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android / platform / external / pytorch / ae44627661 / . / test / test_autograd.py
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import contextlib
import gc
import sys
import math
import torch
import unittest
import warnings
from copy import deepcopy
from collections import OrderedDict
from itertools import product
from operator import mul, itemgetter
from functools import reduce, wraps
from torch._six import inf, nan
from torch.autograd.gradcheck import gradgradcheck, gradcheck
from torch.autograd.function import once_differentiable
from torch.autograd.profiler import profile
from common_utils import (TEST_MKL, TestCase, run_tests, skipIfNoLapack,
suppress_warnings, skipIfRocm,
prod_single_zero, random_square_matrix_of_rank,
random_symmetric_matrix, random_symmetric_psd_matrix,
random_symmetric_pd_matrix, make_nonzero_det,
random_fullrank_matrix_distinct_singular_value)
from torch.autograd import Variable, Function, detect_anomaly
from torch.autograd.function import InplaceFunction
from torch.testing import make_non_contiguous, randn_like
from common_methods_invocations import (method_tests,
create_input, unpack_variables,
EXCLUDE_FUNCTIONAL, EXCLUDE_GRADCHECK,
EXCLUDE_GRADGRADCHECK,
EXCLUDE_GRADGRADCHECK_BY_TEST_NAME,
exclude_tensor_method,
mask_not_all_zeros,
L, S)
if sys.version_info[0] == 2:
import cPickle as pickle
else:
import pickle
PRECISION = 1e-4
@contextlib.contextmanager
def backward_engine(engine):
_prev_engine = Variable._execution_engine
Variable._execution_engine = engine()
try:
yield
finally:
Variable._execution_engine = _prev_engine
def graph_desc(fn):
if fn is None:
return 'None'
result = type(fn).__name__ + '('
next_functions = fn.next_functions
for next_fn, _ in next_functions:
result += graph_desc(next_fn)
result += ', '
if next_functions:
result = result[:-2]
return result + ')'
class TestAutograd(TestCase):
def _function_test(self, cls):
x = torch.randn(5, 5, requires_grad=True)
y = torch.randn(5, 5, requires_grad=True)
result = cls.apply(x, 2, y)
go = torch.ones((), requires_grad=True)
result.sum().backward(go, create_graph=True)
self.assertEqual(x.grad.data, y.data + torch.ones(5, 5))
self.assertEqual(y.grad.data, x.data + torch.ones(5, 5) * 2)
self.assertIsNotNone(x.grad.grad_fn)
self.assertIsNotNone(y.grad.grad_fn)
return x, y
def test_function(self):
class MyFunction(Function):
@staticmethod
def forward(ctx, tensor1, pyscalar, tensor2):
ctx.pyscalar = pyscalar
ctx.save_for_backward(tensor1, tensor2)
return tensor1 + pyscalar * tensor2 + tensor1 * tensor2
@staticmethod
def backward(ctx, grad_output):
var1, var2 = ctx.saved_tensors
# NOTE: self is the test case here
self.assertIsInstance(var1, torch.Tensor)
self.assertIsInstance(var2, torch.Tensor)
self.assertIsInstance(grad_output, torch.Tensor)
return (grad_output + grad_output * var2, None,
grad_output * ctx.pyscalar + grad_output * var1)
x, y = self._function_test(MyFunction)
x_grad_desc = graph_desc(x.grad.grad_fn)
y_grad_desc = graph_desc(y.grad.grad_fn)
self.assertExpected(x_grad_desc, "x_grad_desc")
self.assertExpected(y_grad_desc, "y_grad_desc")
def test_once_differentiable(self):
class MyFunction(Function):
@staticmethod
def forward(ctx, tensor1, pyscalar, tensor2):
ctx.pyscalar = pyscalar
ctx.save_for_backward(tensor1, tensor2)
return tensor1 + pyscalar * tensor2 + tensor1 * tensor2
@staticmethod
@once_differentiable
def backward(ctx, grad_output):
self.assertFalse(torch.is_grad_enabled())
t1, t2 = ctx.saved_tensors
return (grad_output + grad_output * t2, None,
grad_output * ctx.pyscalar + grad_output * t1)
x, y = self._function_test(MyFunction)
self.assertEqual(graph_desc(x.grad.grad_fn),
'CloneBackward(Error(AccumulateGrad(), None, AccumulateGrad()))')
self.assertEqual(graph_desc(y.grad.grad_fn),
'CloneBackward(Error(AccumulateGrad(), None, AccumulateGrad()))')
def test_function_returns_input(self):
class MyFunction(Function):
@staticmethod
def forward(ctx, x):
return x
@staticmethod
def backward(ctx, grad):
return grad * 2
for shape in [(1,), ()]:
v = torch.ones(shape, requires_grad=True)
MyFunction.apply(v).backward()
self.assertEqual(v.grad, torch.full(shape, 2))
v.grad.data.zero_()
MyFunction.apply(v.clone()).backward()
self.assertEqual(v.grad, torch.full(shape, 2))
def test_legacy_function_none_grad(self):
class MyFunction(Function):
def forward(self, x):
return torch.zeros(2, 2, 2)
def backward(self, grad_output):
return None
shape = (2, 3)
v = torch.ones(shape, requires_grad=True)
y = v[0, 0].expand(3, 5).t().sum()
MyFunction()(y).sum().backward()
self.assertEqual(v.grad.data, torch.zeros(shape))
def test_invalid_gradients(self):
class MyFunction(Function):
@staticmethod
def forward(ctx, x):
return x * 2
@staticmethod
def backward(ctx, grad_output):
return torch.randn(10, dtype=torch.float)
with self.assertRaisesRegex(RuntimeError, 'expected shape'):
input = torch.randn(5, 5, dtype=torch.float, requires_grad=True)
MyFunction.apply(input).sum().backward()
with self.assertRaisesRegex(RuntimeError, 'expected type'):
input = torch.randn(10, dtype=torch.double, requires_grad=True)
MyFunction.apply(input).sum().backward()
def test_accumulate_grad(self):
grad_output = torch.ones(5, 5)
def compute_grad(create_graph):
x = torch.randn(5, 5, requires_grad=True)
y = x + 2
y.backward(grad_output, retain_graph=True)
x_grad = x.grad
x_grad_clone = x.grad.clone()
y.backward(grad_output, create_graph=create_graph)
return x_grad, x_grad_clone
# Accumulate in-place when create_graph is False
x_grad, x_grad_clone = compute_grad(create_graph=False)
self.assertEqual(x_grad, x_grad_clone * 2)
# Accumulate out-of-place when create_graph is False
x_grad, x_grad_clone = compute_grad(create_graph=True)
self.assertEqual(x_grad, x_grad_clone)
def test_hessian_vector(self):
x = torch.randn(2, 2, requires_grad=True)
y = torch.randn(2, 2, requires_grad=True)
z = x ** 2 + y * x + y ** 2
z.backward(torch.ones(2, 2), create_graph=True)
x_grad = 2 * x.data + y.data
y_grad = x.data + 2 * y.data
self.assertEqual(x.grad.data, x_grad)
self.assertEqual(y.grad.data, y_grad)
grad_sum = 2 * x.grad + y.grad
grad_sum.backward(torch.ones(2, 2))
x_hv = torch.ones(2, 2) * 5
y_hv = torch.ones(2, 2) * 4
self.assertEqual(x.grad.data, x_grad + x_hv)
self.assertEqual(y.grad.data, y_grad + y_hv)
def test_grad(self):
x = torch.randn(2, 2, requires_grad=True)
y = torch.randn(2, 2, requires_grad=True)
z = x ** 2 + y * x + y ** 2
z.backward(torch.ones(2, 2), create_graph=True)
x_grad = 2 * x.data + y.data
y_grad = x.data + 2 * y.data
self.assertEqual(x.grad.data, x_grad)
self.assertEqual(y.grad.data, y_grad)
grad_sum = 2 * x.grad + y.grad
x_hv = torch.autograd.grad(
outputs=[grad_sum], grad_outputs=[torch.ones(2, 2)],
inputs=[x], create_graph=True)
expected_x_hv = torch.ones(2, 2) * 5
expected_y_hv = torch.ones(2, 2) * 4
self.assertEqual(x_hv[0].data, expected_x_hv)
self.assertEqual(x.grad.data, x_grad)
self.assertEqual(y.grad.data, y_grad)
def test_grad_nonleaf(self):
x_init = torch.randn(2, 2, requires_grad=True)
x = x_init
y = torch.randn(2, 2, requires_grad=True)
grad_output = torch.ones(2, 2)
def fn(x):
return x ** 2 + y * x + y ** 2
for i in range(5):
grad_x, = torch.autograd.grad(
fn(x), x, grad_outputs=grad_output, create_graph=True)
grad_x_expected = 2 * x.data + y.data
self.assertIsNone(y.grad)
self.assertIsNone(x.grad)
self.assertEqual(grad_x.data, grad_x_expected)
x = x + 0.05 * grad_x
val_init = fn(x_init).data.sum()
val_final = fn(x).data.sum()
self.assertGreater(val_final, val_init)
x.backward(grad_output)
self.assertIsNotNone(y.grad)
self.assertIsNotNone(x_init.grad)
def test_grad_nonleaf_many_outputs(self):
# This checks an edge case for function callbacks
# We want to capture two grads of a function, but can only
# register a single callback.
x = torch.randn(4, 2, requires_grad=True)
a, b = x.chunk(2)
def hook(*grads):
hook_called[0] = True
hook_called = [False]
x.register_hook(hook)
go = torch.randn(2, 2)
grad_a, grad_b = torch.autograd.grad(
(a + 2 * b), [a, b], grad_outputs=go, create_graph=True)
self.assertEqual(grad_a.data, go)
self.assertEqual(grad_b.data, go * 2)
self.assertFalse(hook_called[0])
self.assertIsNone(x.grad)
def test_grad_nonleaf_register_hook(self):
# This checks an edge case for register_hook.
# We want to capture grad of a nonleaf tensor,
# but avoid segfault during backward of other nonleaf tensors
x = torch.randn(5, requires_grad=True)
x_list = x.unbind()
x0 = x_list[0]
hook_results = [None]
def hook(grad):
hook_results[0] = grad
x0.register_hook(hook)
x_list[0].backward()
self.assertEqual(hook_results[0], torch.tensor(1.))
expected_grad = torch.tensor([1., 0, 0, 0, 0])
self.assertEqual(x.grad, expected_grad)
self.assertIsNone(x_list[0].grad)
for i in range(1, 5, 1):
x_list[i].backward()
self.assertEqual(hook_results[0], None)
expected_grad[i] = 1.0
self.assertEqual(x.grad, expected_grad)
self.assertIsNone(x_list[i].grad)
def test_sharded_grad(self):
leaves = [torch.zeros(5, 5, requires_grad=True) for _ in range(10)]
intermediates = [l * i + l * l for i, l in enumerate(leaves)]
loss = sum(v * i for i, v in enumerate(intermediates)).sum()
# define a helper for dividing intermediates into groups
def group(l, group_size):
return (l[i:i + group_size] for i in range(0, len(l), group_size))
# Compute the d loss / d intermediates in chunks of shard_size
shard_size = 2
d_intermediates = [d_i for intermediates_batch in group(intermediates, shard_size)
for d_i in torch.autograd.grad(loss, intermediates_batch)]
# Compute rest of backward pass
torch.autograd.backward(intermediates, d_intermediates)
for i, l in enumerate(leaves):
self.assertEqual(l.grad.data, i * i * (1 + l.data))
def test_backward_badcalls(self):
x = torch.ones(1)
with self.assertRaisesRegex(RuntimeError, 'does not require grad'):
x.backward()
def test_grad_badcalls(self):
x = torch.ones(1)
y = x ** 2
with self.assertRaisesRegex(RuntimeError, 'does not require grad'):
torch.autograd.grad(x, y)
with self.assertRaisesRegex(RuntimeError, 'does not require grad'):
torch.autograd.grad(y, x)
x = torch.ones(1, requires_grad=True)
y = x ** 2
torch.autograd.grad(y, x) # this should succeed now
def test_grad_fn_badcalls(self):
error_regex = 'expected .* arguments, got .* instead'
x = torch.ones(1, requires_grad=True)
y = x ** 2
with self.assertRaisesRegex(TypeError, error_regex):
y.grad_fn(x.detach(), x.detach()) # too many
with self.assertRaisesRegex(TypeError, error_regex):
y.grad_fn() # too few
y.grad_fn(x.detach()) # this should succeed
def test_grad_unreachable(self):
x = torch.ones(1, requires_grad=True)
y = torch.ones(1, requires_grad=True)
# Make sure x and y have grad accumulators allocated
z = x * 2
w = y * 2
grad_x, grad_y = torch.autograd.grad(x * 2, [x, y], allow_unused=True)
self.assertEqual(grad_x, x * 2)
self.assertIsNone(grad_y)
# This is slightly different than the case above, because z doesn't even
# have a grad accumulator allocated.
z = torch.ones(1, requires_grad=True)
grad_x, grad_z = torch.autograd.grad(x * 2, [x, z], allow_unused=True)
self.assertEqual(grad_x, x * 2)
self.assertIsNone(grad_z)
def test_hooks(self):
x = torch.ones(5, 5, requires_grad=True)
y = Variable(torch.ones(5, 5) * 4, requires_grad=True)
counter = [0]
def bw_hook(inc, grad):
self.assertIsInstance(grad, torch.Tensor)
counter[0] += inc
z = x ** 2 + x * 2 + x * y + y
x.register_hook(lambda *args: bw_hook(0, *args))
test = z.register_hook(lambda *args: bw_hook(1, *args))
z.backward(torch.ones(5, 5), retain_graph=True)
self.assertEqual(counter[0], 1)
test2 = z.register_hook(lambda *args: bw_hook(2, *args))
z.backward(torch.ones(5, 5), retain_graph=True)
self.assertEqual(counter[0], 4)
test2.remove()
z.backward(torch.ones(5, 5), retain_graph=True)
self.assertEqual(counter[0], 5)
def bw_hook_modify(grad):
return grad.mul(2)
test.remove()
z.register_hook(bw_hook_modify)
y.grad.data.zero_()
z.backward(torch.ones(5, 5), retain_graph=True)
self.assertEqual(y.grad.data, (x.data + 1) * 2)
y.register_hook(bw_hook_modify)
y.grad.data.zero_()
z.backward(torch.ones(5, 5))
self.assertEqual(y.grad.data, (x.data + 1) * 4)
def test_hooks_cpp(self):
# Tests hooks for autograd function implemented in C++
bn = torch.nn.BatchNorm1d(5, affine=False)
bn.eval()
counter = [0]
def bw_hook(grad):
counter[0] += 1
return grad * 2
x = torch.ones(5, 5, requires_grad=True)
z = bn(x)
z.register_hook(bw_hook)
z.sum().backward()
self.assertEqual(counter[0], 1, 'bw_hook not called')
self.assertEqual(x.grad.data, torch.ones(5, 5) * 2)
def test_hook_none(self):
# WARNING: this is a test for autograd internals.
# You should never have to use such things in your code.
class NoneGradientFunction(Function):
def forward(self, x, y):
assert self.needs_input_grad[0]
assert not self.needs_input_grad[1]
return x, y
def backward(self, grad_x, grad_y):
return grad_x, None
fn = NoneGradientFunction()
was_called = [False]
def hook(grad_input, grad_output):
self.assertIsInstance(grad_input, tuple)
self.assertIsInstance(grad_output, tuple)
self.assertIsNotNone(grad_input[0])
self.assertIsNotNone(grad_input[1])
self.assertIsNotNone(grad_output[0])
self.assertIsNotNone(grad_output[1])
was_called[0] = True
fn.register_hook(hook)
x = torch.randn(5, 5, requires_grad=True)
y = torch.randn(5, 5)
sum(fn(x, y)).sum().backward()
self.assertTrue(was_called[0])
def test_retain_grad(self):
input = torch.rand(1, 3, requires_grad=True)
h1 = input * 3
out = (h1 * h1).sum()
# It should be possible to call retain_grad() multiple times
h1.retain_grad()
h1.retain_grad()
# Gradient should be accumulated
out.backward(retain_graph=True)
self.assertEqual(h1.data * 2, h1.grad.data)
out.backward(retain_graph=True)
self.assertEqual(h1.data * 4, h1.grad.data)
input.grad.data.zero_()
# It should be a no-op for leaves
input.retain_grad()
input.retain_grad()
out.backward()
self.assertEqual(input.data * 18, input.grad.data)
def test_retain_grad_cycle(self):
import gc
import weakref
counter = [0]
refs = [None]
x = torch.ones(5, 5, requires_grad=True)
def run_test():
y = x * 2
y.retain_grad()
def inc(*args):
counter[0] += 1
refs[0] = weakref.ref(y, inc)
return y / 2
z = run_test()
gc.collect()
self.assertIsNone(refs[0]())
self.assertEqual(counter[0], 1)
z.sum().backward()
def test_backward(self):
v_t = torch.randn(5, 5)
x_t = torch.randn(5, 5)
y_t = torch.rand(5, 5) + 0.1
z_t = torch.randn(5, 5)
grad_output = torch.randn(5, 5)
v = Variable(v_t, requires_grad=True)
x = Variable(x_t, requires_grad=True)
y = Variable(y_t, requires_grad=True)
z = Variable(z_t, requires_grad=True)
v.backward(grad_output)
self.assertEqual(v.grad.data, grad_output)
a = x + (y * z) + 4 * z ** 2 * x / y
a.backward(grad_output)
x_grad = 4 * z_t.pow(2) / y_t + 1
y_grad = z_t - 4 * x_t * z_t.pow(2) / y_t.pow(2)
z_grad = 8 * x_t * z_t / y_t + y_t
self.assertEqual(x.grad.data, x_grad * grad_output)
self.assertEqual(y.grad.data, y_grad * grad_output)
self.assertEqual(z.grad.data, z_grad * grad_output)
def test_sparse_backward(self):
class FixedGradientFunction(Function):
def __init__(self, grad):
self.grad = grad
def forward(self, x):
return x
def backward(self, grad_x):
return self.grad
size = torch.Size([6, 3, 2])
i1 = torch.LongTensor([
[0, 3, 4],
[0, 2, 2],
])
v1 = torch.DoubleTensor([[1, 2], [4, 5], [7, 8]])
sparse_grad1 = torch.sparse.DoubleTensor(i1, v1, size)
i2 = torch.LongTensor([
[0, 1, 3, 4],
[0, 1, 2, 2],
])
v2 = torch.DoubleTensor([[1, 2], [4, 3], [4, 5], [7, 8]])
sparse_grad2 = torch.sparse.DoubleTensor(i2, v2, size)
dense_grad = torch.rand(size).double()
sparse_fn1 = FixedGradientFunction(sparse_grad1)
sparse_fn2 = FixedGradientFunction(sparse_grad2)
dense_fn = FixedGradientFunction(dense_grad)
# sparse first
x = torch.randn(size, requires_grad=True)
(sparse_fn1(x) + dense_fn(x) + sparse_fn2(x)).sum().backward()
self.assertEqual(x.grad, dense_grad + sparse_grad1 + sparse_grad2)
# dense first
x = torch.randn(size, requires_grad=True)
(dense_fn(x) + sparse_fn1(x) + sparse_fn2(x)).sum().backward()
self.assertEqual(x.grad, dense_grad + sparse_grad1 + sparse_grad2)
# sparse only
x = torch.randn(size, requires_grad=True)
(sparse_fn1(x) + sparse_fn2(x)).sum().backward()
self.assertEqual(x.grad, sparse_grad1 + sparse_grad2)
@skipIfRocm
def test_sparse_ctor_getter_backward(self):
# See NOTE [ Sparse: autograd and API ] on the expected behavior of this test
def test(size, sparse_dim, nnz, device):
v_size = [nnz] + list(size[sparse_dim:])
i = torch.rand(sparse_dim, nnz)
i.mul_(torch.tensor(size[:sparse_dim]).unsqueeze(1).to(i))
i = i.to(torch.long)
inp = torch.randn(v_size, requires_grad=True)
other = self.genSparseTensor(size, sparse_dim, nnz, is_uncoalesced=True)[0]
other = other.to(device)
def fn(v):
x = torch.sparse_coo_tensor(i, v, size, device=device)
y = (x + other).coalesce()
yv = y.values()
new_v = yv.tanh()
z = torch.sparse_coo_tensor(y.indices(), new_v, y.size())
return z.coalesce().values()
gradcheck(fn, (inp,))
# FIXME: make gradgradcheck work.
# gradgradcheck(fn, (inp,))
# assert that _values is non-differentiable
with self.assertRaisesRegex(RuntimeError, "does not have a grad_fn"):
other.detach().requires_grad_()._values().backward(torch.ones_like(other._values()))
devices = ['cpu']
if torch.cuda.is_available():
devices.append('cuda')
for empty_i, empty_v, empty_nnz in product([True, False], repeat=3):
sparse_size = [] if empty_i else [2, 1]
dense_size = [1, 0, 2] if empty_v else [1, 2]
nnz = 0 if empty_nnz else 5
for device in devices:
test(sparse_size + dense_size, len(sparse_size), nnz, device)
def test_multi_backward(self):
x = torch.randn(5, 5, requires_grad=True)
y = torch.randn(5, 5, requires_grad=True)
q = torch.randn(5, 5, requires_grad=True)
a = torch.randn(5, 5, requires_grad=True)
b = torch.randn(5, 5, requires_grad=True)
q2 = q * 2
z = x + y + q2
c = a * b + q2
grad_z = torch.randn(5, 5)
grad_c = torch.randn(5, 5)
torch.autograd.backward([z, c], [grad_z, grad_c])
self.assertEqual(x.grad.data, grad_z)
self.assertEqual(y.grad.data, grad_z)
self.assertEqual(a.grad.data, grad_c * b.data)
self.assertEqual(b.grad.data, grad_c * a.data)
self.assertEqual(q.grad.data, (grad_c + grad_z) * 2)
def test_multi_backward_no_grad(self):
x = torch.randn(5, 5, requires_grad=True)
y = torch.randn(5, 5, requires_grad=False)
z = x + y
q = y * 2
# NB: we currently raise an exception if any arguments to backwards
# have requires_grad=False and don't have a grad_fn. We may want to
# relax that check to a warning.
def call_backwards():
torch.autograd.backward([z, q], [torch.ones(5, 5), torch.ones(5, 5)])
self.assertRaises(RuntimeError, call_backwards)
def test_dependent_backward(self):
x = torch.randn(10, requires_grad=True)
y = x ** 2
z = y ** 3
go_y = torch.randn(10)
go_z = torch.randn(10)
torch.autograd.backward([y, z], [go_y, go_z])
xd = x.data
self.assertEqual(x.grad.data, 2 * xd * go_y + 6 * xd.pow(5) * go_z)
def test_save_output_nr(self):
x = torch.randn(10, requires_grad=True)
class MultiOutputFn(Function):
@staticmethod
def forward(ctx, x):
return x[:5], x[5:]
@staticmethod
def backward(ctx, *grad):
return torch.cat(grad)
a, b = MultiOutputFn.apply(x)
self.assertEqual(b.output_nr, 1)
class TestFn(Function):
@staticmethod
def forward(ctx, b):
ctx.save_for_backward(b)
return b * 2
@staticmethod
def backward(ctx, grad_b):
b, = ctx.saved_tensors
self.assertEqual(b.output_nr, 1)
TestFn.apply(b).sum().backward()
def test_free_deep_graph(self):
def scope():
depth = 150000
x = torch.randn(1, requires_grad=True)
y = x.clone()
# build a "chain" computation graph
for i in range(depth):
y = y + y * 0.000001
# graph deletion occurs when the above locals go out of scope.
# In this case `del y` will trigger it but it's easier to leave
# it to Python to delete the locals.
# Should not stack overflow
scope()
def test_free_deep_graph_complicated(self):
def scope():
depth = 100000
randchoice = torch.randint(2, [depth, 2])
x = torch.randn(1, requires_grad=True)
y = x.clone()
# Hold the two previous values
prev_values = [None, None]
# Build a "chain with skip connections" graph
for i in range(depth):
prev_tensors = [tensor for tensor in prev_values[:-1]
if tensor is not None]
prev_values.append(y)
prev_values.pop(0)
# Definitely pick one tensor to add
y += y * 0.000001
# Possibly add other tensors
nprev = len(prev_tensors)
if nprev == 2:
y += randchoice[depth].mul(torch.cat(prev_tensors)).sum()
# graph deletion occurs when the above locals go out of scope.
# Should not stack overflow
scope()
def test_free_deep_graph_pyfunction(self):
class MyOp(Function):
@staticmethod
def forward(ctx, tensor1, tensor2):
return tensor1 + tensor2
@staticmethod
def backward(ctx, grad_output):
return grad_output, grad_output
def scope():
depth = 150000
x = torch.randn(1, requires_grad=True)
y = x.clone()
# build deeply nested computation graph
for i in range(depth):
y = MyOp.apply(y, y)
# graph deletion occurs when the above locals go out of scope.
# Should not stack overflow
scope()
def test_no_grad(self):
x = torch.ones(5, 5, requires_grad=True)
y = Variable(torch.ones(5, 5) * 4)
with torch.no_grad():
w = x + y
@torch.no_grad()
def adder(x, y):
return x + y
z = adder(x, y)
self.assertFalse(w.requires_grad)
self.assertRaises(RuntimeError, lambda: w.backward(torch.ones(5, 5)))
self.assertIsNone(w.grad_fn)
self.assertFalse(z.requires_grad)
self.assertRaises(RuntimeError, lambda: z.backward(torch.ones(5, 5)))
self.assertIsNone(z.grad_fn)
# test nested decorator and with-statement on no_grad
with torch.no_grad():
self.assertFalse(torch.is_grad_enabled())
w = adder(x, y)
self.assertFalse(torch.is_grad_enabled())
def test_no_grad_python_function(self):
"""Python Functions should respect grad mode."""
x = torch.ones(5, 5, requires_grad=True)
class MyOp(Function):
@staticmethod
def forward(self, x):
return x + 1
@staticmethod
def backward(self, dy):
return dy
with torch.no_grad():
y = MyOp.apply(x)
self.assertFalse(y.requires_grad)
def test_indexing(self):
x = torch.arange(1., 17).view(4, 4)
y = Variable(x, requires_grad=True)
def compare(x, y, idx, indexed_tensor, indexed_var):
indexed_var_t = indexed_var.data
if not isinstance(indexed_tensor, torch.Tensor):
indexed_var_t = indexed_var_t[0]
self.assertEqual(indexed_tensor, indexed_var_t)
indexed_var.sum().backward()
expected_grad = torch.Tensor(x.size()).fill_(0)
expected_grad[idx] = 1
self.assertEqual(y.grad.data, expected_grad)
def check_index(x, y, idx):
if y.grad is not None:
y.grad.data.zero_()
indexed_tensor = x[idx]
indexed_var = y[idx]
compare(x, y, idx, indexed_tensor, indexed_var)
check_index(x, y, 1)
check_index(x, y, (1, 1))
check_index(x, y, slice(1, None))
check_index(x, y, slice(None, 2))
check_index(x, y, (slice(None, 2), 2))
check_index(x, y, (slice(1, 2), 2))
check_index(x, y, (1, slice(2, None)))
check_index(x, y, (slice(None, None), slice(2, None)))
check_index(x, y, torch.LongTensor([0, 2]))
check_index(x, y, torch.rand(4, 4).bernoulli().byte())
check_index(x, y, (Ellipsis, slice(2, None)))
check_index(x, y, ([0], [0]))
check_index(x, y, ([1, 2, 3], [0]))
check_index(x, y, ([1, 2], [2, 1]))
check_index(x, y, ([[1, 2], [3, 0]], [[0, 1], [2, 3]]))
check_index(x, y, ([slice(None), [2, 3]]))
check_index(x, y, ([[2, 3], slice(None)]))
# advanced indexing, with less dim, or ellipsis
check_index(x, y, ([0]))
check_index(x, y, ([0], ))
x = torch.arange(1., 49).view(4, 3, 4)
y = Variable(x, requires_grad=True)
check_index(x, y, (slice(None), [0], [0]))
check_index(x, y, ([0], [0], slice(None)))
check_index(x, y, (slice(None), [0, 1, 2], [0]))
check_index(x, y, ([0, 1, 2], [0], slice(None)))
check_index(x, y, (slice(None), [1, 2], [2, 1]))
check_index(x, y, ([1, 2], [2, 1], slice(None)))
check_index(x, y, (slice(None), [[1, 2], [2, 0]], [[0, 1], [2, 3]]))
check_index(x, y, ([[1, 2], [3, 0]], [[0, 1], [2, 2]], slice(None)))
check_index(x, y, (slice(None), slice(None), [2, 1]))
check_index(x, y, (slice(None), [2, 1], slice(None)))
check_index(x, y, ([2, 1], slice(None), slice(None)))
# advanced indexing, with less dim, or ellipsis
check_index(x, y, ([0], ))
check_index(x, y, ([0], slice(None)))
check_index(x, y, ([0], Ellipsis))
check_index(x, y, ([1, 2], [0, 1]))
check_index(x, y, ([1, 2], [0, 1], Ellipsis))
check_index(x, y, (Ellipsis, [1, 2], [0, 1]))
# advanced indexing, with a tensor wrapped in a variable
z = torch.LongTensor([0, 1])
zv = Variable(z, requires_grad=False)
seq = [z, Ellipsis]
seqv = [zv, Ellipsis]
if y.grad is not None:
y.grad.data.zero_()
indexed_tensor = x[seq]
indexed_var = y[seqv]
compare(x, y, seq, indexed_tensor, indexed_var)
def test_indexing_duplicates(self):
x = torch.arange(1., 17).view(4, 4)
y = Variable(x, requires_grad=True)
idx = torch.LongTensor([1, 1, 3, 2, 1, 2])
y[idx].sum().backward()
expected_grad = torch.zeros(4, 4)
for i in idx:
expected_grad[i] += 1
self.assertEqual(y.grad.data, expected_grad)
# with advanced indexing
x = torch.arange(1., 17).view(4, 4)
y = Variable(x, requires_grad=True)
idx = [[1, 1, 3, 2, 1, 2], [0]]
y[idx].sum().backward()
expected_grad = torch.zeros(4, 4)
for i in idx[0]:
for j in idx[1]:
expected_grad[i][j] += 1
self.assertEqual(y.grad.data, expected_grad)
x = torch.arange(1., 17).view(4, 4)
y = Variable(x, requires_grad=True)
idx = [[[1, 2], [0, 0]], [[0, 1], [1, 1]]]
y[idx].sum().backward()
expected_grad = torch.Tensor([[0, 2, 0, 0],
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 0, 0]])
self.assertEqual(y.grad.data, expected_grad)
x = torch.arange(1., 65).view(4, 4, 4)
y = Variable(x, requires_grad=True)
idx = [[1, 1, 1], slice(None), slice(None)]
y[idx].sum().backward()
expected_grad = torch.Tensor(4, 4, 4).zero_()
expected_grad[1].fill_(3)
self.assertEqual(y.grad.data, expected_grad)
def test_volatile_deprecated(self):
v = torch.autograd.torch.randn(3, 3)
with warnings.catch_warnings(record=True) as w:
self.assertFalse(v.volatile)
self.assertIn('volatile', str(w[0].message))
def test_saved_variables_deprecated(self):
class MyFunction(Function):
@staticmethod
def forward(ctx, tensor1, tensor2):
ctx.save_for_backward(tensor1, tensor2)
return tensor1 + tensor2
@staticmethod
def backward(ctx, grad_output):
var1, var2 = ctx.saved_variables
return (grad_output, grad_output)
with warnings.catch_warnings(record=True) as warns:
warnings.simplefilter("always")
x = torch.randn((3, 3), requires_grad=True)
y = torch.randn((3, 3), requires_grad=True)
model = MyFunction()
model.apply(x, y).sum().backward()
has_deprecated = map(lambda warn:
'deprecated' in str(warn) and
'saved_variables' in str(warn),
warns)
has_deprecated = reduce(lambda x, y: x or y, has_deprecated)
self.assertTrue(has_deprecated)
def test_requires_grad(self):
x = torch.randn(5, 5)
y = torch.randn(5, 5)
z = torch.randn(5, 5, requires_grad=True)
a = x + y
self.assertFalse(a.requires_grad)
b = a + z
self.assertTrue(b.requires_grad)
def error():
raise RuntimeError
# Make sure backward isn't called on these
a._backward_hooks = OrderedDict()
x._backward_hooks = OrderedDict()
y._backward_hooks = OrderedDict()
a._backward_hooks['test'] = error
x._backward_hooks['test'] = error
y._backward_hooks['test'] = error
b.backward(torch.ones(5, 5))
def test_requires_grad_(self):
x = torch.randn(5, 5)
y = torch.randn(5, 5, requires_grad=True)
self.assertIs(x, x.requires_grad_())
self.assertTrue(x.requires_grad)
self.assertIs(y, y.requires_grad_())
self.assertTrue(y.requires_grad)
self.assertIs(x, x.requires_grad_(True))
self.assertTrue(x.requires_grad)
self.assertIs(y, y.requires_grad_(True))
self.assertTrue(y.requires_grad)
z = x * y
self.assertRaises(RuntimeError, lambda: z.requires_grad_(False))
self.assertIs(z, z.requires_grad_())
self.assertTrue(z.requires_grad)
self.assertIs(z, z.requires_grad_(True))
self.assertTrue(z.requires_grad)
self.assertIs(x, x.requires_grad_(False))
self.assertFalse(x.requires_grad)
self.assertIs(y, y.requires_grad_(False))
self.assertFalse(y.requires_grad)
def test_requires_grad_inplace(self):
a = torch.randn(5, 5)
b = torch.randn(5, 5, requires_grad=True)
a += b
self.assertTrue(a.requires_grad)
# non-leaf Variable
a = torch.randn(5, 5) + 0
b = torch.randn(5, 5, requires_grad=True)
a += b
self.assertTrue(a.requires_grad)
def test_no_requires_grad_inplace(self):
# basic case, should be able to modify inplace while requires_grad is False
a = torch.randn(2, 3)
a.add_(5)
a.requires_grad = True
a.sum().backward()
self.assertEqual(a.grad.data, torch.ones(2, 3))
# same but with a view
a = torch.randn(2, 3)
b = a[:]
b.add_(5)
a.requires_grad = True
a.sum().backward()
self.assertEqual(a.grad.data, torch.ones(2, 3))
# should fail if requires_grad = True when we modify inplace
a = torch.randn(2, 3)
b = a[:]
a.requires_grad = True
with self.assertRaises(RuntimeError):
a.add_(5)
with self.assertRaises(RuntimeError):
b.add_(5)
@skipIfRocm
def test_requires_grad_factory(self):
x = torch.randn(2, 3)
fns = [torch.ones_like, torch.testing.randn_like]
dtypes = [torch.float32, torch.float64]
for fn in fns:
for requires_grad in [True, False]:
for dtype in dtypes:
for use_cuda in [True, False]:
if not use_cuda:
output = fn(x, dtype=dtype, requires_grad=requires_grad)
self.assertEqual(requires_grad, output.requires_grad)
self.assertIs(dtype, output.dtype)
elif torch.cuda.is_available() and torch.cuda.device_count() > 1:
output = fn(x, dtype=dtype, device=1, requires_grad=requires_grad)
self.assertEqual(requires_grad, output.requires_grad)
self.assertIs(dtype, output.dtype)
self.assertEqual(1, output.get_device())
def test_grad_assignment(self):
x = torch.randn(5, 5)
with self.assertRaises(RuntimeError):
x.grad = torch.randn(2, 2)
with self.assertRaises(RuntimeError):
x.grad = Variable(torch.randn(5, 5).long())
with self.assertRaises(RuntimeError):
x.grad = x
if not torch.cuda.is_available():
raise unittest.SkipTest("CUDA not available")
with self.assertRaises(RuntimeError):
x.grad = Variable(torch.randn(5, 5).cuda())
x = x.cuda().half()
x.grad = torch.zeros_like(x) # would raise an error unless sparse type is properly handled
if torch.cuda.device_count() < 2:
raise unittest.SkipTest("At least 2 CUDA devices needed")
x = Variable(torch.randn(5, 5).cuda(0))
with self.assertRaises(RuntimeError):
x.grad = Variable(torch.randn(5, 5).cuda(1))
def test_duplicate_backward_root(self):
a = torch.randn(5, 5, requires_grad=True)
b = torch.randn(5, 5, requires_grad=True)
x = a * b
grad_output = torch.randn_like(x)
torch.autograd.backward([x, x], [grad_output, grad_output])
self.assertEqual(a.grad.data, b.data * grad_output * 2)
self.assertEqual(b.grad.data, a.data * grad_output * 2)
def test_backward_no_grad(self):
a = torch.randn(5, 5, requires_grad=True)
b = a + 2
with self.assertRaises(RuntimeError):
torch.autograd.backward([b], [None])
def test_next_functions(self):
x = torch.randn(5, 5, requires_grad=True)
y = torch.randn(5, 5, requires_grad=True)
a = x + y
self.assertIsNotNone(a.grad_fn)
next_functions = a.grad_fn.next_functions
self.assertEqual(len(next_functions), 2)
self.assertIsInstance(next_functions[0][0], torch._C._functions.AccumulateGrad)
self.assertEqual(next_functions[0][1], 0)
self.assertIsInstance(next_functions[1][0], torch._C._functions.AccumulateGrad)
self.assertEqual(next_functions[1][1], 0)
b = a + 5
next_functions = b.grad_fn.next_functions
self.assertEqual(len(next_functions), 2)
self.assertIs(next_functions[0][0], a.grad_fn)
self.assertIs(next_functions[1][0], None)
def test_inplace(self):
x = torch.ones(5, 5, requires_grad=True)
y = Variable(torch.ones(5, 5) * 4, requires_grad=True)
z = x * y
q = z + y
w = z * y
z.add_(2)
# Add doesn't need it's inputs to do backward, so it shouldn't raise
q.backward(torch.ones(5, 5), retain_graph=True)
# Mul saves both inputs in forward, so it should raise
self.assertRaises(RuntimeError, lambda: w.backward(torch.ones(5, 5)))
z = x * y
q = z * y
r = z + y
w = z.add_(y)
# w is a the last expression, so this should succeed
w.backward(torch.ones(5, 5), retain_graph=True)
# r doesn't use the modified value in backward, so it should succeed
r.backward(torch.ones(5, 5), retain_graph=True)
# q uses dirty z, so it should raise
self.assertRaises(RuntimeError, lambda: q.backward(torch.ones(5, 5)))
x.grad.data.zero_()
m = x / 2
z = m + y / 8
q = z * y
r = z + y
prev_version = z._version
w = z.exp_()
self.assertNotEqual(z._version, prev_version)
r.backward(torch.ones(5, 5), retain_graph=True)
self.assertEqual(x.grad.data, torch.ones(5, 5) / 2)
w.backward(torch.ones(5, 5), retain_graph=True)
self.assertEqual(x.grad.data, torch.Tensor(5, 5).fill_((1 + math.e) / 2))
self.assertRaises(RuntimeError, lambda: q.backward(torch.ones(5, 5)))
leaf = torch.ones(5, 5, requires_grad=True)
x = leaf.clone()
x.add_(10)
self.assertEqual(x.data, torch.ones(5, 5) * 11)
# x should be still usable
y = x + 2
y.backward(torch.ones(5, 5))
self.assertEqual(leaf.grad.data, torch.ones(5, 5))
z = x * y
x.add_(2)
self.assertRaises(RuntimeError, lambda: z.backward(torch.ones(5, 5)))
def test_mark_non_differentiable(self):
class MyFunction(Function):
@staticmethod
def forward(ctx, input):
output = input > 0
ctx.mark_non_differentiable(output)
return output
@staticmethod
def backward(ctx, grad_output):
return (grad_output * 0).type(torch.DoubleTensor)
x = torch.randn(5, 5, requires_grad=True)
mask = MyFunction.apply(x)
self.assertFalse(mask.requires_grad)
y = x.masked_fill(mask, 0)
y.sum().backward()
def test_mark_non_differentiable_mixed(self):
class MyFunction(Function):
@staticmethod
def forward(ctx, input):
a = input + 1
b = input + 2
ctx.mark_non_differentiable(a)
return a, b
@staticmethod
def backward(ctx, grad_a, grad_b):
self.assertTrue((grad_a == 0).all())
self.assertTrue((grad_b == 1).all())
return grad_b
x = torch.randn(5, 5, requires_grad=True)
a, b = MyFunction.apply(x)
self.assertFalse(a.requires_grad)
self.assertTrue(b.requires_grad)
b.sum().backward()
self.assertEqual(x.grad.data, torch.ones(5, 5))
def test_mark_non_differentiable_none(self):
# This used to segfault because MyFunction would send back null
# gradients to MulBackward, which is implemented in C++. C++
# implemented functions expect incoming grad_ouptuts to be non-null.
class MyFunction(Function):
@staticmethod
def forward(ctx, input):
output = input.clone()
ctx.mark_non_differentiable(output)
return output
@staticmethod
def backward(ctx, grad_output):
return None
x = torch.randn(5, 5, requires_grad=True)
r = MyFunction.apply(x * x)
(r * x).sum().backward()
def test_return_duplicate(self):
class DoubleDuplicate(Function):
@staticmethod
def forward(ctx, x):
output = x * 2
return output, output
@staticmethod
def backward(ctx, grad1, grad2):
return grad1 * 2 + grad2 * 2
def fn(x):
a, b = DoubleDuplicate.apply(x)
self.assertIs(a, b)
return a + b
x = torch.randn(5, 5, requires_grad=True)
gradcheck(fn, [x])
gradgradcheck(fn, [x])
def test_return_duplicate_inplace(self):
class DoubleInplace(Function):
@staticmethod
def forward(ctx, x):
x.mul_(2)
ctx.mark_dirty(x)
return x, x
@staticmethod
def backward(ctx, grad1, grad2):
return grad1 * 2 + grad2 * 2
def inplace_fn(x):
a, b = DoubleInplace.apply(x.clone())
self.assertIs(a, b)
return a + b
x = torch.randn(5, 5, requires_grad=True)
gradcheck(inplace_fn, [x])
gradgradcheck(inplace_fn, [x])
# Can't modify leaf variables in-place
self.assertRaises(RuntimeError, lambda: InplaceFunction.apply(x))
# Functions which modify views in-place must return only one output
self.assertRaises(RuntimeError, lambda: InplaceFunction.apply(x.clone()[0]))
@suppress_warnings
def test_resize(self):
x = torch.ones(2, 3)
self.assertTrue(x.resize(3, 2).size() == (3, 2))
def _test_setitem(self, size, index):
x = torch.ones(*size, requires_grad=True)
y = x + 2
y_version = y._version
y[index] = 2
self.assertNotEqual(y._version, y_version)
y.backward(torch.ones(*size))
expected_grad = torch.ones(*size)
expected_grad[index] = 0
self.assertEqual(x.grad, expected_grad)
def _test_setitem_tensor(self, size, index):
x = torch.ones(*size, requires_grad=True)
y = x + 2
y_version = y._version
value = x.new(x[index].size()).fill_(7)
value.requires_grad = True
y[index] = value
self.assertNotEqual(y._version, y_version)
y.backward(torch.ones(*size))
expected_grad_input = torch.ones(*size)
expected_grad_input[index] = 0
self.assertEqual(x.grad, expected_grad_input)
self.assertEqual(value.grad, torch.ones_like(value))
# case when x broadcasts to as y[1]
x = torch.randn(4, requires_grad=True)
y = torch.zeros(2, 3, 4)
y[1] = x
y.backward(torch.randn(2, 3, 4))
self.assertEqual(x.size(), x.grad.size())
def test_setitem(self):
self._test_setitem((5, 5), 1)
self._test_setitem((5,), 1)
self._test_setitem((1,), 0)
self._test_setitem((10,), [[0, 4, 2]])
self._test_setitem((5, 5), [[0, 4], [2, 2]])
self._test_setitem((5, 5, 5), [slice(None), slice(None), [1, 3]])
self._test_setitem((5, 5, 5), [slice(None), [1, 3], slice(None)])
self._test_setitem((5, 5, 5), [[1, 3], slice(None), slice(None)])
self._test_setitem((5, 5, 5), [slice(None), [2, 4], [1, 3]])
self._test_setitem((5, 5, 5), [[1, 3], [2, 4], slice(None)])
self._test_setitem_tensor((5, 5), 3)
self._test_setitem_tensor((5, 5), [[0, 1], [1, 0]])
self._test_setitem_tensor((5,), 3)
self._test_setitem_tensor((5,), Variable(torch.LongTensor([3]), requires_grad=False).sum())
self._test_setitem_tensor((5,), [[0, 1, 2, 3]])
self._test_setitem_tensor((5, 5, 5), [slice(None), slice(None), [1, 3]])
self._test_setitem_tensor((5, 5, 5), [slice(None), [1, 3], slice(None)])
self._test_setitem_tensor((5, 5, 5), [[1, 3], slice(None), slice(None)])
self._test_setitem_tensor((5, 5, 5), [slice(None), [2, 4], [1, 3]])
self._test_setitem_tensor((5, 5, 5), [[1, 3], [2, 4], slice(None)])
self._test_setitem_tensor((5, 5, 5), [Variable(torch.LongTensor([1,
3]), requires_grad=False), [2, 4], slice(None)])
def test_setitem_mask(self):
mask = torch.ByteTensor(5, 5).bernoulli_()
self._test_setitem((5, 5), Variable(mask))
self._test_setitem((5,), Variable(mask[0]))
self._test_setitem((1,), Variable(mask[0, 0:1]))
self._test_setitem_tensor((5, 5), Variable(mask))
self._test_setitem_tensor((5,), Variable(mask[0]))
def test_select_sum(self):
# both select and sum return Scalars in ATen; ensure they work together.
x = torch.randn(10, requires_grad=True)
def func(x):
return x.select(0, 1).sum()
gradcheck(func, [x])
gradgradcheck(func, [x])
def test_stack(self):
x = torch.randn(10, 10, requires_grad=True)
y = torch.randn(10, 10, requires_grad=True)
z = torch.randn(10, 10, requires_grad=True)
stacked = torch.stack([x, y, z], 0)
grad = torch.randn(3, 10, 10)
stacked.backward(grad)
self.assertEqual(x.grad.data, grad[0])
self.assertEqual(y.grad.data, grad[1])
self.assertEqual(z.grad.data, grad[2])
def test_unbind(self):
stacked = torch.randn(3, 10, 10, requires_grad=True)
x, y, z = stacked.unbind()
grad = torch.randn(3, 10, 10)
torch.autograd.backward([x, y, z], grad.unbind())
self.assertEqual(stacked.grad.data, grad)
# check that it works with only one gradient provided (#9977)
for i in range(3):
stacked = torch.randn(3, 10, 10, requires_grad=True)
outs = stacked.unbind()
gi = grad.unbind()[i]
g, = torch.autograd.grad(outs[i], stacked, gi)
g_expected = torch.stack([gi if j == i else torch.zeros_like(gi)
for j in range(3)], dim=0)
self.assertEqual(g, g_expected)
def test_put(self):
root = torch.randn(4, 5, requires_grad=True)
values = torch.randn(6, requires_grad=True)
idx = Variable(torch.LongTensor([1, 2, 3, -1, -2, -3]))
def func(root, values):
x = root.clone()
x.put_(idx, values)
return x
gradcheck(func, [root, values])
gradgradcheck(func, [root, values])
def test_put_accumulate(self):
root = torch.randn(4, 5, requires_grad=True)
values = torch.randn(6, requires_grad=True)
idx = Variable(torch.LongTensor([1, 2, 3, 1, 2, 3]))
def func(root, values):
x = root.clone()
x.put_(idx, values, accumulate=True)
return x
gradcheck(func, [root, values])
gradgradcheck(func, [root, values])
def test_fill(self):
root = torch.randn(4, 5, requires_grad=True)
def func(root):
x = root.clone()
x.fill_(2)
return x
gradcheck(func, [root])
gradgradcheck(func, [root])
def test_unused_output(self):
x = torch.randn(10, 10, requires_grad=True)
outputs = x.chunk(5)
o = outputs[2]
o = o * 4 + 2
o.sum().backward()
expected_grad = torch.zeros(10, 10)
expected_grad[4:6] = 4
self.assertEqual(x.grad.data, expected_grad)
x.grad.data.zero_()
grad_output = torch.randn(2, 10)
outputs = x.chunk(5)
outputs[0].backward(grad_output)
expected_grad = torch.zeros(10, 10)
expected_grad[:2] = grad_output
self.assertEqual(x.grad.data, expected_grad)
@skipIfRocm
def test_ctc_loss(self):
batch_size = 64
num_labels = 101
target_length = 15
gradcheck_input_size = 10
# device, input_length
tests = [('cpu', 150)]
if torch.cuda.is_available():
tests += [('cuda', 50),
('cuda', 150)]
for device, input_length in tests:
targets = torch.randint(1, num_labels, (batch_size, target_length),
device=device, dtype=torch.long)
x = torch.randn(gradcheck_input_size, device=device, requires_grad=True)
tile_factors = torch.randn(input_length * batch_size * num_labels // gradcheck_input_size + 1,
device=device)
input_lengths = [input_length for _ in range(batch_size)]
target_lengths = [target_length for _ in range(batch_size)]
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])
def test_gc_in_destructor(self):
"""
Previously, if a Function destructor triggered a garbage collection,
the Variable's tp_dealloc handler would get called twice leading to a
segfault.
"""
class CollectOnDelete(Function):
def __del__(self):
gc.collect()
for i in range(10):
Variable(torch.randn(10, 10), _grad_fn=CollectOnDelete())
@unittest.skipIf(torch.cuda.device_count() < 2, "no multi-GPU")
@skipIfRocm
def test_unused_output_gpu(self):
from torch.nn.parallel._functions import Broadcast
x = Variable(torch.randn(5, 5).float().cuda(), requires_grad=True)
outputs = Broadcast.apply(list(range(torch.cuda.device_count())), x)
y = outputs[-1] * 2
y.sum().backward()
self.assertEqual(x.grad.data, torch.ones(5, 5) * 2)
@unittest.skipIf(torch.cuda.device_count() < 2, "no multi-GPU")
def test_backward_device(self):
# check that current device matches the variable's device
device = [None]
class Identity(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
return x.clone()
@staticmethod
def backward(ctx, grad_output):
device[0] = torch.cuda.current_device()
return grad_output.clone()
v = Variable(torch.randn(1).cuda(1), requires_grad=True)
Identity.apply(v).backward()
self.assertEqual(device[0], 1)
@unittest.skipIf(torch.cuda.device_count() < 2, "no multi-GPU")
@skipIfRocm
def test_inputbuffer_add_multigpu(self):
input = torch.randn(1).cuda(0).requires_grad_()
output = input.cuda(1) + input.cuda(1)
output.backward()
def test_detach(self):
x = torch.randn(10, 10, requires_grad=True)
y = x + 2
y = y.detach()
z = y * 4 + 2
self.assertFalse(y.requires_grad)
self.assertFalse(z.requires_grad)
x = torch.randn(10, 10, requires_grad=True)
y = x * 2
y = y.detach()
self.assertFalse(y.requires_grad)
self.assertIsNone(y.grad_fn)
z = x + y
z.sum().backward()
# This is an incorrect gradient, but we assume that's what the user
# wanted. detach() is an advanced option.
self.assertEqual(x.grad.data, torch.ones(10, 10))
# in-place detach
x = torch.randn(10, 10, requires_grad=True)
y = torch.randn(10, 10, requires_grad=True)
a = x * 2
(y + a).sum().backward(retain_graph=True)
a.detach_()
self.assertFalse(a.requires_grad)
(y + a).sum().backward() # this won't backprop to x
self.assertEqual(x.grad.data, torch.ones(10, 10) * 2)
self.assertEqual(y.grad.data, torch.ones(10, 10) * 2)
# in-place deatch on a view raises an exception
view = x.narrow(0, 1, 4)
self.assertRaisesRegex(RuntimeError, 'view', lambda: view.detach_())
def test_detach_base(self):
"detaching base does not detach view"
x = torch.randn(10, 10, requires_grad=True)
view = x.narrow(0, 1, 4)
x.detach_()
self.assertFalse(x.requires_grad)
self.assertTrue(view.requires_grad)
self.assertIsNotNone(view.grad_fn)
self.assertIs(view._base, x)
@skipIfRocm
def _test_type_conversion_backward(self, t, ):
fvar = Variable(t(torch.randn(5, 5).float()), requires_grad=True)
fvar.double().sum().backward()
self.assertEqual(fvar.grad, torch.ones_like(fvar))
self.assertEqual(type(fvar.grad.data), type(fvar.data))
dvar = Variable(t(torch.randn(5, 5).double()), requires_grad=True)
dvar.float().sum().backward()
self.assertEqual(dvar.grad, torch.ones_like(dvar))
self.assertEqual(type(dvar.grad.data), type(dvar.data))
def test_type_conversions(self):
x = torch.randn(5, 5)
self.assertIsInstance(x.float(), torch.FloatTensor)
self.assertIsInstance(x.int(), torch.IntTensor)
if torch.cuda.is_available():
self.assertIsInstance(x.float().cuda(), torch.cuda.FloatTensor)
self.assertIsInstance(x.int().cuda(), torch.cuda.IntTensor)
self.assertIsInstance(x.int().cuda().cpu(), torch.IntTensor)
if torch.cuda.device_count() >= 2:
x2 = x.float().cuda(1)
self.assertIsInstance(x2, torch.cuda.FloatTensor)
self.assertIs(x2.get_device(), 1)
x2 = x.float().cuda()
self.assertIsInstance(x2.data, torch.cuda.FloatTensor)
self.assertIs(x2.get_device(), 0)
x2 = x2.cuda(1)
self.assertIsInstance(x2, torch.cuda.FloatTensor)
self.assertIs(x2.get_device(), 1)
y = Variable(torch.randn(5).cuda(1), requires_grad=True)
y.cpu().sum().backward()
self.assertIs(y.grad.get_device(), 1)
self.assertIs(y.long().data.get_device(), 1)
for t in [torch.DoubleTensor, torch.FloatTensor, torch.IntTensor, torch.ByteTensor]:
for y_var in (True, False):
y = torch.randint(5, (5, 5), dtype=t.dtype)
y = Variable(y) if y_var else y
self.assertIsInstance(x.type(t), t)
self.assertIsInstance(x.type_as(y), t)
# TODO: t.dtype should work
t_dtype = t().dtype
self.assertIsInstance(x.type(t_dtype), t)
self.assertIs(t_dtype, x.type(t_dtype).dtype)
self.assertEqual(y.data_ptr(), y.type(t).data_ptr())
if torch.cuda.is_available():
for x_cuda in (True, False):
for y_cuda in (True, False):
x_c = x.cuda() if x_cuda else x
y_c = y.cuda() if y_cuda else y
_, y_type = y_c.type().rsplit('.', 1)
y_typestr = ('torch.cuda.' if y_cuda else 'torch.') + y_type
self.assertEqual(y_c.type(), x_c.type(y_typestr).type())
self.assertIs(y_c.dtype, x_c.type(y_c.dtype).dtype)
self.assertEqual(y_c.data_ptr(), y_c.cuda().data_ptr() if y_cuda else y_c.data_ptr())
self._test_type_conversion_backward(lambda x: x)
if torch.cuda.is_available():
self._test_type_conversion_backward(lambda x: x.cuda())
if torch.cuda.device_count() >= 2:
# one of these has to be the non-default device
self._test_type_conversion_backward(lambda x: x.cuda(0))
self._test_type_conversion_backward(lambda x: x.cuda(1))
def _test_pyscalar_conversions(self, t, integral_conv):
# integral -> integral
l = t(torch.zeros(1, 1, 1, dtype=torch.long))
pyscalar = -12345
l[0] = pyscalar
self.assertEqual(integral_conv(l), pyscalar)
# floating point -> floating point
f = Variable(t(torch.randn(1, 1)))
pyscalar = -12345.1
f[0] = pyscalar
self.assertEqual(float(f), pyscalar)
f[0] = nan
self.assertTrue(math.isnan(float(f)))
f[0] = inf
self.assertEqual(float(f), inf, allow_inf=True)
f[0] = -inf
self.assertEqual(float(f), -inf, allow_inf=True)
# integral -> floating point
# check we can convert something that loses precision
pyscalar = 1234567890123456789
self.assertNotEqual(pyscalar, integral_conv(float(pyscalar)))
l[0] = pyscalar
self.assertEqual(float(l), float(pyscalar))
# floating point -> integral
f[0] = nan
self.assertRaises(ValueError, lambda: integral_conv(f[0]))
f[0] = inf
self.assertRaises(OverflowError, lambda: integral_conv(f[0]))
f[0] = -inf
self.assertRaises(OverflowError, lambda: integral_conv(f[0]))
f[0] = sys.float_info.max
self.assertEqual(integral_conv(f), sys.float_info.max)
# bool, nonzero
def test_nonzero(tensor, value, expected):
tensor[0] = value
self.assertEqual(expected, bool(tensor))
self.assertEqual(expected, True if tensor else False)
test_nonzero(l, 0, False)
test_nonzero(l, -2, True)
test_nonzero(f, 0.0, False)
test_nonzero(f, sys.float_info.min, True)
test_nonzero(f, nan, bool(nan))
test_nonzero(f, inf, bool(inf))
test_nonzero(f, -inf, bool(-inf))
def test_pyscalar_conversions(self):
self._test_pyscalar_conversions(lambda x: x, lambda x: int(x))
if sys.version_info[0] == 2:
self._test_pyscalar_conversions(lambda x: x, lambda x: long(x))
if torch.cuda.is_available():
self._test_pyscalar_conversions(lambda x: x.cuda(), lambda x: int(x))
if sys.version_info[0] == 2:
self._test_pyscalar_conversions(lambda x: x.cuda(), lambda x: long(x))
@unittest.skipIf(not torch.cuda.is_available(), "CUDA unavailable")
@skipIfRocm
def test_pin_memory(self):
x = torch.randn(2, 2, requires_grad=True)
self.assertEqual(x, x.pin_memory())
self.assertIsNot(x, x.pin_memory())
self.assertTrue(x.pin_memory().requires_grad)
gradcheck(lambda x: x.pin_memory(), [x])
gradgradcheck(lambda x: x.pin_memory(), [x])
def test_isolated_node(self):
x = torch.randn(5, 5, requires_grad=True)
y = torch.randn(5, 5, requires_grad=True)
a = x + y
b = torch.max(a, 1, True)[1].repeat(1, 5).double()
o = (b + a).sum()
o.backward()
def test_shape(self):
x = torch.randn(3, 4)
self.assertEqual(2, len(x.shape))
self.assertEqual(x.shape[0], 3)
self.assertEqual(x.shape[1], 4)
def test_numpy_requires_grad(self):
x = torch.randn(2, 2, requires_grad=True)
self.assertRaisesRegex(RuntimeError, 'requires grad', lambda: x.numpy())
def test_return_leaf(self):
class Identity(Function):
def forward(self, a, b):
return a, a + b
def backward(self, grad_a, grad_b):
return grad_a + grad_b, grad_b
hook_called = [False]
x = torch.randn(5, 5, requires_grad=True)
y = torch.randn(5, 5, requires_grad=True)
q, p = Identity()(x, y)
# Make sure hooks only receive grad from usage of q, not x.
def hook(grad):
hook_called[0] = True
self.assertEqual(grad.data, torch.ones(5, 5))
q.register_hook(hook)
(q + p + x).sum().backward()
self.assertEqual(x.grad.data, torch.ones(5, 5) * 3)
self.assertEqual(y.grad.data, torch.ones(5, 5))
self.assertTrue(hook_called[0])
def test_return_leaf_inplace(self):
class Inplace(InplaceFunction):
def forward(self, a, b):
self.mark_dirty(a)
return a.add_(b), b + 2
def backward(self, grad_a, grad_b):
return grad_a, grad_a + grad_b
x = torch.randn(5, 5)
y = torch.randn(5, 5, requires_grad=True)
fn = Inplace(True)
q, p = fn(x, y)
self.assertIs(q, x)
self.assertIs(q.grad_fn, fn)
self.assertTrue(q.requires_grad)
q.sum().backward()
self.assertEqual(y.grad.data, torch.ones(5, 5))
def test_leaf_assignment(self):
x = torch.randn(5, 5)
y = torch.randn(5, requires_grad=True)
z = torch.randn(5, requires_grad=True)
x[0] = y
x[1] = 2 * z
self.assertTrue(x.requires_grad)
self.assertIsNot(x.grad_fn, None)
x.sum().backward()
self.assertEqual(y.grad.data, torch.ones(5))
self.assertEqual(z.grad.data, torch.ones(5) * 2)
def test_no_grad_assignment(self):
x = torch.randn(5, 5, requires_grad=True)
y = torch.randn(5)
with torch.no_grad():
x[0] = y
self.assertTrue(x.requires_grad)
self.assertIsNone(x.grad_fn)
def test_no_grad_modifies_version(self):
x = torch.randn(5, requires_grad=True)
y = torch.randn(5, requires_grad=True)
z = (x * y).sum()
with torch.no_grad():
x *= 2
self.assertRaisesRegex(RuntimeError, 'modified by an inplace operation',
lambda: z.backward())
def test_no_grad_input(self):
class MyFunction(Function):
@staticmethod
def forward(self, x):
return x
@staticmethod
def backward(self, grad_output):
return grad_output
x = torch.randn(5, requires_grad=True)
with torch.no_grad():
y = MyFunction.apply(x)
self.assertTrue(x.requires_grad)
self.assertIsNone(y.grad_fn)
def test_backward_copy(self):
# This tests checks backward engine for a very subtle bug that appreared
# in one of the initial versions of autograd. Gradients tensors were
# simply stored in lists while the function waited for all its gradients
# to be computed. However, sometimes an output was used multiple times,
# so the gradients needed to be summed. Engine used to keep a need_copy
# set of tensors that will need a clone upon next addition and removed
# them from the set as soon as the clone was performed. However, this
# could lead to incorrect results if the same gradient tensor was
# buffered in three places in the graph:
# 1. When accumulating gradients in one of these places it was cloned
# and removed from need_copy set.
# 2. When accumulating in second place, it wasn't in the need_copy set,
# so the gradients were simply accumulated in-place (which already
# modified the grad in 3rd place)
# 3. When accumulating in the third place, it wasn't in the need_copy set
# as well, so the incoming gradient was summed in-place, yielding
# incorrect results in all functions, except the first one.
x = torch.ones(5, 5, requires_grad=True)
y = torch.ones(5, 5, requires_grad=True)
# Simulate that we're in the middle of the graph
a = x + 2
b = y + 2
c = x + 2
# This op will just return grad_output two times in backward
add1 = a + b
add2 = add1 + c
# Simulate a long branch, so grad_output will get buffered.
for i in range(4):
a = a * 2
b = b * 2
c = c * 2
branch = a + b + c
out = add2 + branch
# expected gradients are:
# for x: 34 (16 from final a, 16 from final c, 2 from add2)
# for y: 17 (16 from final b, 1 from add2)
grad_output = torch.ones(5, 5)
out.backward(grad_output)
self.assertEqual(x.grad, torch.ones(5, 5) * 34)
self.assertEqual(y.grad, torch.ones(5, 5) * 17)
def test_save_none_for_backward(self):
test_case = self
class MyFn(Function):
def forward(self, input):
self.save_for_backward(None, input, None)
return input * input
def backward(self, grad_output):
n1, input, n2 = self.saved_tensors
test_case.assertIsNone(n1)
test_case.assertIsNone(n2)
return 2 * input * grad_output
x = torch.randn(5, 5, requires_grad=True)
y = MyFn()(x)
y.sum().backward()
self.assertEqual(x.grad, 2 * x)
def test_too_many_grads(self):
class MyFn(Function):
def forward(self, input):
return input
def backward(self, grad_output):
return grad_output, None, None
x = torch.randn(5, 5, requires_grad=True)
y = MyFn()(x)
y.sum().backward()
self.assertEqual(x.grad, torch.ones_like(x))
def test_pickle(self):
x = torch.randn(10, 10, requires_grad=True)
y = torch.randn(10, 10, requires_grad=False)
def assert_strict_equal(var1, var2):
self.assertEqual(var1.data, var2.data)
self.assertEqual(var1.requires_grad, var2.requires_grad)
serialized = [pickle.dumps([x, y], protocol=p) for p in range(3)]
for dump in serialized:
xc, yc = pickle.loads(dump)
assert_strict_equal(xc, x)
assert_strict_equal(yc, y)
def test_dep_nograd(self):
class F1(Function):
def forward(self, input):
out = torch.randn(input.size())
self.mark_non_differentiable(out)
return input, out
def backward(self, grad_output, ignored):
return grad_output
class F2(Function):
def forward(self, input, ignored):
return input
def backward(self, grad_output):
return grad_output, None
x = torch.randn(5, requires_grad=True)
a, b = F1()(x)
b = b + 1 # separate F1 from F2 by another op
self.assertTrue(a.requires_grad)
self.assertFalse(b.requires_grad)
c = F2()(a, b)
c.backward(torch.ones(c.size()))
self.assertEqual(x.grad.data, torch.ones(x.size()))
def test_set_grad_enabled(self):
x = torch.tensor([1.], requires_grad=True)
with torch.set_grad_enabled(False):
y = x * 2
self.assertFalse(y.requires_grad)
with torch.set_grad_enabled(True):
y = x * 2
self.assertTrue(y.requires_grad)
with torch.set_grad_enabled(False):
torch.set_grad_enabled(True)
y = x * 2
self.assertTrue(y.requires_grad)
def test_reentrant(self):
y_data = torch.randn(2, 2)
class Reenter(Function):
@staticmethod
def forward(ctx, x):
with torch.enable_grad():
ctx.x = Variable(x.data, requires_grad=True)
ctx.y = Variable(y_data, requires_grad=True)
ctx.output_var = ctx.x * ctx.y
return ctx.output_var.detach()
@staticmethod
def backward(ctx, grad_output):
with torch.enable_grad():
ctx.output_var.sum().backward()
return ctx.x.grad * grad_output
x = torch.randn(2, 2, requires_grad=True)
out = Reenter.apply(x)
out.sum().backward()
self.assertEqual(x.grad.data, y_data)
def test_broadcast_tensors(self):
f_args_variable = (torch.randn(3, requires_grad=True),
torch.randn(1, 2, 1, requires_grad=True),
torch.randn(1, 1, requires_grad=True),
torch.randn(5, 1, 1, requires_grad=True))
f_args_tensor = deepcopy(unpack_variables(f_args_variable))
run_functional_checks(self, "test_broadcast_tensors", "broadcast",
lambda a, b, c, d: torch.broadcast_tensors(a, b, c, d),
True, f_args_variable, f_args_tensor)
def test_cat(self):
f_args_variable = (torch.randn(1, S, S, requires_grad=True),
torch.randn(2, S, S, requires_grad=True),
torch.randn(3, S, S, requires_grad=True),
0)
f_args_tensor = deepcopy(unpack_variables(f_args_variable))
run_functional_checks(self, "test_cat", "cat",
lambda a, b, c, dim: torch.cat((a, b, c), dim),
True, f_args_variable, f_args_tensor)
def test_cat_negdim_1(self):
f_args_variable = (torch.randn(S, S, 1, requires_grad=True),
torch.randn(S, S, 2, requires_grad=True),
torch.randn(S, S, 3, requires_grad=True),
-1)
f_args_tensor = deepcopy(unpack_variables(f_args_variable))
run_functional_checks(self, "test_cat_negdim_1", "cat",
lambda a, b, c, dim: torch.cat((a, b, c), dim),
True, f_args_variable, f_args_tensor)
def test_cat_negdim_2(self):
f_args_variable = (torch.randn(S, 1, S, requires_grad=True),
torch.randn(S, 2, S, requires_grad=True),
torch.randn(S, 3, S, requires_grad=True),
-2)
f_args_tensor = deepcopy(unpack_variables(f_args_variable))
run_functional_checks(self, "test_cat_negdim_2", "cat",
lambda a, b, c, dim: torch.cat((a, b, c), dim),
True, f_args_variable, f_args_tensor)
def test_cat_empty_legacy(self):
f_args_variable = (torch.randn(0, requires_grad=True),
torch.randn(S, S, requires_grad=True))
# gradgradcheck doesn't work, probably because legacy size tracking is wrong somewhere,
# hence False passed below, but gradcheck checked explicitly.
f_args_tensor = deepcopy(unpack_variables(f_args_variable))
run_functional_checks(self, "test_cat_empty_legacy", "cat",
lambda a, b: torch.cat((a, b)),
False, f_args_variable, f_args_tensor)
self.assertTrue(gradcheck(lambda a, b: torch.cat((a, b)), f_args_variable, eps=1e-6, atol=PRECISION))
def test_cat_empty(self):
f_args_variable = (torch.randn(0, S, requires_grad=True),
torch.randn(S, S, requires_grad=True))
f_args_tensor = deepcopy(unpack_variables(f_args_variable))
run_functional_checks(self, "test_cat_empty", "cat",
lambda a, b: torch.cat((a, b)),
True, f_args_variable, f_args_tensor)
def test_potrf(self):
root = Variable(torch.tril(torch.rand(S, S)), requires_grad=True)
def run_test(upper):
def func(root):
x = torch.mm(root, root.t())
return torch.potrf(x, upper)
gradcheck(func, [root])
gradgradcheck(func, [root])
run_test(upper=True)
run_test(upper=False)
@skipIfNoLapack
def test_trtrs(self):
def _test_with_size(N, C):
A = torch.rand(N, N, requires_grad=True)
b = torch.rand(N, C, requires_grad=True)
for upper, transpose, unitriangular in product((True, False), repeat=3):
def func(A, b):
return torch.trtrs(b, A, upper, transpose, unitriangular)
gradcheck(func, [A, b])
gradgradcheck(func, [A, b])
_test_with_size(S, S + 1)
_test_with_size(S, S - 1)
@unittest.skipIf(not TEST_MKL, "PyTorch is built without MKL support")
def test_fft_ifft_rfft_irfft(self):
def _test_complex(sizes, signal_ndim):
x = torch.randn(sizes, requires_grad=True, dtype=torch.double)
for normalized in (True, False):
def fft(x):
return x.fft(signal_ndim, normalized=normalized)
gradcheck(fft, [x])
gradgradcheck(fft, [x], gen_non_contig_grad_outputs=True)
def ifft(fx):
return fx.ifft(signal_ndim, normalized=normalized)
# Use output of fft(x) for inverse fft, due to symmetry requirements
fx = fft(x).detach()
fx.requires_grad = True
gradcheck(ifft, [fx])
gradgradcheck(ifft, [fx], gen_non_contig_grad_outputs=True)
def _test_real(sizes, signal_ndim):
x = torch.randn(sizes, requires_grad=True, dtype=torch.double)
if x.dim() == signal_ndim:
start_dim = 0
else:
start_dim = 1
signal_sizes = x.size()[start_dim:start_dim + signal_ndim]
for normalized, onesided in product((True, False), repeat=2):
def rfft(x):
return x.rfft(signal_ndim, normalized=normalized, onesided=onesided)
gradcheck(rfft, [x])
gradgradcheck(rfft, [x], gen_non_contig_grad_outputs=True)
# Generally speaking, irfft itself won't and can't pass the
# current gradcheck as it assumes the input follows conjugate
# symmetry, an requirement that is never true with our point
# numerical Jacobian estimate. Without input symmtry, irfft's
# behavior is undefined.
#
# Even onesided results can't remove all redundancy. For
# example, consider the .select(last_signal_dim, 0) slice.
# It is entirely represented in the onesided results (except
# for 1D), and will be reflected onto itself!
#
# So only 1D onesided irfft should pass grad check as it is
# guaranteed that the input has no symmetrical values.
#
# In other cases, we test a function that first uses rfft to
# generate a tensor that follows the conjugate symmetry irfft
# expects, and then feeds it into irfft. Since rfft is already
# tested above, we thereby verify the correctness of irfft.
if signal_ndim == 1 and onesided:
def irfft(fx):
return fx.irfft(signal_ndim, normalized=normalized,
onesided=onesided, signal_sizes=signal_sizes)
# Use output of rfft(x) for inverse rfft, due to symmetry requirements
fx = rfft(x).detach()
fx.requires_grad = True
gradcheck(irfft, [fx])
gradgradcheck(irfft, [fx], gen_non_contig_grad_outputs=True)
else:
# Test this function: f(x) = ifft(rfft(x) + rfft(z)), where
# z is some fixed tensor of same size as x. rfft(z) term is
# needed because otherwise f becomes identity.
z = torch.randn(sizes, dtype=torch.double)
fz = z.rfft(signal_ndim, normalized=normalized, onesided=onesided)
def rfft_irfft(x):
fx = x.rfft(signal_ndim, normalized=normalized, onesided=onesided)
y = fx + fz
return y.irfft(signal_ndim, normalized=normalized,
onesided=onesided, signal_sizes=signal_sizes)
gradcheck(rfft_irfft, [x])
gradgradcheck(rfft_irfft, [x], gen_non_contig_grad_outputs=True)
_test_real((2, 10), 1)
_test_real((2, 3, 4), 2)
_test_real((2, 3, 4, 3), 3)
_test_complex((2, 2, 10, 2), 1)
_test_complex((1, 2, 3, 4, 2), 2)
_test_complex((2, 1, 3, 4, 3, 2), 3)
def test_variable_traverse(self):
def get_out_and_unrefed_cycle():
inp = torch.randn(10, requires_grad=True)
tmp = inp.view(10, 1)
out = tmp.view(10)
# Create a reference cycle that contains an
# intermediary Variable in the graph
my_list = []
my_list.append(tmp)
my_list.append(my_list)
return out
out = get_out_and_unrefed_cycle()
gc.collect()
# This will segfault if things have been erroneously released
out.backward(torch.randn(out.size()))
def test_norm_subgradient(self):
def run_test(input_size, norm_deg):
input = torch.zeros(*input_size, requires_grad=True)
input.norm(norm_deg).backward()
self.assertEqual(input.grad.data.abs().sum(), 0)
run_test((10,), 2)
run_test((10, 10), 2)
run_test((10,), 3)
run_test((10,), 1)
run_test((10,), 1.5)
def test_pow_zero_tensor_gradient(self):
def run_test(input_size, exponent):
input = torch.zeros(*input_size, requires_grad=True)
input.pow(exponent).sum().backward()
self.assertEqual(input.grad.data.abs().sum(), 0)
run_test((10,), torch.zeros(10))
run_test((10, 10), torch.zeros(10, 10))
run_test((10,), 0)
def test_pow_scalar_base(self):
a = torch.arange(1, 13, dtype=torch.double).view(3, 4).requires_grad_()
gradcheck(lambda a: torch.pow(2, a), (a,))
def test_pinverse(self):
# Why is pinverse tested this way, and not ordinarily as other linear algebra methods?
# 1. Pseudo-inverses are not generally continuous, which means that they are not differentiable
# 2. Derivatives for pseudo-inverses exist typically for constant rank (Golub et al, 1973)
# 3. This method creates two orthogonal matrices, and a constructs a test case with large
# singular values (given by x to the function).
# 4. This will ensure that small perturbations don't affect the rank of matrix, in which case
# a derivative exists.
# 5. This test exists since pinverse is implemented using SVD, and is hence a backpropable method
m, n = 5, 10
U = torch.randn(n, m).qr()[0].t() # Orthogonal with dimensions m x n
V = torch.randn(n, m).qr()[0].t() # Orthogonal with dimensions m x n
def func(x):
S = torch.cat([x, torch.zeros(n - m)], 0)
M = U.mm(torch.diag(S)).mm(V.t())
return M.pinverse()
gradcheck(func, [torch.rand(m).add_(1).requires_grad_()])
gradcheck(func, [torch.rand(m).add_(10).requires_grad_()])
gradgradcheck(func, [torch.rand(m).add_(1).requires_grad_()])
gradgradcheck(func, [torch.rand(m).add_(10).requires_grad_()])
@skipIfRocm
def test_chain_matmul(self):
def gen_matrices(p):
matrices = []
for (pi, pi_1) in zip(p[:-1], p[1:]):
matrices.append(torch.randn(pi, pi_1).requires_grad_())
return matrices
gradcheck(torch.chain_matmul, gen_matrices([5, 10, 15, 5]))
gradcheck(torch.chain_matmul, gen_matrices([3, 5, 2, 6]))
gradcheck(torch.chain_matmul, gen_matrices([6, 2, 4, 8, 10]))
gradgradcheck(torch.chain_matmul, gen_matrices([5, 10, 15, 5]))
gradgradcheck(torch.chain_matmul, gen_matrices([3, 5, 2, 6]))
gradgradcheck(torch.chain_matmul, gen_matrices([6, 2, 4, 8, 10]))
def test_profiler(self):
x = torch.randn(10, 10)
with profile() as p:
y = x * 2 + 4
last_end = 0
names = ['mul', 'add']
self.assertEqual(len(p.function_events), len(names))
for info, expected_name in zip(p.function_events, names):
self.assertGreater(info.cpu_interval.start, last_end)
self.assertEqual(info.name, expected_name)
last_end = info.cpu_interval.end
def test_dir(self):
x = torch.randn(10, 10)
keys = dir(x)
self.assertIn('shape', keys)
for key in keys:
self.assertTrue(hasattr(x, key))
def test_as_strided(self):
def test(x, prepro_fn, size, strides, offset=None):
x = x.to(torch.double).detach().requires_grad_()
# Check that forward will **not** resize storage because it may
# cause NaN in output and fail numerical Jacobian check consequently
with torch.no_grad():
y = prepro_fn(x) if prepro_fn is not None else x
max_offset = sum((si - 1) * st for si, st in zip(size, strides))
max_offset += offset if offset is not None else y.storage_offset()
assert max_offset < len(y.storage()), "test case resizes storage"
def closure(x):
if prepro_fn is not None:
x = prepro_fn(x)
return x.as_strided(size, strides, offset)
gradcheck(closure, [x])
gradgradcheck(closure, [x])
# test
test(torch.arange(0, 25), lambda x: x.view(5, 5), [3, 3], [6, 2], 2)
# test crazy stride at dim with size 1 case
test(torch.randn(12), None, [1, 2, 1, 5], [0, 5, 100, 1], 2)
# test expand case
test(torch.randn(5), None, [3, 3, 3], [0, 1, 0], 2)
test(torch.randn(5), None, [3, 3, 3], [0, 0, 0], 4)
test(torch.randn(5), lambda x: x.expand(5, 5), [5, 5], [0, 1], 0)
# test non-expand overlapping case
test(torch.randn(35), None, [6, 6], [5, 1], 2)
test(torch.randn(15), None, [3, 2], [3, 6], 2)
# test transpose case
test(torch.randn(3, 4), None, [4, 3], [1, 4])
# test "getting things outside the input" case
x = torch.randn(6, 2)
test(x[3:], None, [3, 2], [2, 1], 0) # should be all zeros
self.assertEqual(x[3:].as_strided([3, 2], [2, 1], 0), x[:3])
# test select on expanded input case
test(torch.randn(2, 3), lambda x: x.expand(10, 2, 3), [2, 3], [3, 1], 0)
def _test_where_functional(self, t):
x = Variable(t(torch.randn(5, 5)), requires_grad=True)
y = Variable(t(torch.randn(5, 5)), requires_grad=True)
cond = Variable(t(mask_not_all_zeros((5, 5))), requires_grad=False)
def where(cond, x, y):
return torch.where(cond, x, y)
gradcheck(where, [cond, x, y], raise_exception=True)
gradgradcheck(where, [cond, x, y], [Variable(t(torch.randn(5, 5)))])
x = Variable(t(torch.randn(5, 1, 5)), requires_grad=True)
y = Variable(t(torch.randn(5, 5, 1)), requires_grad=True)
gradcheck(where, [cond, x, y], raise_exception=True)
gradgradcheck(where, [cond, x, y], [Variable(t(torch.randn(5, 5, 5)))])
def test_where_functional(self):
self._test_where_functional(lambda t: t)
@unittest.skipIf(not torch.cuda.is_available(), "CUDA unavailable")
@skipIfRocm
def test_where_functional_cuda(self):
self._test_where_functional(lambda t: t.cuda())
def test_reduce_dtype(self):
def test_reduction(op):
x = torch.randn(3, 3, dtype=torch.float, requires_grad=True)
grad1, = torch.autograd.grad([op(x)], [x])
grad2, = torch.autograd.grad([op(x, dtype=torch.double)], [x])
self.assertEqual(grad1, grad2)
self.assertEqual(grad2.dtype, torch.float)
gi = torch.randn(3, dtype=torch.float)
grad1, = torch.autograd.grad([op(x, dim=0)], [x], gi)
grad2, = torch.autograd.grad([op(x, dim=0, dtype=torch.double)], [x], gi.double())
self.assertEqual(grad1, grad2)
self.assertEqual(grad2.dtype, torch.float)
test_reduction(torch.sum)
test_reduction(torch.prod)
def test_inplace_view_backprop_base(self):
# modify view and back-prop through base
root = torch.randn(2, 2, requires_grad=True)
x = root.clone()
v1 = x.narrow(0, 0, 1)
v1.mul_(2)
x.sum().backward()
self.assertEqual(root.grad.data.tolist(), [[2, 2], [1, 1]])
def test_inplace_view_backprop_view_of_view(self):
# modify view and backprop through view-of-view
root = torch.randn(2, 2, requires_grad=True)
x = root.clone()
v1 = x.narrow(0, 0, 1)
v2 = x.narrow(0, 0, 1)
v1.mul_(2)
v2.sum().backward()
self.assertEqual(root.grad.data.tolist(), [[2, 2], [0, 0]])
def test_inplace_view_of_view(self):
# modify view-of-view and backprop through base
root = torch.randn(2, 2, requires_grad=True)
x = root.clone()
v1 = x.narrow(0, 0, 1)
v2 = v1.narrow(1, 1, 1)
v2.mul_(2)
x.sum().backward()
self.assertEqual(root.grad.data.tolist(), [[1, 2], [1, 1]])
def test_inplace_view_gradcheck(self):
# gradcheck modifications to views
a = torch.randn(4, 4, requires_grad=True)
b = torch.randn(2, 2, requires_grad=True)
def func(root, b):
x = root.clone()
x.narrow(1, 2, 2).narrow(0, 1, 2).mul_(b)
x.narrow(1, 0, 2).narrow(0, 1, 2).mul_(b)
return x
gradcheck(func, [a, b], raise_exception=True)
go = torch.randn(a.size(), requires_grad=True)
gradgradcheck(func, (a, b), (go,))
def test_inplace_view_makes_base_require_grad(self):
# in-place modification to view makes base require grad
a = torch.randn(4, 4, requires_grad=False)
b = torch.randn(4, 2, requires_grad=True)
def func(root, b):
x = root.clone()
self.assertFalse(x.requires_grad)
x.narrow(1, 2, 2).mul_(b)
self.assertTrue(x.requires_grad)
return x
gradcheck(func, [a, b], raise_exception=True)
go = torch.randn(a.size(), requires_grad=True)
gradgradcheck(func, (a, b), (go,))
def test_inplace_view_backprop_view(self):
# modify view and backprop through view
a = Variable(torch.Tensor([2, 5]), requires_grad=False)
b = Variable(torch.Tensor([3]), requires_grad=True)
res = a.narrow(0, 1, 1).mul_(b)
res.sum().backward()
self.assertEqual(b.grad.data.tolist(), [5])
self.assertIsNone(a.grad)
def test_inplace_view_modify_base(self):
# Test that an in-place operation on a base that forced it to require
# grad also forces any previous views to require grad and backprop
# correctly
r = torch.ones(1, requires_grad=True)
def fn(r):
x = torch.ones(5)
v = x.select(0, 1)
self.assertFalse(v.requires_grad)
self.assertIsNone(v.grad_fn)
x.add_(r) # v is now dependent on r due to the in-place op on x
self.assertTrue(v.requires_grad)
return v
gradcheck(fn, [r])
gradgradcheck(fn, [r])
def test_inplace_view_python(self):
# in-place modifications of Python-autograd created view
a = torch.randn(4, 4, requires_grad=True)
b = torch.randn(2, 2, requires_grad=True)
class PyAdd(torch.autograd.Function):
@staticmethod
def forward(ctx, x, y):
ctx.mark_dirty(x)
x.add_(y)
return x
@staticmethod
def backward(ctx, grad):
return grad, grad
def func(root, b):
x = root.clone()
PyAdd.apply(x.narrow(1, 2, 2).narrow(0, 1, 2), b)
PyAdd.apply(x.narrow(1, 0, 2).narrow(0, 1, 2), b)
return x
gradcheck(func, [a, b], raise_exception=True)
go = torch.randn(a.size(), requires_grad=True)
gradgradcheck(func, (a, b), (go,))
def test_inplace_view_non_contig(self):
data = torch.ones(2, 3, 2).select(2, 1).t()
root = Variable(data, requires_grad=True)
x = root.clone()
v1 = x.narrow(0, 0, 1)
v2 = v1.narrow(1, 1, 1)
v2.mul_(2)
x.sum().backward()
self.assertEqual(root.grad.data.tolist(), [[1, 2], [1, 1], [1, 1]])
def test_inplace_view_saved_output(self):
# Test an in-place operation on a view in which the in-place op saves
# its output. Previously, this created a reference cycle.
dealloc = [0]
class IncrementOnDelete(object):
def __del__(self):
dealloc[0] += 1
def test():
root = torch.randn(3, 3, requires_grad=True)
copy = root.clone()
copy.grad_fn.register_hook(IncrementOnDelete())
view = copy.view(9)
torch.nn.functional.relu(view, inplace=True)
test()
self.assertEqual(dealloc[0], 1)
def test_mul_out(self):
a = torch.randn(2, 2, requires_grad=True)
b = torch.randn(2, 2, requires_grad=True)
x = torch.zeros_like(a)
# out=... functions don't support automatic differentiation currently
self.assertRaisesRegex(RuntimeError, 'out=', lambda: torch.mul(a, b, out=x))
# the inputs can require grad if we're in no_grad() mode
with torch.no_grad():
torch.mul(a, b, out=x)
self.assertEqual(x, a * b)
def test_mul_out_result_requires_grad(self):
a = torch.randn(2, 2)
b = torch.randn(2, 2)
x = torch.zeros(2, 2, requires_grad=True)
# we should throw an exception if the output requires grad
self.assertRaisesRegex(RuntimeError, 'out=', lambda: torch.mul(a, b, out=x))
def test_diagonal_derivative_requires_grad(self):
# test that the backward requires grad
# we do this is because diagonal_backward uses inplace
# operations and gradgradcheck does not catch whether
# they works as expected (it will succeed even if
# the gradient has requires_grad == False
a = torch.randn(5, 6, requires_grad=True)
b = torch.diagonal(a)**2
c = b.sum()
d, = torch.autograd.grad(c, a, retain_graph=True, create_graph=True)
self.assertTrue(d.requires_grad)
@staticmethod
def _test_set_requires_grad_only_for_floats(self, cuda):
dtypes = [torch.int64, torch.int32, torch.int16, torch.int8,
torch.float, torch.double]
if cuda:
dtypes.append(torch.half)
def f1(dt):
a = torch.ones(1, dtype=dt, device='cuda' if cuda else 'cpu')
a.requires_grad_()
def f2(dt):
a = torch.ones(1, dtype=dt, device='cuda' if cuda else 'cpu')
a.requires_grad = True
def f3(dt):
torch.ones(1, dtype=dt, device='cuda' if cuda else 'cpu', requires_grad=True)
for dt in dtypes:
a = torch.ones(1, dtype=dt, device='cuda' if cuda else 'cpu')
a.requires_grad = False # should always work
a.requires_grad_(False)
for f in [f1, f2, f3]:
if dt.is_floating_point:
f(dt)
else:
with self.assertRaisesRegex(RuntimeError, 'floating point',
msg="dt: {} device: {}".format(a.dtype, a.device)):
f(dt)
@unittest.skipIf(not torch.cuda.is_available(), "CUDA unavailable")
@skipIfRocm
def test_set_requires_grad_only_for_floats_cuda(self):
self._test_set_requires_grad_only_for_floats(self, True)
def test_set_requires_grad_only_for_floats(self):
self._test_set_requires_grad_only_for_floats(self, False)
@unittest.skipIf(not torch.cuda.is_available(), "CUDA unavailable")
@skipIfRocm
def test_rnn_backward_to_input_but_not_parameters_cuda(self):
# this checks whether it is possible to not require
# weight parameters, but require inputs, see #7722
dev = torch.device('cuda')
l = torch.nn.LSTM(2, 3).to(dev)
for p in l.parameters():
p.requires_grad = False
s = torch.randn(1, 1, 2, requires_grad=True, device=dev)
out, _ = l(s)
out.sum().backward()
self.assertFalse(s.grad is None or s.grad.abs().sum().item() == 0)
@unittest.skipIf(not torch.cuda.is_available(), "CUDA unavailable")
@skipIfRocm
def test_lstmcell_backward_only_one_output_grad(self):
# checks that undefined gradients doen't hamper the backward
# see #11872
dev = torch.device('cuda')
l = torch.nn.LSTMCell(2, 3).to(dev).double()
s = torch.randn(1, 2, device=dev, dtype=torch.double, 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_anomaly_detect_nan(self):
size = 10
class MyFunc(Function):
@staticmethod
def forward(ctx, inp1, inp2, fail_0th):
ctx.fail_0th = fail_0th
return inp1.sum(0, keepdim=True)
@staticmethod
def backward(ctx, gO):
gI = gO.clone().expand(size)
gI[0] = 0
gI[0] /= 0 # Generate a nan
if ctx.fail_0th:
return gI, None, None
else:
return None, gI, None
inp = torch.rand(size, requires_grad=True)
out = MyFunc.apply(inp, inp, True)
out.backward() # Should not fail
inp = torch.rand(size, requires_grad=True)
out = MyFunc.apply(inp, inp, True)
with self.assertRaisesRegex(RuntimeError, "Function 'MyFuncBackward' returned nan values in its 0th output."):
with warnings.catch_warnings(record=True) as w:
with detect_anomaly():
out.backward()
self.assertIn('No forward pass information', str(w[0].message))
inp = torch.rand(size, requires_grad=True)
with self.assertRaisesRegex(RuntimeError, "Function 'MyFuncBackward' returned nan values in its 1th output."):
with warnings.catch_warnings(record=True) as w:
with detect_anomaly():
out = MyFunc.apply(inp, inp, False)
out.backward()
self.assertIn('MyFunc.apply', str(w[0].message))
def test_symeig_no_eigenvectors(self):
A = torch.tensor([[1., 2.], [2., 4.]], dtype=torch.float32, requires_grad=True)
w, v = torch.symeig(A, eigenvectors=False)
with self.assertRaisesRegex(RuntimeError, 'cannot compute backward'):
torch.autograd.backward([w, v], [torch.ones_like(w), torch.ones_like(v)])
@skipIfRocm
def test_svd_no_singularvectors(self):
A = torch.randn(2, 2, dtype=torch.float32, requires_grad=True)
u, s, v = torch.svd(A, compute_uv=False)
with self.assertRaisesRegex(RuntimeError, 'cannot compute backward'):
torch.autograd.backward([u, s, v], [torch.ones_like(u), torch.ones_like(s), torch.ones_like(v)])
def test_no_grad_copy(self):
# create autograd function that saves grad pointer as class static
class MyFunc(Function):
static_grad_ptr = None
@staticmethod
def forward(ctx, inp1, inp2):
return inp1 + inp2
@staticmethod
def backward(ctx, grad):
MyFunc.static_grad_ptr = grad.data_ptr()
return grad, grad
class NonContGradFunc(Function):
@staticmethod
def forward(ctx, inp1):
ctx.size = inp1.size()
return torch.tensor([1.])
@staticmethod
def backward(ctx, grad):
return torch.ones(1).expand(ctx.size)
a = torch.randn(5, 6, requires_grad=True)
b = torch.randn(5, 6, requires_grad=True)
# non-contiguous grad should be copied
NonContGradFunc.apply(MyFunc.apply(a, b)).backward()
self.assertFalse(a.grad.data_ptr() == MyFunc.static_grad_ptr)
self.assertFalse(b.grad.data_ptr() == MyFunc.static_grad_ptr)
# test case that should trigger no copy for one of a,b
a.grad = b.grad = None
MyFunc.apply(a, b)[1][0].backward()
p_g = MyFunc.static_grad_ptr
p_a = a.grad.data_ptr()
p_b = b.grad.data_ptr()
# check a,b uses different grad buffer
self.assertFalse(p_a == p_b)
# check one of them is using the computed buffer
self.assertTrue(p_a == p_g or p_b == p_g)
def index_variable(shape, max_indices):
if not isinstance(shape, tuple):
shape = (shape,)
index = torch.rand(*shape).mul_(max_indices).floor_().long()
return index
def index_perm_variable(shape, max_indices):
if not isinstance(shape, tuple):
shape = (shape,)
index = torch.randperm(max_indices).narrow(0, 0, reduce(mul, shape)).view(shape)
return index
def gather_variable(shape, index_dim, max_indices, duplicate=False):
assert len(shape) == 2
assert index_dim < 2
batch_dim = 1 - index_dim
index = torch.LongTensor(*shape)
for i in range(shape[index_dim]):
index.select(index_dim, i).copy_(
torch.randperm(max_indices)[:shape[batch_dim]])
if duplicate:
index.select(batch_dim, 0).copy_(index.select(batch_dim, 1))
return index
def bernoulli_scalar():
return torch.tensor(0, dtype=torch.uint8).bernoulli_()
def gradgradcheck_method_precision_override(test_name):
# these are just empirical observations, we should improve
gradgradcheck_precision_override = {
'test_norm': {'atol': 2e-2, 'rtol': 1e-2},
'test_norm_1_5': {'atol': 1.5e-2, 'rtol': 1e-2},
'test_norm_3': {'atol': 5e-2, 'rtol': 1e-2},
'test_dist': {'atol': 5e-2, 'rtol': 1e-2},
'test_dist_4': {'atol': 8e-2, 'rtol': 1e-2},
}
non_broadcasted_test_name = test_name.split("_broadcast")[0]
override = gradgradcheck_precision_override.get(non_broadcasted_test_name)
if override:
if 'broadcast_lhs' in test_name or 'broadcast_rhs' in test_name:
# errors accumulated across 1 dimension
override = {'atol': override['atol'] * S, 'rtol': override['atol'] * S}
elif 'broadcast_all' in test_name:
# errors accumulated across multiple dimensions
override = {'atol': override['atol'] * S * S, 'rtol': override['atol'] * S * S}
return override
def run_grad_and_gradgrad_checks(test_case, name, test_name, apply_method, output_variable,
input_variables, run_gradgradcheck=True):
test_case.assertTrue(gradcheck(apply_method, input_variables, eps=1e-6, atol=PRECISION))
if name in EXCLUDE_GRADGRADCHECK or test_name in EXCLUDE_GRADGRADCHECK_BY_TEST_NAME:
return
gradgradcheck_precision_override = gradgradcheck_method_precision_override(test_name)
if gradgradcheck_precision_override is not None:
atol = gradgradcheck_precision_override['atol']
rtol = gradgradcheck_precision_override['rtol']
test_case.assertTrue(gradgradcheck(apply_method, input_variables, None, atol=atol, rtol=rtol,
gen_non_contig_grad_outputs=True))
else:
test_case.assertTrue(gradgradcheck(apply_method, input_variables, gen_non_contig_grad_outputs=True))
def run_functional_checks(test_case, test_name, name, apply_fn, run_grad_checks,
f_args_variable, f_args_tensor):
output_variable = apply_fn(*f_args_variable)
if run_grad_checks:
run_grad_and_gradgrad_checks(test_case, name, test_name, apply_fn,
output_variable, f_args_variable)
self_variable = f_args_variable[0]
if isinstance(output_variable, torch.Tensor) and output_variable.requires_grad and self_variable is not None:
output_variable.backward(randn_like(output_variable))
test_case.assertEqual(self_variable.type(), self_variable.grad.type())
test_case.assertEqual(self_variable.size(), self_variable.grad.size())
def add_test(
name,
self_size,
args,
variant_name='',
dim_args_idx=(),
skipTestIf=(),
output_process_fn=lambda x: x,
kwargs=None):
kwargs = kwargs if kwargs else {}
basic_test_name = 'test_' + name
if variant_name != '':
basic_test_name += '_' + variant_name
for dim_perm in product([-1, 1], repeat=len(dim_args_idx)):
test_name = basic_test_name
new_args = [arg * dim_perm[dim_args_idx.index(i)] if i in dim_args_idx else arg for i, arg in enumerate(args)]
test_name = basic_test_name + ''.join('_neg' + str(i) for i, idx in enumerate(dim_perm) if idx < 0)
new_args = tuple(new_args)
# for-loop bodies don't define scopes, so we have to save the variables
# we want to close over in some way
def do_test(self, name=name, self_size=self_size, args=new_args, test_name=test_name,
output_process_fn=output_process_fn):
def check(name):
is_magic_method = name[:2] == '__' and name[-2:] == '__'
is_inplace = name[-1] == "_" and not is_magic_method
self_variable = create_input((self_size,))[0][0]
# FixMe: run grad checks on inplace self
if is_inplace:
self_variable.requires_grad = False
# need to record this because methods can change the szie (e.g. unsqueeze)
args_variable, kwargs_variable = create_input(args, requires_grad=not is_inplace, call_kwargs=kwargs)
self_tensor = deepcopy(self_variable.data)
args_tensor = deepcopy(unpack_variables(args_variable))
output_variable = getattr(self_variable, name)(*args_variable, **kwargs_variable)
if not exclude_tensor_method(name, test_name):
output_tensor = getattr(self_tensor, name)(*args_tensor, **kwargs_variable)
if not isinstance(output_tensor, torch.Tensor) and not isinstance(output_tensor, tuple):
output_tensor = torch.DoubleTensor((output_tensor,))
self.assertEqual(unpack_variables(output_variable), output_tensor)
# TODO: check that both have changed after adding all inplace ops
def fn(*inputs):
output = getattr(inputs[0], name)(*inputs[1:], **kwargs)
return output_process_fn(output)
if not is_inplace and name not in EXCLUDE_GRADCHECK:
run_grad_and_gradgrad_checks(self, name, test_name, fn,
output_variable, (self_variable,) + args_variable)
# functional interface tests
if hasattr(torch, name) and name not in EXCLUDE_FUNCTIONAL:
def fn(*inputs):
output = getattr(torch, name)(*inputs)
return output_process_fn(output)
f_args_variable = (self_variable,) + args_variable
f_args_tensor = (self_tensor,) + args_tensor
# could run the gradchecks again, but skip since we did it for the methods above.
run_functional_checks(self, test_name, name, fn,
False, f_args_variable, f_args_tensor)
# check for correct type of input.data and input.grad.data
if not is_inplace:
self_variable = create_input((self_size,), requires_grad=True)[0][0]
args_variable, kwargs_variable = create_input(args, requires_grad=False, call_kwargs=kwargs)
output_variable = getattr(self_variable, name)(*args_variable, **kwargs_variable)
if isinstance(output_variable, torch.autograd.Variable):
output_variable.backward(randn_like(output_variable))
self.assertTrue(type(self_variable.data) == type(self_variable.grad.data))
self.assertTrue(self_variable.size() == self_variable.grad.size())
# compare grads to inplace grads
inplace_name = name + '_'
# can't broadcast inplace to left hand side
skip_inplace = ('broadcast_lhs' in test_name or
'broadcast_all' in test_name)
if hasattr(torch.ones(1), inplace_name) and not skip_inplace:
output_variable = getattr(self_variable, name)(*args_variable, **kwargs_variable)
if not isinstance(output_variable, tuple):
output_variable = (output_variable,)
inplace_self_variable = deepcopy(self_variable)
inplace_self_variable_copy = tuple(i.clone() if isinstance(i, torch.Tensor) else i
for i in (inplace_self_variable,))
inplace_args_variable = deepcopy(args_variable)
inplace_args_variable_copy = tuple(i.clone() if isinstance(i, torch.Tensor) else i
for i in inplace_args_variable)
inplace_output_variable = (
getattr(inplace_self_variable_copy[0], inplace_name)(*inplace_args_variable_copy,
**kwargs_variable))
if not isinstance(inplace_output_variable, tuple):
inplace_output_variable = (inplace_output_variable,)
self.assertEqual(inplace_output_variable, output_variable)
# Check that gradient is the same
for inp_i, i in zip((inplace_self_variable,) + inplace_args_variable,
(self_variable,) + args_variable):
if not isinstance(inp_i, torch.Tensor):
assert not isinstance(i, torch.Tensor)
continue
if inp_i.grad is not None:
inp_i.grad.data.zero_()
if i.grad is not None:
i.grad.data.zero_()
for io, o in zip(inplace_output_variable, output_variable):
grad = randn_like(io).double()
io.backward(grad)
o.backward(grad)
for inp_i, i in zip((inplace_self_variable,) + inplace_args_variable,
(self_variable,) + args_variable):
if not isinstance(inp_i, torch.Tensor):
continue
self.assertEqual(inp_i.grad, i.grad)
check(name)
inplace_name = name + '_'
# can't broadcast inplace to left hand side
broadcast_skip_inplace = 'broadcast_lhs' in test_name or 'broadcast_all' in test_name
if hasattr(torch.ones(1), inplace_name) and not broadcast_skip_inplace:
check(inplace_name)
assert not hasattr(TestAutograd, test_name), 'Two tests have the same name: ' + test_name
for skip in skipTestIf:
do_test = skip(do_test)
setattr(TestAutograd, test_name, do_test)
for test in method_tests:
add_test(*test)
if __name__ == '__main__':
run_tests()