reduction)
output = m(input, target)
output_unit = m_unit(input, target)
self.assertEqual(output, output_unit)
@parametrize_test('reduction', ['none', 'mean', 'sum'])
@parametrize_test('weighted', [False, True])
def test_cross_entropy_loss_prob_target_no_batch_dim(self, device, reduction, weighted):
C = 5
input = torch.randn(C, device=device).log_softmax(dim=-1)
target = torch.randn(C, device=device).softmax(dim=-1)
weight = torch.randn(C, device=device) if weighted else None
m = nn.CrossEntropyLoss(reduction=reduction, weight=weight)
loss_no_batch = m(input, target)
loss_batch = m(input.unsqueeze(0), target.unsqueeze(0))
if reduction == 'none':
loss_batch = loss_batch.squeeze(0)
self.assertEqual(loss_no_batch, loss_batch)
def test_cross_entropy_loss_index_target_unit_weights(self, device):
# Test with k-dimensional loss.
for k in range(5):
N, C = 5, 4
other_dims = [torch.randint(2, 5, size=(1,)).item() for _ in range(k)]
input = torch.randn(N, C, *other_dims, device=device, requires_grad=True)
target = torch.empty(N, *other_dims, dtype=torch.long, device=device).random_(0, C)
for reduction in ['none', 'mean', 'sum']:
# Ensure result with unit weights is equivalent to result without weights.
m = torch.nn.CrossEntropyLoss(reduction=reduction)
unit_weight = torch.ones(C, device=device, dtype=input.dtype)
m_unit = torch.nn.CrossEntropyLoss(weight=unit_weight, reduction=reduction)
output = m(input, target)
output_unit = m_unit(input, target)
self.assertEqual(output, output_unit)
def test_cross_entropy_loss_one_hot_target(self, device):
# Test with k-dimensional loss.
for k in range(5):
N, C = 5, 4
other_dims = [torch.randint(2, 5, size=(1,)).item() for _ in range(k)]
input = torch.randn(N, C, *other_dims, device=device, requires_grad=True)
target = torch.empty(N, *other_dims, dtype=torch.long, device=device).random_(0, C)
weight = torch.randn(C, device=device).abs()
# Get one-hot representation of the target.
target_one_hot = F.one_hot(target, num_classes=C).to(input.dtype)
# Need to put the C dim at index 1.
target_one_hot = target_one_hot.permute(0, -1, *range(1, target_one_hot.dim() - 1))
for reduction, w in product(['none', 'mean', 'sum'], [None, weight]):
# Skip this case for now because soft and hard label CE are not consistent
# in the way they apply class weights (see issue #61309).
if reduction == 'mean' and weight is not None:
continue
# Ensure loss computed with class indices matches loss
# computed with one-hot class probs.
m = torch.nn.CrossEntropyLoss(weight=w, reduction=reduction)
output = m(input, target)
output_one_hot = m(input, target_one_hot)
self.assertEqual(output, output_one_hot)
def test_cross_entropy_label_smoothing_errors(self, device):
N, C = 3, 4
input_args = [
(torch.randn((N, C), device=device), torch.arange(0, C, device=device)),
(torch.randn((N, C), device=device), torch.randn(N, C, device=device))
]
for input_arg in input_args:
loss = nn.CrossEntropyLoss(label_smoothing=1.2)
with self.assertRaisesRegex(RuntimeError,
r"label_smoothing must be between 0\.0"):
loss(*input_arg)
@set_default_dtype(torch.double)
def test_cross_entropy_label_smoothing_consistent_index_target_and_probs(self, device):
N, C = 10, 4
ks = range(5)
reductions = ['none', 'mean', 'sum']
label_smoothings = [0.05, 0.15]
for k, reduction, label_smoothing in product(ks, reductions, label_smoothings):
other_dims = [torch.randint(2, 5, size=(1,)).item() for _ in range(k)]
input = torch.randn(N, C, *other_dims, device=device, requires_grad=True)
target = torch.empty(N, *other_dims, dtype=torch.long, device=device).random_(0, C)
# construct target probablity that should have the same result as label_smoothing
target_proba = F.one_hot(target, num_classes=C)
# Need to put the C dim at index 1.
target_proba = target_proba.permute(0, -1, *range(1, target_proba.dim() - 1))
target_mask = (target_proba == 1)
target_proba = target_proba.to(dtype=input.dtype)
# y_k^ls = y_k * (1 - label_smoothing) + label_smoothing / n_classes
# Get one-hot representation of the target.
target_proba.masked_fill_(target_mask, 1 - label_smoothing + label_smoothing / C)
target_proba.masked_fill_(~target_mask, label_smoothing / C)
loss = nn.CrossEntropyLoss(reduction=reduction)
output_with_prob = loss(input, target_proba)
loss = nn.CrossEntropyLoss(
reduction=reduction, label_smoothing=label_smoothing)
output_with_index = loss(input, target)
self.assertEqual(output_with_prob, output_with_index,
rtol=1e-07, atol=1e-05)
def test_cross_entropy_label_smoothing_with_probs(self, device):
N, C = 10, 4
ks = range(5)
reductions = ['none', 'mean', 'sum']
label_smoothings = [0.05, 0.15]
# Test with k-dimensional loss.
for k, label_smoothing in product(ks, label_smoothings):
other_dims = [torch.randint(2, 5, size=(1,)).item() for _ in range(k)]
input = torch.randn(N, C, *other_dims, device=device, requires_grad=True)
target = F.log_softmax(torch.randn(N, C, *other_dims, device=device), dim=1)
for reduction in reductions:
# use with label_smoothing
loss = nn.CrossEntropyLoss(reduction=reduction, label_smoothing=label_smoothing)
output_with_smoothing = loss(input, target)
# manually smoothing target
# class_proba^ls = class_proba * (1 - label_smoothing) +
# label_smoothing / n_classes
target_with_smoothing = target * (1 - label_smoothing) + label_smoothing / C
loss = nn.CrossEntropyLoss(reduction=reduction)
output_with_manual_smoothing = loss(input, target_with_smoothing)
self.assertEqual(output_with_smoothing, output_with_manual_smoothing)
def test_cross_entropy_label_smoothing_weight_ignore_indices(self, device):
reductions = ['none', 'sum', 'mean']
label_smoothings = [0.05, 0.15]
weight = torch.tensor([0.3, 0.6], device=device)
inp1 = torch.tensor([[0.3, 0.4], [1, 2]], device=device)
inp2 = torch.tensor([[0.3, 0.6], [1, 2]], device=device)
targ_default_ignore_index = torch.tensor([-100, 1], device=device)
targ_negative_ignore_index = torch.tensor([-2, 1], device=device)
targ_positive_ignore_index = torch.tensor([2, 1], device=device)
for reduction, label_smoothing, weight in product(reductions, label_smoothings, (None, weight)):
def check_equal(loss, inp_targ_1, inp_targ_2):
inp1, targ1 = inp_targ_1
inp2, targ2 = inp_targ_2
l1 = loss(inp1, targ1)
l2 = loss(inp2, targ2)
self.assertEqual(l1, l2)
# Default ignore_index
loss = nn.CrossEntropyLoss(reduction=reduction,
label_smoothing=label_smoothing,
weight=weight)
check_equal(loss, (inp1, targ_default_ignore_index), (inp2, targ_default_ignore_index))
if reduction != 'none':
# Check that we correctly tally the denominator for `mean`
# i.e. we don't count the ignored_idx at all.
check_equal(loss, (inp1, targ_default_ignore_index), (inp2[1:], targ_default_ignore_index[1:]))
# negative ignore_index
loss = nn.CrossEntropyLoss(reduction=reduction,
label_smoothing=label_smoothing,
ignore_index=-2,
weight=weight)
check_equal(loss, (inp1, targ_negative_ignore_index), (inp2, targ_negative_ignore_index))
if reduction != 'none':
# Check that we correctly tally the denominator for `mean`
# i.e. we don't count the ignored_idx at all.
check_equal(loss, (inp1, targ_negative_ignore_index), (inp2[1:], targ_negative_ignore_index[1:]))
# positive ignore_index
loss = nn.CrossEntropyLoss(reduction=reduction,
label_smoothing=label_smoothing,
ignore_index=2,
weight=weight)
check_equal(loss, (inp1, targ_positive_ignore_index), (inp2, targ_positive_ignore_index))
if reduction != 'none':
# Check that we correctly tally the denominator for `mean`
# i.e. we don't count the ignored_idx at all.
check_equal(loss, (inp1, targ_positive_ignore_index), (inp2[1:], targ_positive_ignore_index[1:]))
# Ref: https://github.com/pytorch/pytorch/issue/85005
@onlyCUDA
@largeTensorTest("45GB", "cpu")
@largeTensorTest("45GB", "cuda")
@parametrize_test("reduction", ("none", "mean", "sum"))
def test_cross_entropy_large_tensor(self, device, reduction):
logits = torch.randn(int(2 ** 16), int(2 ** 16) + 1, dtype=torch.float32, device='cuda', requires_grad=True)
labels = torch.zeros(logits.size(0), dtype=torch.long, device='cuda')
loss = F.cross_entropy(logits, labels, reduction=reduction)
if reduction != "none":
loss.backward()
with torch.no_grad():
logits_cpu = logits.cpu().detach().requires_grad_()
labels_cpu = labels.cpu().detach()
loss_cpu = F.cross_entropy(logits_cpu, labels_cpu, reduction=reduction)
if reduction != "none":
loss_cpu.backward()
# workaround to reduce memory usage vs. self.assertEqual, see #84944
rtol, atol = torch.testing._comparison.get_tolerances(torch.float32, rtol=None, atol=None)
self.assertTrue(torch.allclose(loss.cpu(), loss_cpu, rtol=rtol, atol=atol))
if reduction != "none":
self.assertTrue(torch.allclose(logits.grad.cpu(), logits_cpu.grad, rtol=rtol, atol=atol))
def test_smoothl1loss_backward_zero_beta(self, device):
input = torch.randn(300, 256, requires_grad=True, device=device)
target = input.detach()
loss = F.smooth_l1_loss(input, target, beta=0.0, reduction='sum')
loss.backward()
grad_max_abs = input.grad.abs().max().item()
self.assertLessEqual(grad_max_abs, 1.0)
def test_softshrink_negative(self, device):
input = torch.randn(5, device=device, requires_grad=True)
m = torch.nn.Softshrink(-1)
with self.assertRaisesRegex(RuntimeError,
r'lambda must be greater or equal to 0, but found to be -1\.'):
m(input)
def test_fold(self, device):
def test_dtype(fn, input, dtype):
input = input.detach().clone().to(dtype=dtype).requires_grad_(True)
input2 = input.detach().clone().float().requires_grad_(True)
out = fn(input)
out.sum().backward()
out2 = fn(input2)
out2.sum().backward()
self.assertEqual(out.dtype, dtype)
self.assertEqual(input.grad.dtype, dtype)
self.assertEqual(out, out2.to(dtype=dtype), atol=0.05, rtol=0)
self.assertEqual(input.grad, input2.grad.to(dtype=dtype))
def func(x):
return F.fold(x, output_size=(4, 5), kernel_size=(2, 2))
seeds = (44, 83, 71, 25, 999)
for sd in seeds:
torch.manual_seed(sd)
x = torch.randn(1, 12, 12, device=device, requires_grad=True, dtype=torch.double)
gradcheck(func, [x], check_forward_ad=True)
gradgradcheck(func, [x], check_fwd_over_rev=True)
if device == 'cpu':
test_dtype(func, x, torch.bfloat16)
def test_logsigmoid_out(self, device):
# this isn't actually documented, but was broken previously:
# https://github.com/pytorch/pytorch/issues/36499
x = torch.randn(2, 3, device=device).t()
empty_out = torch.randn(0, device=device)
self.assertEqual(F.logsigmoid(x), F.logsigmoid(x, out=empty_out))
noncontig_out = torch.randn(2, 3, device=device).t()
self.assertEqual(F.logsigmoid(x), F.logsigmoid(x, out=noncontig_out))
# Check that clip_grad_norm_ raises an error if the total norm of the
# parameters' gradients is non-finite
def test_clip_grad_norm_error_if_nonfinite(self, device):
norms_pos = [0.1, 1, 2, 3.5, inf]
norms_neg = [-0.1, -1, -2, -3.5]
norms_except_0 = norms_pos + norms_neg
norms_all = norms_except_0 + [0]
# Each entry in test_cases has the following values, in this order:
#
# grad_only_one_elem If True, only one element of the parameter's
# gradient is set to the scalar grad, and the
# rest of the elements are 0. If False, all grad
# elements are equal to the scalar.
#
# prefix_finite_grad_param If True, prefix a parameter that has a grad
# of 1.
#
# scalars Scalars to use as the parameter's grad, through
# multiplication
#
# norms_nonfinite Norm types that should produce nonfinite total norm
#
# norms_finite Norm types that should produce finite total norm
test_cases = [
# Test errors from an infinite grad
(False, False, [inf, -inf], norms_except_0, [0]),
(False, True, [inf, -inf], norms_pos, norms_neg + [0]),
(True, False, [inf, -inf], norms_pos, norms_neg + [0]),
(True, True, [inf, -inf], norms_pos, norms_neg + [0]),
# Test errors from a NaN grad
(False, False, [nan], norms_except_0, [0]),
(False, True, [nan], norms_except_0, [0]),
(True, False, [nan], norms_except_0, [0]),
(True, True, [nan], norms_except_0, [0]),
# Test a grad that should never error
(False, False, [2e22, -2e22], [], norms_all),
(False, True, [2e22, -2e22], [], norms_all),
(True, False, [2e22, -2e22], [], norms_all),
(True, True, [2e22, -2e22], [], norms_all),
# Test a grad that will overflow to inf for only some norm orders
(False, False, [2e200, -2e200], [3.5, 2, -2, -3.5], [inf, 1, 0.1, 0, -1, -0.1]),
(False, True, [2e200, -2e200], [3.5, 2], norms_neg + [inf, 1, 0.1, 0]),
(True, False, [2e200, -2e200], [3.5, 2], norms_neg + [inf, 1, 0.1, 0]),
(True, True, [2e200, -2e200], [3.5, 2], norms_neg + [inf, 1, 0.1, 0]),
]
def gen_parameters(scalar, grad_only_one_elem, prefix_finite_grad_param):
param = torch.ones(10, dtype=torch.float64, device=device, requires_grad=True)
if grad_only_one_elem:
param[1].mul(scalar).sum().backward()
else:
param.mul(scalar).sum().backward()
if prefix_finite_grad_param:
prefix_param = torch.ones(1, dtype=torch.float64, device=device, requires_grad=True)
prefix_param.mul(1).sum().backward()
parameters = [prefix_param, param]
else:
parameters = [param]
return parameters
def run_test_case(norm_type, error_if_nonfinite, scalar, grad_only_one_elem, prefix_finite_grad_param, is_norm_nonfinite):
msg = (
f'norm_type: {norm_type}, ',
f'error_if_nonfinite: {error_if_nonfinite}, '
f'scalar: {scalar}, '
f'grad_only_one_elem: {grad_only_one_elem}, '
f'prefix_finite_grad_param: {prefix_finite_grad_param}, '
f'is_norm_nonfinite: {is_norm_nonfinite}')
parameters = gen_parameters(scalar, grad_only_one_elem, prefix_finite_grad_param)
# Should only throw an error if the total norm is expected to be
# nonfinite and `error_if_nonfinite=True`
if is_norm_nonfinite and error_if_nonfinite:
error_msg = f'The total norm of order {float(norm_type)} for gradients'
grads_before = [p.grad.clone() for p in parameters]
with self.assertRaisesRegex(RuntimeError, error_msg, msg=msg):
clip_grad_norm_(parameters, 1, norm_type=norm_type, error_if_nonfinite=True)
# Grad should not change if error is thrown
grads_after = [p.grad for p in parameters]
self.assertEqual(grads_before, grads_after, msg=msg)
else:
clip_grad_norm_(parameters, 1, norm_type=norm_type, error_if_nonfinite=error_if_nonfinite)
for grad_only_one_elem, prefix_finite_grad_param, scalars, norms_nonfinite, norms_finite in test_cases:
for error_if_nonfinite in [False, True]:
for norm_type, scalar in product(norms_nonfinite, scalars):
run_test_case(norm_type, error_if_nonfinite, scalar, grad_only_one_elem, prefix_finite_grad_param, True)
for norm_type, scalar in product(norms_finite, scalars):
run_test_case(norm_type, error_if_nonfinite, scalar, grad_only_one_elem, prefix_finite_grad_param, False)
@onlyCUDA
@deviceCountAtLeast(2)
@parametrize_test('foreach', (False, True))
def test_clip_grad_norm_multi_device(self, devices, foreach):
class TestModel(nn.Module):
def __init__(self):
super().__init__()
self.layer1 = nn.Linear(10, 10)
self.layer2 = nn.Linear(10, 10)
test_model = TestModel()
test_model.layer1.to(devices[0])
test_model.layer2.to(devices[1])
ref_model = TestModel().to(devices[0])
for norm_type in [2., math.inf]:
for p in test_model.parameters():
p.grad = torch.ones_like(p)
for p in ref_model.parameters():
p.grad = torch.ones_like(p)
norm = clip_grad_norm_(test_model.parameters(), 0.5, norm_type=norm_type, foreach=foreach)
expected = clip_grad_norm_(ref_model.parameters(), 0.5, norm_type=norm_type, foreach=foreach)
self.assertEqual(norm, expected)
for p, pe in zip(test_model.parameters(), ref_model.parameters()):
self.assertEqual(p.grad.to(devices[0]), pe.grad)
def test_elu_inplace_overlap(self, device):
x = torch.randn((1, 6), dtype=torch.bfloat16, device=device).expand((6, 6))
with self.assertRaisesRegex(RuntimeError, 'unsupported operation'):
F.elu(x, inplace=True)
with self.assertRaisesRegex(RuntimeError, 'unsupported operation'):
F.elu_(x)
# Merge into OpInfo?
@onlyNativeDeviceTypes
def test_elu_inplace_with_neg_alpha(self, device):
a = torch.tensor([-1., 1.], device=device, requires_grad=True)
b = torch.nn.functional.elu_(a.clone(), alpha=-2)
with self.assertRaisesRegex(RuntimeError, "call out-of-place version"):
b.backward(torch.ones(2, device=device))
a = torch.tensor([-1., 1.], device=device, requires_grad=True)
b = torch.nn.functional.celu_(a.clone(), alpha=-2)
with self.assertRaisesRegex(RuntimeError, "call out-of-place version"):
b.backward(torch.ones(2, device=device))
@expectedFailureMeta # https://github.com/pytorch/pytorch/issues/54897
def test_hardswish_inplace_overlap(self, device):
x = torch.randn((1, 6), device=device).expand((6, 6))
with self.assertRaisesRegex(RuntimeError, 'unsupported operation'):
F.hardswish(x, inplace=True)
def test_silu_inplace_overlap(self, device):
x = torch.randn((1, 6), device=device).expand((6, 6))
with self.assertRaisesRegex(RuntimeError, 'unsupported operation'):
F.silu(x, inplace=True)
@onlyNativeDeviceTypes
def test_mish_inplace_overlap(self, device):
x = torch.randn((1, 6), device=device).expand((6, 6))
with self.assertRaisesRegex(RuntimeError, 'unsupported operation'):
F.mish(x, inplace=True)
def test_softplus_inplace_overlap(self, device):
x = torch.randn((1, 6), device=device).expand((6, 6))
with self.assertRaisesRegex(RuntimeError, 'unsupported operation'):
F.softplus(x, out=x)
def test_softplus_low_threshold(self, device):
# Ensure gradients are computed correctly with a low threshold.
model = torch.nn.Softplus(threshold=1).double()
input = torch.tensor(0.9, device=device, dtype=torch.double,
requires_grad=True)
output = model(input)
torch.autograd.gradcheck(model, input)
def test_softshrink_inplace_overlap(self, device):
x = torch.randn((1, 6), device=device).expand((6, 6))
with self.assertRaisesRegex(RuntimeError, 'unsupported operation'):
F.softshrink(x, out=x)
def test_leaky_relu_inplace_overlap(self, device):
x = torch.randn((1, 6), device=device).expand((6, 6))
with self.assertRaisesRegex(RuntimeError, 'unsupported operation'):
F.leaky_relu(x, inplace=True)
with self.assertRaisesRegex(RuntimeError, 'unsupported operation'):
F.leaky_relu_(x)
# Merge into OpInfo?
def test_leaky_relu_inplace_with_neg_slope(self, device):
a = torch.tensor([-1., 1.], device=device, requires_grad=True)
b = torch.nn.functional.leaky_relu_(a.clone(), -2)
with self.assertRaisesRegex(RuntimeError, "call out-of-place version"):
b.backward(torch.ones(2, device=device))
a = torch.tensor([-1., 1.], device=device, requires_grad=True)
b = torch.nn.functional.rrelu_(a.clone(), -5.0, 1.0)
with self.assertRaisesRegex(RuntimeError, "call out-of-place version"):
b.backward(torch.ones(2, device=device))
# Merge into OpInfo?
def test_leaky_relu_inplace_with_zero_slope(self, device):
a = torch.tensor([-2., 0., 2.], device=device, requires_grad=True)
b = torch.nn.functional.leaky_relu_(a.clone(), 0.0)
b.backward(torch.ones(3, device=device))
expected = torch.tensor([0., 0., 1.], device=device)
self.assertEqual(a.grad, expected)
a_bf16 = torch.tensor([-2., 0., 2.], device=device, dtype=torch.bfloat16, requires_grad=True)
b_bf16 = torch.nn.functional.leaky_relu_(a_bf16.clone(), 0.0)
b_bf16.backward(torch.ones(3, device=device))
expected_bf16 = torch.tensor([0., 0., 1.], device=device, dtype=torch.bfloat16)
self.assertEqual(a_bf16.grad, expected_bf16)
@onlyCPU
def test_softshrink(self, device):
x = torch.tensor([[1.21, 0.56, 0.5001, 0.4999, 1.2357, -0.4999, -0.5001, -1.154,
0.254, -0.24, -0.225, 0.104, 0.002, -0.001, 0.0574, 1.2344,
0.1748, -0.1797, -0.8125, 0.2051, -1.1328, 1.2344, -0.1562, 2.3554,
-0.1953, 0.0304, -0.3613, -1.3047, 1.0312, 0.1436, -0.6953, 0.5664,
-0.5820, -0.3301, 0.8203, 0.6133, 0.5938],
[-0.8203, -1.2344, -0.5234, 2.5312, -0.4551, -0.6875, -1.5547, -0.2217,
-0.3027, 2.6406, 1.3047, 0.2344, -1.6719, 0.2773, -1.3516, 3.4575,
0.4414, 0.2656, 2.1094, -1.5156, 1.2344, -0.4336, 0.6797, -3.5486,
0.9766, -0.4062, 1.4844, 0.7500, -1.7578, 0.7461, 1.6094, 8.5458,
0.3730, -0.3477, -1.0625, 0.3848, 0.0557]], device=device)
expected = torch.tensor([[0.71, 0.06, 0.0001, 0., 0.7357, 0., -0.0001, -0.654,
0., 0., 0., 0., 0., 0., 0., 0.7344,
0., 0., -0.3125, 0., -0.6328, 0.7344, 0., 1.8554,
0., 0., 0., -0.8047, 0.5312, 0., -0.1953, 0.0664,
-0.0820, 0.0, 0.3203, 0.1133, 0.0938],
[-0.3203, -0.7344, -0.0234, 2.0312, 0.0, -0.1875, -1.0547, 0.,
0.0, 2.1406, 0.8047, 0., -1.1719, 0., -0.8516, 2.9575,
0., 0., 1.6094, -1.0156, 0.7344, 0., 0.1797, -3.0486,
0.4766, 0., 0.9844, 0.2500, -1.2578, 0.2461, 1.1094, 8.0458,
0., 0., -0.5625, 0., 0.]])
softshrink = torch.nn.Softshrink()
out = softshrink(x)
self.assertEqual(out, expected, atol=1e-2, rtol=0)
def test_threshold_inplace_overlap(self, device):
# Inplace threshold is okay, because it is idempotent
x = torch.randn((1, 6), device=device).expand((6, 6))
F.threshold(x, 0.5, 0.5, inplace=True)
F.threshold_(x, 0.5, 0.5)
@onlyNativeDeviceTypes
def test_triplet_margin_with_distance_loss_default_parity(self, device):
# Test for `nn.TripletMarginWithDistanceLoss` and
# `F.triplet_margin_with_distance_loss`. Checks
# for parity against the respective non-distance-agnostic
# implementations of triplet margin loss (``nn.TripletMarginLoss`
# and `F.triplet_margin_loss`) under *default args*.
for extra_args in \
itertools.product((0.5, 1, 1.5), (True, False), ('none', 'mean', 'sum')):
kwargs = {'margin': extra_args[0], 'swap': extra_args[1], 'reduction': extra_args[2]}
anchor = torch.randn(5, 10, device=device, requires_grad=True, dtype=torch.double)
positive = torch.randn(5, 10, device=device, requires_grad=True, dtype=torch.double)
negative = torch.randn(5, 10, device=device, requires_grad=True, dtype=torch.double)
# Test forward, functional
expected = F.triplet_margin_loss(anchor, positive, negative, **kwargs)
actual = F.triplet_margin_with_distance_loss(anchor, positive, negative, **kwargs)
self.assertEqual(actual, expected, rtol=1e-6, atol=1e-6)
# Test forward, module
loss_ref = nn.TripletMarginLoss(**kwargs)
loss_op = nn.TripletMarginWithDistanceLoss(**kwargs)
self.assertEqual(loss_op(anchor, positive, negative),
loss_ref(anchor, positive, negative),
rtol=1e-6, atol=1e-6)
# Test backward
self.assertTrue(gradcheck(lambda a, p, n: F.triplet_margin_with_distance_loss(
a, p, n, **kwargs), (anchor, positive, negative)))
self.assertTrue(gradcheck(lambda a, p, n: loss_op(a, p, n),
(anchor, positive, negative)))
@onlyNativeDeviceTypes
def test_triplet_margin_with_distance_loss(self, device):
# Test for parity between `nn.TripletMarginWithDistanceLoss` and
# `F.triplet_margin_with_distance_loss`.
pairwise_distance = nn.PairwiseDistance()
def cosine_distance(x, y):
return 1.0 - F.cosine_similarity(x, y)
distance_functions = (pairwise_distance, cosine_distance,
lambda x, y: 1.0 - F.cosine_similarity(x, y))
reductions = ('mean', 'none', 'sum')
margins = (1.0, 1.5, 0.5)
swaps = (True, False)
for distance_fn, reduction, margin, swap \
in itertools.product(distance_functions, reductions, margins, swaps):
anchor = torch.randn(5, 10, device=device, requires_grad=True, dtype=torch.double)
positive = torch.randn(5, 10, device=device, requires_grad=True, dtype=torch.double)
negative = torch.randn(5, 10, device=device, requires_grad=True, dtype=torch.double)
# Test backward
self.assertTrue(gradcheck(lambda a, p, n: F.triplet_margin_with_distance_loss(
a, p, n, distance_function=distance_fn, reduction=reduction, margin=margin, swap=swap),
(anchor, positive, negative)))
loss_op = nn.TripletMarginWithDistanceLoss(distance_function=distance_fn,
reduction=reduction, margin=margin, swap=swap)
self.assertTrue(gradcheck(lambda a, p, n: loss_op(
a, p, n), (anchor, positive, negative)))
traced_loss_op = torch.jit.trace(loss_op, (anchor, positive, negative))
self.assertTrue(gradcheck(lambda a, p, n: traced_loss_op(
a, p, n), (anchor, positive, negative)))
# Test forward parity
functional = F.triplet_margin_with_distance_loss(anchor, positive, negative,
distance_function=distance_fn,
reduction=reduction, margin=margin, swap=swap)
modular = loss_op(anchor, positive, negative)
traced = traced_loss_op(anchor, positive, negative)
self.assertEqual(functional, modular, atol=1e-6, rtol=1e-6)
self.assertEqual(traced, modular, atol=1e-6, rtol=1e-6)
def test_to_complex(self, device):
m = nn.Linear(3, 5).to(device)
self.assertIs(m, m.to(device))
m.to(torch.cfloat)
self.assertIs(m.weight.dtype, torch.cfloat)
m.to(torch.cdouble)
self.assertIs(m.weight.dtype, torch.cdouble)
m.to(torch.float)
self.assertIs(m.weight.dtype, torch.float)
with warnings.catch_warnings(record=True) as w:
# Trigger warning
m.to(torch.cfloat)
# Check warning occurs
self.assertEqual(len(w), 1)
self.assertTrue("Complex modules are a new feature" in str(w[-1].message))
@skipMeta
@dtypes(torch.float32, torch.float64)
def test_module_to_empty(self, device, dtype):
class MyModule(nn.Module):
def __init__(self, in_features, out_features, device=None, dtype=None):
super().__init__()
factory_kwargs = {"device": device, "dtype": dtype}
self.weight = nn.Parameter(torch.randn(in_features, out_features, **factory_kwargs))
def forward(self, x):
return x @ self.weight
# Test meta module instantiation.
input = torch.randn(5, 10, device=device, dtype=dtype)
m = MyModule(10, 1, device='meta', dtype=dtype)
m(input)
# Test materializing meta module on a real device.
m.to_empty(device=device)
m(input)
with torch.no_grad():
torch.nn.init.kaiming_uniform_(m.weight)
m(input)
# Test creating meta module from materialized module.
m.to_empty(device='meta')
m(input)
def test_module_to_empty_non_recursive(self, device):
class Layer(nn.Module):
def __init__(self, in_features, out_features):
super().__init__()
self.weight = nn.Parameter(torch.randn(in_features, out_features))
self.register_buffer('buf', torch.randn(out_features))
def forward(self, x):
return x @ self.weight + self.buf
class MyModule(nn.Module):
def __init__(self, in_features, out_features):
super().__init__()
self.weight = nn.Parameter(torch.randn(in_features, out_features))
self.register_buffer('buf1', torch.randn(out_features))
self.layer = Layer(out_features, out_features)
def forward(self, x):
return self.layer(x @ self.weight + self.buf1)
with torch.device('meta'):
m = MyModule(3, 5)
m.to_empty(device=device, recurse=False)
# params/buffers of parent should have been materialized on device
self.assertTrue(not m.weight.is_meta)
self.assertTrue(not m.buf1.is_meta)
# parameters/buffers of children submodules should still be on meta
for p in (*m.layer.parameters(), *m.layer.buffers()):
self.assertTrue(p.is_meta)
@skipMeta
def test_skip_init(self, device):
torch.manual_seed(1)
m_initialized = torch.nn.Linear(5, 1)
m_initialized.to(device)
torch.manual_seed(1)
m_uninitialized = torch.nn.utils.skip_init(torch.nn.Linear, 5, 1, device=device)
self.assertEqual(m_initialized.weight.device, m_uninitialized.weight.device)
self.assertFalse(torch.allclose(m_initialized.weight, m_uninitialized.weight))
@dtypes(torch.float)
@dtypesIfCUDA(torch.double, torch.float, torch.half)
def test_transformerencoderlayer(self, device, dtype):
# this is a deterministic test for TransformerEncoderLayer
d_model = 4
nhead = 2
dim_feedforward = 16
dropout = 0.0
bsz = 2
atol = 1e-5
rtol = 1e-7
if "cuda" in device:
atol = 1e-3
rtol = 1e-2
def _test(training, batch_first, atol, rtol):
def perm_fn(x):
return x.transpose(1, 0) if batch_first else x
model = nn.TransformerEncoderLayer(d_model, nhead, dim_feedforward, dropout,
batch_first=batch_first, device=device, dtype=dtype)
if not training:
assert dropout == 0
model = model.eval()
# set constant weights of the model
for idx, p in enumerate(model.parameters()):
x = p.data
sz = x.view(-1).size(0)
shape = x.shape
x = torch.cos(torch.arange(0, sz).float().view(shape))
p.data.copy_(x)
# deterministic input
encoder_input = torch.tensor([[[20., 30., 40., 50.]]], device=device, dtype=dtype)
result = model(encoder_input)
ref_output = torch.tensor([[[2.258703, 0.127985, -0.697881, 0.170862]]], device=device, dtype=dtype)
self.assertEqual(result.shape, ref_output.shape)
torch.testing.assert_close(result, ref_output, atol=atol, rtol=rtol)
# 0 values are NOT masked. This shouldn't mask anything.
mask = torch.tensor([[0]], device=device) == 1
# TODO: enable fast path for calls with a mask!
result = model(encoder_input, src_key_padding_mask=mask)
self.assertEqual(result.shape, ref_output.shape)
torch.testing.assert_close(result, ref_output, atol=atol, rtol=rtol)
# 1 values are masked. Since there is only 1 input embedding this
# will result in nan.
mask = torch.tensor([[1]], device=device) == 1
result = model(encoder_input, src_key_padding_mask=mask)
result = result.cpu().detach().numpy()
self.assertTrue(np.isnan(result).all())
# deterministic input
encoder_input = perm_fn(torch.tensor([[[1., 2., 3., 4.]],
[[5., 6., 7., 8.]]], device=device, dtype=dtype))
result = model(encoder_input)
ref_output = perm_fn(torch.tensor([[[2.272644, 0.119035, -0.691669, 0.153486]],
[[2.272644, 0.119035, -0.691669, 0.153486]]], device=device, dtype=dtype))
self.assertEqual(result.shape, ref_output.shape)
torch.testing.assert_close(result, ref_output, atol=atol, rtol=rtol)
# all 0 which is no masking
mask = torch.tensor([[0, 0]], device=device) == 1
result = model(encoder_input, src_key_padding_mask=mask)
self.assertEqual(result.shape, ref_output.shape)
torch.testing.assert_close(result, ref_output, atol=atol, rtol=rtol)
mask = torch.tensor([[1, 0]], device=device) == 1
result = model(encoder_input, src_key_padding_mask=mask)
ref_output = perm_fn(torch.tensor([[[2.301516, 0.092249, -0.679101, 0.103088]],
[[2.301516, 0.092249, -0.679101, 0.103088]]], device=device, dtype=dtype))
self.assertEqual(result.shape, ref_output.shape)
torch.testing.assert_close(result, ref_output, atol=atol, rtol=rtol)
# deterministic input
encoder_input = perm_fn(torch.tensor([[[0.7462, 0.6653, 0.5679, 0.4891],
[0.5387, 0.1655, 0.3565, 0.0471]],
[[0.8335, 0.2799, 0.5031, 0.2947],
[0.1402, 0.0318, 0.7636, 0.1346]],
[[0.6333, 0.9344, 0.1376, 0.9938],
[0.8924, 0.2872, 0.6692, 0.2944]],
[[0.9897, 0.6915, 0.3154, 0.1733],
[0.8645, 0.3513, 0.3064, 0.0767]],
[[0.8117, 0.2366, 0.4838, 0.7881],
[0.3718, 0.4945, 0.9511, 0.0864]]], device=device, dtype=dtype))
result = model(encoder_input)
ref_output = perm_fn(torch.tensor([[[2.428589, 0.020835, -0.602055, -0.085249],
[2.427987, 0.021213, -0.602496, -0.084103]],
[[2.424689, 0.019155, -0.604793, -0.085672],
[2.413863, 0.022211, -0.612486, -0.072490]],
[[2.433774, 0.021598, -0.598343, -0.087548],
[2.425104, 0.019748, -0.604515, -0.084839]],
[[2.436185, 0.022682, -0.596625, -0.087261],
[2.433556, 0.021891, -0.598509, -0.086832]],
[[2.416246, 0.017512, -0.610712, -0.082961],
[2.422901, 0.024187, -0.606178, -0.074929]]], device=device, dtype=dtype))
self.assertEqual(result.shape, ref_output.shape)
torch.testing.assert_close(result, ref_output, atol=atol, rtol=rtol)
# all 0
mask = torch.zeros([2, 5], device=device) == 1
result = model(encoder_input, src_key_padding_mask=mask)
self.assertEqual(result.shape, ref_output.shape)
torch.testing.assert_close(result, ref_output, atol=atol, rtol=rtol)
mask[0, 1] = 1
mask[1, 3] = 1
mask[1, 4] = 1
result = model(encoder_input, src_key_padding_mask=mask)
ref_output = perm_fn(torch.tensor([[[2.429026, 0.020793, -0.601741, -0.085642],
[2.428811, 0.021445, -0.601912, -0.084252]],
[[2.425009, 0.019155, -0.604566, -0.085899],
[2.415408, 0.02249 , -0.611415, -0.073]],
[[2.434199, 0.021682, -0.598039, -0.087699],
[2.42598, 0.019941, -0.603896, -0.085091]],
[[2.436457, 0.022736, -0.59643 , -0.08736],
[2.434021, 0.022093, -0.598179, -0.08679]],
[[2.416531, 0.017498, -0.610513, -0.083181],
[2.4242, 0.024653, -0.605266, -0.074959]]], device=device, dtype=dtype))
self.assertEqual(result.shape, ref_output.shape)
torch.testing.assert_close(result, ref_output, atol=atol, rtol=rtol)
# NestedTensor is only supported for the fast path
# currently, which won't be used if training.
if (batch_first and not training and
('cuda' in str(device) or 'cpu' in str(device)) and not TEST_WITH_CROSSREF):
encoder_input[0][-1] = torch.zeros_like(encoder_input[0][1])
mask = torch.zeros(encoder_input.shape[:-1], device=device, dtype=torch.bool)
mask[0][-1] = True
nt = torch.nested.nested_tensor([encoder_input[0][:-1], encoder_input[1]], device=device)
result = model(nt)
ref_output = torch.tensor(
[
[
[2.4268184, 0.02042419, -0.603311, -0.08476824],
[2.423306, 0.01889652, -0.6057701, -0.08519465],
[2.431538, 0.02078694, -0.5999354, -0.08746159],
[2.4348664, 0.02212971, -0.5975677, -0.08733892],
[2.423133, 0.02097577, -0.60594773, -0.08113337],
],
[
[2.4279876, 0.02121329, -0.60249615, -0.08410317],
[2.4138637, 0.02221113, -0.6124869, -0.07249016],
[2.4251041, 0.01974815, -0.6045152, -0.08483928],
[2.4335563, 0.0218913, -0.59850943, -0.08683228],
[2.4229012, 0.02418739, -0.6061784, -0.07492948],
],
],
device=device, dtype=dtype
)
result = result.to_padded_tensor(0)
ref_output[0][-1] = torch.zeros_like(
ref_output[0][-1], device=device, dtype=dtype
)
result[0][-1] = torch.zeros_like(
result[0][-1], device=device, dtype=dtype
)
self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
if 'cuda' in device:
if dtype == torch.float:
atol = 2e-4
rtol = 4e-3
else:
atol = 7e-4
rtol = 2e-2
torch.testing.assert_close(result, ref_output, atol=atol, rtol=rtol)
else:
torch.testing.assert_close(result, ref_output)
for batch_first in (True, False):
for training in (True, False):
if training:
cm = contextlib.nullcontext()
else:
# Fast path requires inference mode.
cm = torch.no_grad()
with cm:
_test(batch_first=batch_first, training=training, atol=atol, rtol=rtol)
@onlyCPU
@dtypes(torch.double)
def test_transformerencoderlayer_fast_path(self, device, dtype):
"""
Test transformer fast path on CPU with different valid mask types and shapes
"""
d_model = 512
nhead = 8
batch_size = 32
src_len = 10
model = torch.nn.TransformerEncoderLayer(d_model=d_model, nhead=nhead, batch_first=True,
device=device, dtype=dtype, dropout=0)
model.eval()
# Batched inputs
src = torch.rand(batch_size, src_len, 512, dtype=dtype)
# Attention mask of shape (src_len, src_len)
src_mask = torch.zeros(src_len, src_len).to(torch.bool)
with torch.no_grad():
model(src, src_mask=src_mask)
# Padding mask of shape (batch_size, src_len)
src_key_padding_mask = torch.zeros(batch_size, src_len).to(torch.bool)
with torch.no_grad():
model(src, src_key_padding_mask=src_key_padding_mask)
# Provide both masks
with torch.no_grad():
model(src, src_mask=src_mask, src_key_padding_mask=src_key_padding_mask)
@dtypes(torch.float)
@dtypesIfCUDA(torch.half, torch.float)
def test_transformerencoderlayer_gelu(self, device, dtype):
# this is a deterministic test for TransformerEncoderLayer with gelu activation
d_model = 4
nhead = 2
dim_feedforward = 16
dropout = 0.0
bsz = 2
atol = 0
rtol = 1e-5
if "cuda" in device:
atol = 1e-3
rtol = 1e-2
def _test(activation, batch_first, training):
def perm_fn(x):
return x.transpose(1, 0) if batch_first else x
model = nn.TransformerEncoderLayer(d_model, nhead, dim_feedforward, dropout,
activation, batch_first=batch_first, device=device, dtype=dtype)
if not training:
assert dropout == 0
model = model.eval()
# set constant weights of the model
for idx, p in enumerate(model.parameters()):
x = p.data
sz = x.view(-1).size(0)
shape = x.shape
x = torch.cos(torch.arange(0, sz).float().view(shape))
p.data.copy_(x)
# deterministic input
encoder_input = torch.tensor([[[20., 30., 40., 50.]]], device=device, dtype=dtype)
result = model(encoder_input)
ref_output = torch.tensor([[[2.249815, 0.131006, -0.702199, 0.177868]]], device=device, dtype=dtype)
torch.testing.assert_close(result, ref_output, rtol=rtol, atol=atol)
# deterministic input
encoder_input = perm_fn(torch.tensor([[[1., 2., 3., 4.]],
[[5., 6., 7., 8.]]], device=device, dtype=dtype))
result = model(encoder_input)
ref_output = perm_fn(torch.tensor([[[2.264103, 0.121417, -0.696012, 0.159724]],
[[2.264103, 0.121417, -0.696012, 0.159724]]], device=device, dtype=dtype))
torch.testing.assert_close(result, ref_output, rtol=rtol, atol=atol)
# deterministic input
encoder_input = perm_fn(torch.tensor([[[0.7462, 0.6653, 0.5679, 0.4891],
[0.5387, 0.1655, 0.3565, 0.0471]],
[[0.8335, 0.2799, 0.5031, 0.2947],
[0.1402, 0.0318, 0.7636, 0.1346]],
[[0.6333, 0.9344, 0.1376, 0.9938],
[0.8924, 0.2872, 0.6692, 0.2944]],
[[0.9897, 0.6915, 0.3154, 0.1733],
[0.8645, 0.3513, 0.3064, 0.0767]],
[[0.8117, 0.2366, 0.4838, 0.7881],
[0.3718, 0.4945, 0.9511, 0.0864]]], device=device, dtype=dtype))
result = model(encoder_input)
ref_output = perm_fn(torch.tensor([[[2.42163188, 0.03227153, -0.60714219, -0.05908082],
[2.42151276, 0.03302179, -0.60722523, -0.05762651]],
[[2.41926761, 0.02974034, -0.60879519, -0.0621269],
[2.41626395, 0.03539356, -0.61087842, -0.04978623]],
[[2.42382808, 0.03218872, -0.6055963, -0.06073591],
[2.41983477, 0.03085259, -0.60840145, -0.06046414]],
[[2.42500749, 0.03328855, -0.60476388, -0.0595334],
[2.4237977, 0.03290575, -0.60561789, -0.05940082]],
[[2.41383916, 0.02686345, -0.61256377, -0.06380707],
[2.42000277, 0.03800944, -0.60824798, -0.04754947]]], device=device, dtype=dtype))
torch.testing.assert_close(result, ref_output, rtol=rtol, atol=atol)
for activation, batch_first, training in product(('gelu', F.gelu, nn.GELU()), (True, False), (True, False)):
# Fast path requires inference mode.
if training:
cm = contextlib.nullcontext()
else:
cm = torch.no_grad()
with cm:
_test(activation=activation, batch_first=batch_first, training=training)
@skipIfTorchDynamo("TorchDynamo fails with unknown reason")
@parametrize_test('foreach', (False, True))
def test_clip_grad_value(self, foreach, device):
if torch.device(device).type == 'xla' and foreach:
raise SkipTest('foreach not supported on XLA')
l = nn.Linear(10, 10).to(device)
clip_value = 2.5
grad_w, grad_b = torch.arange(-50., 50, device=device).view(10, 10).div_(5), torch.ones(10, device=device).mul_(2)
for grad_list in [[grad_w, grad_b], [grad_w, None]]:
for p, g in zip(l.parameters(), grad_list):
p._grad = g.clone().view_as(p.data) if g is not None else g
clip_grad_value_(l.parameters(), clip_value, foreach=foreach)
for p in filter(lambda p: p.grad is not None, l.parameters()):
self.assertLessEqual(p.grad.data.max(), clip_value)
self.assertGreaterEqual(p.grad.data.min(), -clip_value)
# Should accept a single Tensor as input
p1, p2 = torch.randn(10, 10, device=device), torch.randn(10, 10, device=device)
g = torch.arange(-50., 50, device=device).view(10, 10).div_(5)
p1._grad = g.clone()
p2._grad = g.clone()
clip_grad_value_(p1, clip_value, foreach=foreach)
clip_grad_value_([p2], clip_value, foreach=foreach)
self.assertEqual(p1.grad, p2.grad)
@skipIfTorchDynamo("TorchDynamo fails with unknown reason")
@parametrize_test('foreach', (False, True))
@parametrize_test('norm_type', (0.5, 1.5, 2, 4, 'inf'))
def test_clip_grad_norm(self, norm_type, foreach, device):
if torch.device(device).type == 'xla' and foreach:
raise SkipTest('foreach not supported on XLA')
l = nn.Linear(10, 10).to(device)
max_norm = 2
def compute_norm(norm_type):
norm_type = float(norm_type)
if norm_type != inf:
total_norm = 0
for p in l.parameters():
total_norm += p.grad.data.abs().pow(norm_type).sum()
return pow(total_norm, 1. / norm_type)
else:
return max(p.grad.data.abs().max() for p in l.parameters())
def compare_scaling(grads):
p_scale = [p.grad.data.div(g).view(-1) for p, g in zip(l.parameters(), grads)]
scale = torch.cat(p_scale)
self.assertEqual(scale.std(), 0)
return scale[0]
grads = torch.arange(1., 101, device=device).view(10, 10), torch.ones(10, device=device).div(1000)
for p, g in zip(l.parameters(), grads):
p._grad = g.clone().view_as(p.data)
norm_before = compute_norm(norm_type)
norm = clip_grad_norm_(l.parameters(), max_norm, norm_type=norm_type, foreach=foreach)
norm_after = compute_norm(norm_type)
self.assertEqual(norm, norm_before)
self.assertEqual(norm_after, max_norm)
self.assertLessEqual(norm_after, norm_before)
compare_scaling(grads)
# Small gradients should be left unchanged
grads = torch.rand(10, 10, device=device).div(10000), torch.ones(10, device=device).div(500)
for p, g in zip(l.parameters(), grads):
p.grad.data.copy_(g)
norm_before = compute_norm(norm_type)
norm = clip_grad_norm_(l.parameters(), max_norm, norm_type=norm_type, foreach=foreach)
norm_after = compute_norm(norm_type)
self.assertEqual(norm, norm_before)
self.assertEqual(norm_before, norm_after)
self.assertLessEqual(norm_after, max_norm)
scale = compare_scaling(grads)
self.assertEqual(scale, 1)
# Should accept a single Tensor as input
p1, p2 = torch.randn(10, 10, device=device), torch.randn(10, 10, device=device)
g = torch.arange(1., 101, device=device).view(10, 10)
p1._grad = g.clone()
p2._grad = g.clone()
clip_grad_norm_(p1, max_norm, norm_type=norm_type, foreach=foreach)
clip_grad_norm_([p2], max_norm, norm_type=norm_type, foreach=foreach)
self.assertEqual(p1.grad, p2.grad)
@onlyCUDA
@largeTensorTest("20GB", "cuda")
def test_softmax_backward_64bit_indexing(self, device):
for numel in (2147483650, 2147483650 + 1):
x = torch.empty([1, 1, numel], device=device, dtype=torch.float16)
x.fill_(1.0 / numel)
out = torch._softmax_backward_data(x, x, 2, x.dtype)
self.assertEqual(out[0, 0, 0], 1 / numel)
@skipMeta
def test_channel_shuffle(self, device):
# 3D tensor
x = torch.tensor(
[[[1, 2],
[5, 6],
[9, 10],
[13, 14],
]], device=device
)
y_ref = torch.tensor(
[[[1, 2],
[9, 10],
[5, 6],
[13, 14],
]], device=device
)
# ChannelsFirst
with warnings.catch_warnings(record=True) as w:
y = F.channel_shuffle(x, 2).to(device)
self.assertEqual(len(w), 0)
self.assertEqual(y, y_ref)
# ChannelsLast not supported for 3dim
# 4D tensor
x = torch.tensor(
[[[[1, 2],
[3, 4]],
[[5, 6],
[7, 8]],
[[9, 10],
[11, 12]],
[[13, 14],
[15, 16]],
]], device=device
)
y_ref = torch.tensor(
[[[[1, 2],
[3, 4]],
[[9, 10],
[11, 12]],
[[5, 6],
[7, 8]],
[[13, 14],
[15, 16]],
]], device=device
)
# ChannelsFirst NCHW
with warnings.catch_warnings(record=True) as w:
y = F.channel_shuffle(x, 2).to(device)
self.assertEqual(len(w), 0)
self.assertEqual(y, y_ref)
# ChannelsLast NHWC
with warnings.catch_warnings(record=True) as w:
y = F.channel_shuffle(x.contiguous(memory_format=torch.channels_last), 2).to(device)
self.assertEqual(len(w), 0)
y = y.contiguous(memory_format=torch.contiguous_format)
self.assertEqual(y, y_ref)
# 5D tensor
x = torch.tensor(
[[[[[1, 2],
[3, 4]]],
[[[5, 6],
[7, 8]]],
[[[9, 10],
[11, 12]]],
[[[13, 14],
[15, 16]]],
]], device=device
)
y_ref = torch.tensor(
[[[[[1, 2],
[3, 4]]],
[[[9, 10],
[11, 12]]],
[[[5, 6],
[7, 8]]],
[[[13, 14],
[15, 16]]],
]], device=device
)
# ChannelsFirst NCHW
with warnings.catch_warnings(record=True) as w:
y = F.channel_shuffle(x, 2).to(device)
self.assertEqual(len(w), 0)
self.assertEqual(y, y_ref)
# ChannelsLast NHWC
with warnings.catch_warnings(record=True) as w:
y = F.channel_shuffle(x.contiguous(memory_format=torch.channels_last_3d), 2).to(device)
self.assertEqual(len(w), 0)
y = y.contiguous(memory_format=torch.contiguous_format)
self.assertEqual(y, y_ref)
class TestFunctionalPickle(TestCase):
# issue gh-38137
def test_pickle_softsign(self):
# Make sure it does not throw an exception
s = pickle.dumps(F.softsign)
class TestFusionUtils(TestCase):
def test_fuse_conv_bn_requires_grad(self):
conv = torch.nn.Conv2d(3, 3, 3)
bn = torch.nn.BatchNorm2d(3)
cases = itertools.product([True, False], [True, False])
for w_rg, b_rg in cases:
conv.weight.requires_grad = w_rg
conv.bias.requires_grad = b_rg
weight, bias = \
fuse_conv_bn_weights(conv.weight, conv.bias,
bn.running_mean, bn.running_var, bn.eps, bn.weight, bn.bias)
self.assertEqual(weight.requires_grad, w_rg)
self.assertEqual(bias.requires_grad, b_rg)
def test_fuse_linear_bn_requires_grad(self):
linear = torch.nn.Linear(3, 3)
bn = torch.nn.BatchNorm1d(3)
cases = itertools.product([True, False], [True, False])
for w_rg, b_rg in cases:
linear.weight.requires_grad = w_rg
linear.bias.requires_grad = b_rg
weight, bias = \
fuse_linear_bn_weights(linear.weight, linear.bias,
bn.running_mean, bn.running_var, bn.eps, bn.weight, bn.bias)
self.assertEqual(weight.requires_grad, w_rg)
self.assertEqual(bias.requires_grad, b_rg)
instantiate_device_type_tests(TestNNDeviceType, globals())
instantiate_parametrized_tests(TestNN)
if __name__ == '__main__':
run_tests()
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