# Owner(s): ["module: nn"] import contextlib import math import random import unittest import io import itertools import warnings import pickle import re from copy import deepcopy from itertools import product from functools import partial from collections import OrderedDict 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 Buffer, 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, gcIfJetson, set_default_dtype from torch.testing._internal.common_cuda import TEST_CUDA, TEST_MULTIGPU, TEST_CUDNN, PLATFORM_SUPPORTS_FLASH_ATTENTION 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 dtypesIfMPS, instantiate_device_type_tests, dtypes, \ dtypesIfCUDA, precisionOverride, skipCUDAIfCudnnVersionLessThan, onlyCUDA, onlyCPU, \ skipCUDAIfRocm, skipCUDAIf, skipCUDAIfNotRocm, \ onlyNativeDeviceTypes, deviceCountAtLeast, largeTensorTest, expectedFailureMeta, expectedFailureMPS, \ 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 from torch.testing._internal.common_mkldnn import bf32_on_and_off 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 = () 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 = () 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) # weights_only=False as this is legacy code that saves the model m = torch.load(path, weights_only=False) 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) -> None: 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) -> None: 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) -> None: 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) -> None: super().__init__() self.buffer1 = Buffer(torch.empty(3, 5)) self.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_buffer_bad_module_subclass(self): class MyBadModule(nn.Linear): def __init__(self) -> None: super().__init__(2, 2) self.bar = Buffer(torch.rand(2, 2)) def register_buffer(self, name, value): # persistent is explicitly missing! super().register_buffer(name, value, True) foo = MyBadModule() self.assertIsNotNone(foo.bar) def test_call_supports_python_dict_output(self): class Net(nn.Module): def __init__(self) -> None: 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) -> None: 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._test_buffer = 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) -> None: 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) -> None: 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)) with self.assertRaises(KeyError): m.attribute_name = Buffer(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) m.buffer_name = Buffer(buffer1) self.assertEqual(m.buffer_name, Buffer(buffer1)) m.buffer_name = Buffer(buffer2) self.assertEqual(m.buffer_name, Buffer(buffer2)) m.buffer_name = Buffer(buffer3) self.assertEqual(m.buffer_name, Buffer(buffer3)) def test_get_buffer(self): m = nn.Module() buffer1 = torch.randn(2, 3) buffer2 = torch.randn(4, 5) m.foo = Buffer(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.foo = Buffer(foo) self.subsub = SubSub(bar) class SubSub(nn.Module): def __init__(self, bar): super().__init__() self.bar = Buffer(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.buf = nn.Buffer(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.buf = nn.Buffer(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.buf = nn.Buffer(torch.rand(5), persistent=False) m.buf = nn.Buffer(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.buf = nn.Buffer(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.buf = nn.Buffer(torch.rand(5), persistent=False) self.assertTrue(len(list(m.buffers())) == 1) self.assertTrue(len(m.state_dict()) == 0) # 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) def test_buffer_not_persistent_load(self): m = nn.Module() m.buf = nn.Buffer(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.attribute_name = Buffer(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)) @skipIfTorchDynamo() 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_set_submodule(self): net = nn.Module() net.t = nn.Module() l = nn.Linear(1, 2) target = "t.l" net.set_submodule(target, l) self.assertEqual(net.get_submodule(target), l) l2 = nn.Linear(2, 1) net.set_submodule(target, l2) self.assertEqual(net.get_submodule(target), l2) self.assertRaises(ValueError, net.set_submodule, "", l) self.assertRaises(AttributeError, net.set_submodule, "a.l", 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_RNN_nonlinearity_passed_as_arg(self): rnn = torch.nn.RNN(2, 3, 1, 'relu') self.assertEqual(rnn.nonlinearity, 'relu') 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_swap_module_params_poisons_acc_grad(self): try: torch.__future__.set_swap_module_params_on_conversion(True) # (1) backward cannot be run after _apply # forward will init AccumulateGrad nodes, which bumps use_count of parameters' at::Tensors # additionally, if any Tensors are saved for backward, their use_count will be bumped m = torch.nn.Linear(2, 3) inp = torch.randn(2, 2) out = m(inp) m.half() self.assertTrue(all(p.dtype == torch.float16 for p in m.parameters())) with self.assertRaisesRegex(RuntimeError, "Trying to execute AccumulateGrad node that was poisoned by swap_tensors"): out.sum().backward() # (2) _apply can be run after backward() # After running backward, all the references generated by "save for backward" will be cleared # So the use_count will be 2 (1 from Tensor itself, and 1 from AccumulateGrad node), swap_tensors # should allow this. inp2 = torch.randn(2, 2, dtype=torch.half) out2 = m(inp2) out2.sum().backward() m.float() self.assertTrue(all(p.dtype == torch.float32 for p in m.parameters())) out3 = m(inp) finally: torch.__future__.set_swap_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.indices = Buffer(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={}), "Should not warn kwarg destination w/o _metadata") 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_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 = Buffer(torch.randn(10)) l.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) -> None: 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): for target_lengths in [[30, 25, 20], [-1, -1, -1]]: for input_lengths in [[50, 50, 50], [-1, -1, -1]]: 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): for target_lengths in [[30, 25, 20], [-1, -1, -1]]: for input_lengths in [[50, 50, 50], [-1, -1, -1]]: 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) def test_CTCLoss_zero_lengths(self): devices = ['cpu'] devices += ['cuda'] if TEST_CUDA else [] N = 3 S = 2 C = 200 T = 1 target = torch.randint(low=1, high=C, size=(N, S), dtype=torch.int) input_lengths = torch.full(size=(N,), fill_value=0, dtype=torch.int) target_lengths = torch.full(size=(N,), fill_value=0, dtype=torch.int) for device in devices: inp = torch.randn(T, N, C, dtype=torch.float, device=device).log_softmax(2).requires_grad_() res = torch.nn.functional.ctc_loss(inp, target, input_lengths, target_lengths, reduction='none') self.assertTrue((res == 0).all().item()) res.sum().backward() self.assertTrue((inp.grad == 0).all().item()) target_lengths = torch.full(size=(N,), fill_value=1, dtype=torch.int) for device in devices: inp = torch.randn(T, N, C, dtype=torch.float, device=device).log_softmax(2).requires_grad_() res = torch.nn.functional.ctc_loss(inp, target, input_lengths, target_lengths, reduction='none') self.assertTrue((res == torch.inf).all().item()) res.sum().backward() self.assertTrue((inp.grad == 0).all().item()) @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) @skipIfRocm(msg='Large numerical errors') 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) def test_cudnn_forward_exception(self): rnns = [ (nn.LSTM(10, 20, batch_first=True), (torch.zeros(1, 2, 19), torch.zeros(1, 2, 19))), (nn.LSTM(10, 20, batch_first=True, proj_size=10), (torch.zeros(1, 2, 19), torch.zeros(1, 2, 19))), (nn.GRU(10, 20, batch_first=True), torch.zeros(1, 2, 19)), (nn.RNN(10, 20, batch_first=True), torch.zeros(1, 2, 19)), ] x_wrong = torch.randn(2, 3, 3) x_right = torch.randn(2, 3, 10) for rnn, hidden in rnns: self.assertRaisesRegex(RuntimeError, "Expected hidden.*size.*got", rnn, x_right, hidden) self.assertRaisesRegex(RuntimeError, re.escape("input.size(-1) must be equal to input_size"), rnn, x_wrong) @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, "needs cudnn") 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, "needs cudnn") @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) @unittest.skipIf(not TEST_CUDNN, "needs cudnn") @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, "needs cudnn") 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) # weights_only=False as this is legacy code that saves the model rnn2 = torch.load(buf, weights_only=False) 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, "needs cudnn") @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 for 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, precision=None): 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, atol=precision, rtol=precision) 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)]: for dtype in [torch.float, torch.bfloat16, torch.float16]: for mixed_dtype in [False, True]: if dtype == torch.float: mixed_dtype = False helper(self, nn.BatchNorm2d, shape, dtype, mixed_dtype, torch.channels_last) precisons = {torch.float: 1e-4, torch.bfloat16: 1e-4, torch.float16: None} for shape in [(4, 8, 2, 10, 10), (4, 1, 2, 9, 9), (4, 9, 1, 1, 1)]: for dtype in [torch.float, torch.bfloat16, torch.float16]: for mixed_dtype in [False, True]: if dtype == torch.float: mixed_dtype = False helper(self, nn.BatchNorm3d, shape, dtype, mixed_dtype, torch.channels_last_3d, precisons[dtype]) @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): def helper(self, dtype): 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=dtype).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) helper(self, torch.bfloat16) helper(self, torch.float16) @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_batchnorm_load_state_dict(self): bn = torch.nn.BatchNorm2d(3) self.assertEqual(bn.state_dict()["num_batches_tracked"], torch.tensor(0)) bn.num_batches_tracked = torch.tensor(10) self.assertEqual(bn.state_dict()["num_batches_tracked"], torch.tensor(10)) empty_dict = OrderedDict() bn.load_state_dict(empty_dict, strict=False) self.assertEqual(bn.state_dict()["num_batches_tracked"], torch.tensor(10)) # test that when `num_batches_tracked` is not in loaded state_dict, # meta num_batches_tracked is still replaced with singleton 0 tensor with torch.device('meta'): meta_bn = torch.nn.BatchNorm2d(3) self.assertTrue(meta_bn.num_batches_tracked.device == torch.device('meta')) meta_bn.load_state_dict(empty_dict, assign=True, strict=False) self.assertEqual(meta_bn.state_dict()["num_batches_tracked"], torch.tensor(0)) def test_batch_norm_update_stats(self): input = torch.rand(0, 1) running_mean = torch.rand(1) running_var = torch.rand(1) with self.assertRaisesRegex(RuntimeError, re.escape("input tensor must have at least one element, but got input_sizes = [0, 1]")): torch.batch_norm_update_stats(input=input, momentum=0.0, running_mean=running_mean, running_var=running_var) 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_cosine_embedding_loss_error_on_diff_shapes(self): for device in device_(): input1 = torch.empty((0, 0), dtype=torch.double, device=device) input2 = torch.empty((0,), dtype=torch.double, device=device) target = torch.empty((0,), dtype=torch.int, device=device) with self.assertRaisesRegex(RuntimeError, ".*expects 2D.*"): torch.nn.functional.cosine_embedding_loss(input1, input2, target) def test_cosine_embedding_loss_error_on_nonexpandable_shapes(self): for device in device_(): input1 = torch.empty((1, 5), dtype=torch.double, device=device) input2 = torch.empty((1, 6), dtype=torch.double, device=device) target = torch.ones((1,), dtype=torch.int, device=device) with self.assertRaisesRegex(RuntimeError, ".*must match the size.*"): torch.nn.functional.cosine_embedding_loss(input1, input2, target) 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: /max(eps, ||x|| * ||y||) # current: # if f(x,y) is the cosine similarity, then # df/dx = y/(||x|| * ||y||) - (x * * ||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.) # Check broadcasting #109333 a = torch.ones(2, 3, dtype=torch.float) b = torch.ones(1, 1, dtype=torch.float) out = F.cosine_similarity(a, b) self.assertEqual(out, torch.ones(2, dtype=torch.float)) a = torch.ones(2, 3, dtype=torch.float) b = torch.ones(1, dtype=torch.float) out = F.cosine_similarity(a, b) self.assertEqual(out, torch.ones(2, dtype=torch.float)) 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) @parametrize_test('device', ['cpu'] + (['cuda'] if TEST_CUDA else [])) @parametrize_test('nd', [2, 3]) def test_affine_grid_backward_cl_cf_consistency(self, device, nd): # Test based on reported issue: https://github.com/pytorch/pytorch/issues/124154 theta = torch.rand([6, nd, nd + 1], requires_grad=True, device=device) size = [6, 3, 4, 5] if nd == 2 else [6, 3, 4, 5, 5] grid = torch.nn.functional.affine_grid(theta, size, align_corners=False) grad_tensor = torch.rand(grid.shape, device=device) memory_format_cl = torch.channels_last if nd == 2 else torch.channels_last_3d grad_tensor_cl = grad_tensor.contiguous(memory_format=memory_format_cl) assert theta.grad is None grid.backward(grad_tensor_cl) theta_grad_cl = theta.grad.clone().contiguous() theta.grad.zero_() grid.backward(grad_tensor) theta_grad_cf = theta.grad self.assertEqual(theta_grad_cf, theta_grad_cl) @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) @skipIfTorchDynamo("TorchDynamo fails here for unknown reasons") def test_native_channel_shuffle_return_alias_of_self(self): groups = 3 input_tensor = torch.rand([0, 9, 4, 4]) output = torch.native_channel_shuffle(input_tensor, groups) 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)) def test_linear_raise_on_scalar_input(self): # This used to cause an int underflow issue when reshaping the input # see https://github.com/pytorch/pytorch/issues/119161 m = nn.Linear(1, 1) inp = torch.ones(1).squeeze() with self.assertRaisesRegex(RuntimeError, ".*both arguments.*1D.*"): 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: raise AssertionError 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_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_layer_norm_eps(self): # test for https://github.com/pytorch/pytorch/issues/108072 x = torch.Tensor([[[2.0, 2.0], [14.0, 14.0]], [[2.0, 2.0], [14.0, 14.0]]]) ln = torch.nn.LayerNorm(2, eps=1e-6, elementwise_affine=False) self.assertEqual(ln.forward(x), torch.zeros_like(x)) 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) def test_pickle_module_no_weights_only_warning(self): with warnings.catch_warnings(record=True) as w: pickle.loads(pickle.dumps(torch.nn.Linear(10, 10))) self.assertEqual(len(w), 0) 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 or dtype == torch.half) 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, dtype): 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=dtype).random_(1, 10) m = nn.LayerNorm([11, 3], elementwise_affine=elementwise_affine).to(device, dtype) # 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/half m_bf16 = deepcopy(m) x_bf16 = input.clone().detach().requires_grad_() out_bf16 = m_bf16(x_bf16) out_bf16.sum().backward() # bf16/half 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.to(dtype=dtype), out_bf16) self.assertEqual(out_fp32.to(dtype=dtype), out_mix) self.assertEqual(x_fp32.grad.to(dtype=dtype), x_bf16.grad, atol=1e-1, rtol=1e-1) self.assertEqual(x_fp32.grad.to(dtype=dtype), 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, dtype): channels = size[1] input = torch.randn(size).cpu().to(dtype=dtype) 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().to(dtype=dtype) 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).cpu().to(dtype=dtype) 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/half 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/half input grad and bfloat16/half parameters out.backward(grad_out_bf1, retain_graph=True) # Need higher tolerances atol=1e-4 and rtol=1e-4 on macos self.assertEqual(m_f.weight.grad, m_f2.weight.grad, atol=1e-4, rtol=1e-4) 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/half has lower precision compared with mixed bf16/half and fp32. # Use Amp to keep module parameters in acc dtype, i.e. float, for better numerical stability atol = None rtol = None if dtype == torch.bfloat16: atol = 1e-2 rtol = 1.2e-1 else: assert dtype == torch.half atol = 5e-3 rtol = 1.5e-2 self.assertEqual(m_bf.weight.grad, m_f.weight.grad.to(dtype=dtype), atol=atol, rtol=rtol) self.assertEqual(m_bf.bias.grad, m_f.bias.grad.to(dtype=dtype), atol=atol, rtol=rtol) self.assertEqual(input_bf1.grad, input_bf2.grad, atol=atol, rtol=rtol) cl_formats = {4: torch.channels_last, 5: torch.channels_last_3d} for dtype in [torch.bfloat16, torch.half]: for shape, g in [((1, 8, 4, 3), 2), ((1, 8, 3, 4), 4), ((4, 40, 40, 40), 2), ((4, 8, 40, 40), 4), ((1, 8, 40, 40), 4), ((1, 8, 40, 40), 2), ((1, 8, 50, 50), 2), ((1, 8, 50, 50), 4), ((1, 40, 50, 50), 2), ((1, 9, 3, 4, 5), 3), ((1, 60, 10, 10, 10), 3), ((1, 9, 10, 50, 50), 3), ((1, 60, 10, 50, 50), 3), ((1, 8, 65, 55), 2), ((1, 3, 65, 55), 1), ((1, 3, 20, 20), 1)]: for is_cl in [False, True]: format = cl_formats[len(shape)] if is_cl else torch.contiguous_format helper(self, shape, g, format, dtype) 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") @expectedFailureMPS # Unsupported Border padding mode https://github.com/pytorch/pytorch/issues/125098 @tf32_on_and_off() @bf32_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") @expectedFailureMPS # Unsupported Border padding mode https://github.com/pytorch/pytorch/issues/125098 @tf32_on_and_off(0.001) @bf32_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") @expectedFailureMPS # Unsupported Border padding mode https://github.com/pytorch/pytorch/issues/125098 @tf32_on_and_off(0.005) @bf32_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)) @onlyCUDA @largeTensorTest("60GB", "cpu") @largeTensorTest("16GB", "cuda") def test_avg_pool_large_tensor(self, device): # test for https://github.com/pytorch/pytorch/issues/113833 a = torch.randn(128, 256, 256, 256, dtype=torch.half, device=device, requires_grad=True) a_cpu = a.detach().cpu().float() m = torch.nn.AvgPool2d(2) o = m(a) a_cpu.requires_grad = True o.sum().backward() o_cpu = m(a_cpu) o_cpu.sum().backward() # workaround for memory usage overhead of assertEqual self.assertTrue(torch.allclose(a.grad.cpu(), a_cpu.grad.half())) @onlyCUDA @largeTensorTest("48GB", "cpu") @largeTensorTest("48GB", "cuda") def test_avg_pool_large_tensor2(self, device): # test for https://github.com/pytorch/pytorch/issues/129785 out_size = [2048, 64, 104, 79] size = [2048, 64, 209, 159] inp = torch.randn(size, device=device, requires_grad=True, dtype=torch.float) inp_cpu = inp.detach().cpu() m = torch.nn.AvgPool2d([2, 2], [2, 2], [0, 0], False, True, None) o = m(inp) inp_cpu.requires_grad = True o.sum().backward() o_cpu = m(inp_cpu) o_cpu.sum().backward() self.assertEqual(o.shape, out_size) self.assertEqual(o_cpu.shape, out_size) # reduce memory usage self.assertEqual(inp.grad.sum(), inp_cpu.grad.sum()) @unittest.skipIf((not TEST_NUMPY) or (not TEST_SCIPY) or (scipy.__version__ < '1.0.0'), "Scipy v1.0 and/or numpy not found") @expectedFailureMPS # Unsupported Border padding mode https://github.com/pytorch/pytorch/issues/125098 @tf32_on_and_off(0.005) @bf32_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") @expectedFailureMPS # aten::grid_sampler_3d not implemented https://github.com/pytorch/pytorch/issues/77764 @tf32_on_and_off(0.005) @bf32_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) @dtypesIfMPS(torch.float, torch.half, torch.complex64) @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) with self.assertRaisesRegex(RuntimeError, re.escape("2D kernel_size expected")): torch._C._nn.thnn_conv2d(torch.rand([1, 1, 1, 1]), kernel_size=[], padding=[1, 1], stride=[1, 1], weight=torch.rand([1, 1])) with self.assertRaisesRegex(RuntimeError, re.escape("2D stride expected")): torch._C._nn.thnn_conv2d(torch.rand([1, 1, 1, 1]), kernel_size=[1, 1], padding=[1, 1], stride=[], weight=torch.rand([1, 1])) with self.assertRaisesRegex(RuntimeError, re.escape("2D padding expected")): torch._C._nn.thnn_conv2d(torch.rand([1, 1, 1, 1]), kernel_size=[1, 1], padding=[], stride=[1, 1], weight=torch.rand([1, 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': for dtype in [torch.half, torch.bfloat16]: self._test_LayerNorm_general(device, dtype=dtype) if self.device_type == 'cuda': self._test_LayerNorm_cuda_half(device) if self.device_type == 'cpu': for dtype in [torch.half, torch.bfloat16]: self._test_LayerNorm_cpu_mixed_dtype(device, dtype=dtype) @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, torch.half) 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/Half is not recommended atol = 5e-4 rtol = 8e-3 self.assertEqual(gn.weight.grad, ref_gn.weight.grad, atol=atol, rtol=rtol) self.assertEqual(gn.bias.grad, ref_gn.bias.grad, atol=atol, rtol=rtol) self.assertEqual(input.grad, ref_input.grad, atol=atol, rtol=rtol) for is_mixed in [True, False]: helper(self, (4, 8, 10, 10), 4, torch.channels_last, is_mixed) helper(self, (2, 30, 9, 9), 3, torch.channels_last, is_mixed) helper(self, (4, 8, 40, 40), 4, torch.channels_last, is_mixed) helper(self, (4, 40, 40, 40), 2, torch.channels_last, is_mixed) helper(self, (2, 30, 50, 50), 3, torch.channels_last, is_mixed) helper(self, (2, 60, 50, 50), 3, torch.channels_last, is_mixed) helper(self, (2, 9, 7, 11, 15), 3, torch.channels_last_3d, is_mixed) helper(self, (2, 9, 7, 200, 15), 3, torch.channels_last_3d, is_mixed) helper(self, (2, 60, 7, 200, 15), 3, torch.channels_last_3d, is_mixed) @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) with self.assertRaisesRegex(RuntimeError, 'padding size is expected to be 2'): torch._C._nn.replication_pad1d(torch.randn([2]), padding=[]) with self.assertRaisesRegex(RuntimeError, 'padding size is expected to be 4'): torch._C._nn.replication_pad2d(torch.randn([2]), padding=[]) with self.assertRaisesRegex(RuntimeError, 'padding size is expected to be 6'): torch._C._nn.replication_pad3d(torch.randn([2]), padding=[]) @expectedFailureMPS # TODO(hvaara): Investigate as possible bug. 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)) @expectedFailureMPS # TODO(hvaara): Investigate as possible bug. 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) @dtypesIfMPS(torch.float) @dtypes(torch.double) def test_BatchNorm_empty(self, device, dtype): mod = torch.nn.BatchNorm2d(3).to(device) inp = torch.randn(0, 3, 2, 2, device=device, dtype=dtype) _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): # cuda throws device assert for invalid data # xla ignores out of bound indices if self.device_type not in ('cuda', 'mps', 'xla'): 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) @expectedFailureMPS # NotImplementedError: aten::rrelu_with_noise https://github.com/pytorch/pytorch/issues/77764 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) @expectedFailureMPS # NotImplementedError: aten::rrelu_with_noise https://github.com/pytorch/pytorch/issues/77764 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. # MPS: NotImplementedError: aten::rrelu_with_noise_ https://github.com/pytorch/pytorch/issues/77764 # MPS: NotImplementedError: aten::hardshrink.out https://github.com/pytorch/pytorch/issues/77764 @expectedFailureMPS 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) @expectedFailureMPS # TypeError: float64 the MPS framework doesn't support float64 @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) @skipIfMps # Partially passes https://github.com/pytorch/pytorch/issues/134430 @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) @expectedFailureMPS # TypeError: the MPS framework doesn't support float64 @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) @skipIfMps # Partially passes https://github.com/pytorch/pytorch/issues/134430 @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) @expectedFailureMPS # TypeError: the MPS framework doesn't support float64 @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) @expectedFailureMPS # NotImplementedError: aten::_upsample_nearest_exact3d.out https://github.com/pytorch/pytorch/issues/77764 @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) @expectedFailureMPS # NotImplementedError: aten::_upsample_bilinear2d_aa.out https://github.com/pytorch/pytorch/issues/77764 @parametrize_test("memory_format", [torch.contiguous_format, torch.channels_last]) def test_upsamplingBilinear2d_aa_correctness(self, device, memory_format): # NOTE: We expand the batch dim such that `b*c` is above the maximum # size of CUDA grid z-dimension (2**16) shape = [23000, 3, 8, 8] t_in = torch.arange(3 * 8 * 8, dtype=torch.float, device=device).reshape(1, *shape[1:]) t_in = t_in.expand(shape) 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.expand([*shape[:2], 2, 2]), t_out) # Partially passes. NotImplementedError: aten::upsample_bicubic2d.out https://github.com/pytorch/pytorch/issues/77764 @skipIfMps @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 range is set to [30, 220] for bicubic 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. input_range = (30, 220) if mode == "bicubic" else (0, 256) input_ui8 = torch.randint(*input_range, 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)) if mode == "bilinear": torch.testing.assert_close(output_f32, output_ui8.float(), rtol=0, atol=1) else: diff = (output_f32 - output_ui8.float()).abs() self.assertLess(diff.max(), 15) threshold = 2 percent = 3 self.assertLess((diff > threshold).float().mean(), percent / 100) threshold = 5 percent = 1 self.assertLess((diff > threshold).float().mean(), percent / 100) self.assertLess(diff.mean(), 0.4) @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) @expectedFailureMPS # NotImplementedError: aten::upsample_bicubic2d.out https://github.com/pytorch/pytorch/issues/77764 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) @expectedFailureMPS # NotImplementedError: aten::_upsample_bicubic2d_aa.out https://github.com/pytorch/pytorch/issues/77764 @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) @expectedFailureMPS # NotImplementedError: aten::upsample_trilinear3d.out https://github.com/pytorch/pytorch/issues/77764 @parametrize_test("align_corners", [True, False]) @parametrize_test("memory_format", [torch.contiguous_format, torch.channels_last_3d]) def test_upsamplingTrilinear3d(self, device, align_corners, memory_format): kwargs = dict(mode='trilinear', align_corners=align_corners) # 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, 4, 4, 4, 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)) grad_out = torch.randn_like(out_t).contiguous(memory_format=memory_format) in_t.grad = None out_t.backward(grad_out) grad_in = in_t.grad self.assertTrue(grad_in.is_contiguous(memory_format=memory_format)) if memory_format == torch.channels_last_3d: # check if grad inputs CF and CL match in_t.grad = None out_t.backward(grad_out.contiguous()) self.assertEqual(in_t.grad, grad_in) input = torch.randn(1, 2, 4, 4, 4, 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)) @onlyCUDA @dtypes(torch.half) @largeTensorTest('40GB') def test_replicatepad_64bit_indexing(self, device, dtype): conv = torch.nn.Conv1d(128, 128, 3, 1, 1, padding_mode="replicate", device=device, dtype=dtype) x = torch.randn(size=(256 * 448 * 2, 128, 96), dtype=dtype, device=device) y = conv(x) torch.mean(y).backward() @onlyCUDA @dtypes(torch.half) @largeTensorTest('40GB') def test_upsamplingnearest2d_backward_64bit_indexing(self, device, dtype): x = torch.randn(size=(36, 128, 512, 512), device=device, dtype=dtype).requires_grad_() y = F.interpolate(x, scale_factor=2, mode="nearest") y.backward(torch.randn_like(y)) 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 ) @dtypes(torch.bfloat16, torch.half) @precisionOverride({torch.bfloat16: 2e-2, torch.half: 3e-3}) def test_masked_softmax_lowp(self, dtype): 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_lowp = torch.randn((B, num_heads, L, L), dtype=dtype).requires_grad_() input_ref = input_lowp.float().detach().requires_grad_() mask = torch.randint(0, 2, (B, L)) mask = mask.reshape(B, 1, 1, L).expand(B, num_heads, L, L).bool() for mask_type in [1, 2]: res_ref = torch._masked_softmax(input_ref, mask, dim, mask_type) res = torch._masked_softmax(input_lowp, mask, dim, mask_type) self.assertEqual(res_ref.to(dtype), res) grad_lowp = torch.randn_like(res_ref).to(dtype=dtype) grad_ref = grad_lowp.float() res_ref.backward(grad_ref) res.backward(grad_lowp) self.assertEqual(input_ref.grad.to(dtype), input_lowp.grad) 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) @onlyCPU @dtypes(torch.bfloat16, torch.half) def test_log_softmax_cpu(self, device, dtype): for dim in [0, 1]: inputf = torch.rand(200, 200, device=device, 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) @onlyCPU @dtypes(torch.bfloat16, torch.half) def test_softmax_cpu(self, device, dtype): for dim in [0, 1]: inputf = torch.rand(200, 200, device=device, 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) @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) @onlyCUDA def test_grid_sample_bfloat16_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.bfloat16) grid = torch.rand(shape_out, device='cuda', dtype=torch.bfloat16) * 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.bfloat16(), 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) @dtypesIfCUDA(torch.half, torch.float, torch.double) @dtypesIfMPS(torch.float) @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) @dtypesIfMPS(torch.half, torch.float) @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 @expectedFailureMPS # TypeError: the MPS framework doesn't support float64 @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 @expectedFailureMPS # TypeError: the MPS framework doesn't support float64 @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) @expectedFailureMPS # NotImplementedError: aten::_ctc_loss https://github.com/pytorch/pytorch/issues/77764 @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)) @expectedFailureMPS # TypeError: the MPS framework doesn't support float64 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) @expectedFailureMPS # TypeError: the MPS framework doesn't support float64 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)) @expectedFailureMPS # NotImplementedError aten::_ctc_loss https://github.com/pytorch/pytorch/issues/77764 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""") @expectedFailureMPS # NotImplementedError aten::_ctc_loss https://github.com/pytorch/pytorch/issues/77764 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) @onlyCUDA @skipCUDAIfRocm(msg="skipped Cudnn test on ROCm") @skipCUDAIfCudnnVersionLessThan(8000) def test_ctc_loss_cudnn_tensor(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] input_lengths = torch.linspace(start=15, end=input_length, steps=batch_size, dtype=torch.long, device='cuda') target_lengths = torch.tensor(batch_size * [target_length], dtype=torch.long, device='cuda') 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('cuda', torch.int32), input_lengths.to('cuda', torch.int32), target_lengths.to('cuda', torch.int32), 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) @expectedFailureMPS # RuntimeError: LSTM with projections is not currently supported with MPS. @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("120GB", "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)) # Ref: https://github.com/pytorch/pytorch/issue/108345 @onlyCUDA @largeTensorTest("20GB", "cpu") @largeTensorTest("20GB", "cuda") @parametrize_test("reduction", ("none", "mean", "sum")) def test_cross_entropy_64bit(self, device, reduction): labels = torch.zeros(190, 50, dtype=torch.long, device=device) logits = torch.ones(190, 229000, 50, dtype=torch.float, device=device) loss = torch.nn.functional.cross_entropy(logits, labels) loss_cpu = torch.nn.functional.cross_entropy(logits.cpu(), labels.cpu()) print(logits.numel(), labels.numel(), loss.numel()) self.assertTrue(torch.allclose(loss_cpu, loss.cpu(), rtol=1e-4, atol=1e-4)) 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) @expectedFailureMPS # RuntimeError: [srcBuf length] > 0 INTERNAL ASSERT FAILED https://github.com/pytorch/pytorch/issues/134431 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) @expectedFailureMPS # RuntimeError: [srcBuf length] > 0 INTERNAL ASSERT FAILED https://github.com/pytorch/pytorch/issues/134431 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) @expectedFailureMPS # AssertionError: Expected nan but got 0.0. 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]) @expectedFailureMPS # AssertionError: Expected nan but got 0.0. 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) @onlyCUDA @skipIfRocm @dtypes(torch.float16, torch.float32) def test_cross_entropy_loss_2d_out_of_bounds_class_index(self, device, dtype): # Test for issue #117532 # Run in a different process to prevent the device-side assert from affecting other tests stderr = TestCase.runWithPytorchAPIUsageStderr(f"""\ #!/usr/bin/env python3 import torch import torch.nn.functional as F from torch.testing._internal.common_utils import (run_tests, TestCase) class TestThatContainsCUDAAssert(TestCase): def test_cross_entropy_loss_2d_out_of_bounds_class_index(self): device = '{str(device)}' dtype = {str(dtype).strip("'")} ignore_index = 255 b = 10 n_classes = 3 w = 768 h = 1024 pred = torch.randn(b, n_classes, w, h, dtype=dtype, device=device) labels = torch.zeros(b, w, h, dtype=torch.int64, device=device) labels[5, 200, 200] = ignore_index # Set invalid class index labels[5, 200, 200] = 254 x = F.cross_entropy( pred, labels, reduction="none", ignore_index=ignore_index ) torch.cuda.synchronize() if __name__ == '__main__': run_tests() """) self.assertIn('CUDA error: device-side assert triggered', stderr) 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) @expectedFailureMPS # TypeError: the MPS framework doesn't support float64 @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] wgt = 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, wgt)): 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/issues/85005 @onlyCUDA @largeTensorTest("45GB", "cpu") @largeTensorTest("70GB", "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) @expectedFailureMPS # TypeError: the MPS framework doesn't support float64 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 @expectedFailureMPS # TypeError: the MPS framework doesn't support float64 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) -> None: 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): dtype = torch.bfloat16 if device != 'mps:0' else torch.float16 x = torch.randn((1, 6), dtype=dtype, 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) @expectedFailureMPS # TypeError: the MPS framework doesn't support float64 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? @expectedFailureMPS # NotImplementedError: aten::rrelu_with_noise_ https://github.com/pytorch/pytorch/issues/77764 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) dtype = torch.bfloat16 if device != 'mps:0' else torch.float16 a_bf16 = torch.tensor([-2., 0., 2.], device=device, dtype=dtype, 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=dtype) 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) @dtypesIfMPS(torch.cfloat, torch.float) @dtypes(torch.cfloat, torch.cdouble, torch.float) def test_to_complex(self, device, dtype): m = nn.Linear(3, 5).to(device) self.assertIs(m, m.to(device)) m.to(dtype) self.assertIs(m.weight.dtype, dtype) 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 @dtypesIfMPS(torch.float32) @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 empty meta module error with torch.nn.Module.to(). with self.assertRaisesRegex( NotImplementedError, re.escape( "Cannot copy out of meta tensor; no data! Please use torch.nn.Module.to_empty() " "instead of torch.nn.Module.to() when moving module from meta to a different " "device." ), ): m.to(device) # 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)) @skipIfRocm(msg='See https://github.com/pytorch/pytorch/issues/135150') @skipIfMps # TODO(hvaara): Investigate as possible bug. macOS 13 passes, while 14 and 15 fails. @dtypes(torch.float) @dtypesIfCUDA(torch.double, torch.float, torch.half) def test_transformerencoderlayer(self, device, dtype): if TEST_WITH_ROCM and PLATFORM_SUPPORTS_FLASH_ATTENTION and dtype == torch.half: self.skipTest("Skip on ROCM due to Flash Attention tolerances") # 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) mask = torch.tensor([[1]], device=device) == 1 result = model(encoder_input, src_key_padding_mask=mask) fast_path_device = result.is_cuda or result.is_cpu result = result.cpu().detach().numpy() # Non Fast Paths if training or not batch_first or TEST_WITH_CROSSREF or not fast_path_device: # We changed the semenatic, on the non fast path so that fully masked out rows return # 0 from attention thus NaNs should no longer be present and the output should be nonzero # due to skip connections self.assertTrue(not np.isnan(result).any()) else: # Fast Paths 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): if TEST_WITH_ROCM and PLATFORM_SUPPORTS_FLASH_ATTENTION and dtype == torch.half: self.skipTest("Skip on ROCM due to Flash Attention tolerances") # 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) @skipIfMps # RuntimeError: foreach=True was passed, but can't use the foreach API on mps tensors @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) @skipIfMps # TypeError: the MPS framework doesn't support float64 @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) # reference issue: https://github.com/pytorch/pytorch/issues/111484 @onlyCUDA @largeTensorTest("42GB", "cuda") def test_softmax_forward_64bit_indexing(self, device): batch_size = 70 seq_len = 2048 vocab_size = 50000 shift_labels = torch.zeros(batch_size, seq_len - 1, dtype=torch.long, device=device) logits = torch.ones(batch_size, seq_len - 1, vocab_size, dtype=torch.float16, device=device) loss_fct = torch.nn.CrossEntropyLoss(reduction="none") nll = loss_fct(logits.permute(0, 2, 1), shift_labels).float() rtol, atol = torch.testing._comparison.get_tolerances(torch.float16, rtol=None, atol=None) self.assertEqual(nll, torch.ones_like(nll) * torch.log(torch.tensor(vocab_size)), rtol=rtol, atol=atol) @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) # reference issue: https://github.com/pytorch/pytorch/issues/68248 @onlyCUDA def test_adaptiveavg_pool1d_shmem(self, device): x = torch.randn(1, 256, 1, 5000, device=device).to(memory_format=torch.channels_last) x_cpu = x.cpu() x_cpu.requires_grad_() x.requires_grad_() y = torch.nn.functional.adaptive_avg_pool2d(x, (1, 256)) y_cpu = torch.nn.functional.adaptive_avg_pool2d(x_cpu, (1, 256)) grad = torch.randn_like(y) grad_cpu = grad.cpu() y.backward(grad) y_cpu.backward(grad_cpu) self.assertEqual(x.grad, x_cpu.grad) @skipMeta @expectedFailureMPS # NotImplementedError: aten::channel_shuffle https://github.com/pytorch/pytorch/issues/77764 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) class TestUtils(TestCase): def test_consume_prefix_in_state_dict_if_present(self): class Block(nn.Module): def __init__(self) -> None: super().__init__() self.conv1 = nn.Conv2d(3, 3, 3, bias=True) self.conv2 = nn.Conv2d(3, 3, 3, bias=False) class Net(nn.Module): def __init__(self) -> None: super().__init__() self.linear1 = nn.Linear(5, 5) self.linear2 = nn.Linear(5, 5) net.bn = nn.BatchNorm2d(2) self.block = Block() # 0. Case non-DDP model empty state_dict net = nn.Module() state_dict = net.state_dict() nn.modules.utils.consume_prefix_in_state_dict_if_present(state_dict, 'module.') # check they are the same preserving order self.assertEqual(list(state_dict.keys()), list(net.state_dict().keys())) self.assertEqual(list(state_dict._metadata.keys()), list(net.state_dict()._metadata.keys())) # 1. Case non-DDP model test example state_dict net = Net() state_dict = net.state_dict() nn.modules.utils.consume_prefix_in_state_dict_if_present(state_dict, 'module.') # Check they are the same preserving order self.assertEqual(list(state_dict.keys()), list(net.state_dict().keys())) self.assertEqual(list(state_dict._metadata.keys()), list(net.state_dict()._metadata.keys())) # 2. Case DDP model test example state_dict state_dict = net.state_dict() metadata = state_dict._metadata ddp_state_dict = OrderedDict((f'module.{k}', v) for k, v in state_dict.items()) ddp_state_dict._metadata = OrderedDict({'': metadata['']}) ddp_state_dict._metadata.update(('module' if k == '' else f'module.{k}', v) for k, v in metadata.items()) nn.modules.utils.consume_prefix_in_state_dict_if_present(ddp_state_dict, 'module.') # Check they are the same preserving order self.assertEqual(list(state_dict.keys()), list(ddp_state_dict.keys())) self.assertEqual(list(state_dict._metadata.keys()), list(ddp_state_dict._metadata.keys())) instantiate_device_type_tests(TestNNDeviceType, globals(), allow_mps=True) instantiate_parametrized_tests(TestNN) if __name__ == '__main__': TestCase._default_dtype_check_enabled = True run_tests()