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android / platform / external / pytorch / fca03eeec128b0bc8c7dd543ea9726fb5864f512 / . / test / test_autograd.py
blob: 830a2d3ff97b8c1a0eae77a70dfb6d18d4d63228 [file] [log] [blame]
# Owner(s): ["module: autograd"]
import contextlib
import gc
import io
import math
import os
import random
import sys
import tempfile
import threading
import time
import unittest
import uuid
import warnings
import operator
import subprocess
from copy import deepcopy
from collections import OrderedDict
from itertools import product
from operator import mul
from functools import reduce, partial
import torch
from torch import nn
from torch._six import inf, nan
from torch.autograd.function import once_differentiable
from torch.autograd.profiler import (profile, record_function, emit_nvtx)
from torch.autograd.profiler_util import (_format_time, EventList, FunctionEvent, FunctionEventAvg)
from torch.utils.checkpoint import checkpoint
from torch.testing import make_tensor
from torch.testing._internal.common_cuda import TEST_CUDA
from torch.testing._internal.common_utils import (
TestCase, run_tests, skipIfNoLapack, slowTest, IS_WINDOWS, IS_MACOS,
disable_gc, gradcheck, gradgradcheck, parametrize,
instantiate_parametrized_tests, skipIfMps)
from torch.autograd import Variable, Function, detect_anomaly, kineto_available, _calculate_shape
from torch.autograd.function import InplaceFunction
import torch.autograd.forward_ad as fwAD
from torch.testing._internal.common_methods_invocations import mask_not_all_zeros
from torch.testing._internal.common_device_type import (instantiate_device_type_tests, skipCUDAIfRocm,
onlyCPU, onlyCUDA, dtypes, dtypesIfCUDA,
deviceCountAtLeast, skipMeta, dtypesIfMPS)
from torch.testing._internal.common_dtype import floating_types_and
from torch.utils._mode_utils import no_dispatch
import pickle
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 test_tensor_grad_warnings(self):
dummy = torch.empty(1)
with warnings.catch_warnings(record=True) as w:
# Accessing .grad on leaf
dummy.requires_grad_()
foo = dummy.grad
self.assertEqual(len(w), 0)
# Accessing .grad on non-leaf
dummy = dummy.clone()
foo = dummy.grad
self.assertEqual(len(w), 1)
# Accessing .grad on non-leaf that retains gradients
dummy.retain_grad()
foo = dummy.grad
self.assertEqual(len(w), 1)
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, y + torch.ones(5, 5))
self.assertEqual(y.grad, x + 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),
'CopyBackwards(None, Error(AccumulateGrad(), None, AccumulateGrad()))')
self.assertEqual(graph_desc(y.grad.grad_fn),
'CopyBackwards(None, 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.))
with torch.no_grad():
v.grad.zero_()
MyFunction.apply(v.clone()).backward()
self.assertEqual(v.grad, torch.full(shape, 2.))
def test_function_returns_undefined_tensor(self):
class MyFunction(Function):
@staticmethod
def forward(ctx, x):
return x * 2
@staticmethod
def backward(ctx, grad):
return None
# Test that undefined tensors returned from custom backward function
# are propagated as undefined and not tensor full of zeroes
x = torch.ones(1, requires_grad=True)
MyFunction.apply(x).backward()
self.assertIsNone(x.grad)
MyFunction.apply(x ** 2).backward()
self.assertIsNone(x.grad)
MyFunction.apply(x).sum().backward()
self.assertIsNone(x.grad)
self.assertIsNone(torch.autograd.grad(MyFunction.apply(x), x, allow_unused=True)[0])
def test_materialize_grads(self):
class MyFunction(Function):
@staticmethod
def forward(ctx, x):
return x
@staticmethod
def backward(ctx, grad):
self.assertEqual(grad, torch.zeros(1))
return grad
x = torch.ones(1, requires_grad=True)
torch._C._functions.UndefinedGrad()(MyFunction.apply(x)).backward()
def test_dont_materialize_grads(self):
class MyFunction(Function):
@staticmethod
def forward(ctx, x):
ctx.set_materialize_grads(False)
return x
@staticmethod
def backward(ctx, grad):
self.assertIsNone(grad)
return grad
x = torch.ones(1, requires_grad=True)
torch._C._functions.UndefinedGrad()(MyFunction.apply(x)).backward()
def test_legacy_function_deprecation_exception(self):
# Trigger exception
class MyFunction(Function):
def forward(self, x):
return x
def backward(self, grad_output):
return grad_output
# Check exception occurs
with self.assertRaisesRegex(
RuntimeError,
'Legacy autograd function with non-static forward method is deprecated'):
MyFunction()(torch.randn(3, 4))
class SimulateBackwardError(Function):
@staticmethod
def forward(ctx, input):
return input.clone()
@staticmethod
@once_differentiable
def backward(ctx, input):
raise Exception("Simulate error on backward pass")
def test_custom_function_exception(self):
t1 = torch.rand((3, 3), requires_grad=True)
t2 = torch.rand((3, 3), requires_grad=True)
tmp = (t1 + t2) * (t1 + t2)
t3 = TestAutograd.SimulateBackwardError.apply(tmp)
with self.assertRaisesRegex(Exception, "Simulate error on backward pass"):
t3.sum().backward()
def test_custom_function_non_tensor_inputs_outputs(self):
class MyFunction(Function):
@staticmethod
def forward(ctx, t1, t2, scale, t3):
t4 = t1 + t2 * t3
t5 = t1 * t2 + t3
t4 *= scale
t5 *= scale
# Save scale
ctx.scale = scale
ctx.save_for_backward(t1, t2, t3)
return scale, t4, None, True, t5, "bar", t1
@staticmethod
@once_differentiable
def backward(ctx, *grads):
# Verify grads
self.assertEqual(7, len(grads))
self.assertIsNone(grads[0])
self.assertIsNone(grads[2])
self.assertIsNone(grads[3])
self.assertIsNone(grads[5])
scale = ctx.scale
var1, var2, var3 = ctx.saved_tensors
return (
grads[1] * scale + grads[4] * var2 * scale + grads[6],
grads[1] * var3 * scale + grads[4] * var1 * scale,
None,
grads[1] * var2 * scale + grads[4] * scale,
)
t1 = torch.rand(10, dtype=torch.double, requires_grad=True)
t2 = torch.rand(10, dtype=torch.double, requires_grad=True)
t3 = torch.rand(10, dtype=torch.double)
scale = random.randint(0, 10)
res = MyFunction.apply(t1, t2, scale, t3)
self.assertEqual(scale, res[0])
self.assertEqual((t1 + t2 * t3) * scale, res[1])
self.assertEqual(None, res[2])
self.assertEqual(True, res[3])
self.assertEqual((t1 * t2 + t3) * scale, res[4])
self.assertEqual("bar", res[5])
self.assertEqual(t1, res[6])
# Validate running backward.
torch.autograd.backward([res[1].sum(), res[4].sum(), res[6].sum()])
self.assertIsNotNone(t1.grad)
self.assertIsNotNone(t2.grad)
self.assertIsNone(t3.grad)
# Test gradcheck
def foo(t1, t2, t3):
res = MyFunction.apply(t1, t2, scale, t3)
return res[1], res[4], res[6]
gradcheck(foo, (t1, t2, t3))
def test_custom_function_no_tensors(self):
class MyFunction(Function):
@staticmethod
def forward(ctx, t1, t2, scale, t3):
t4 = t1 + t2 * t3
t5 = t1 * t2 + t3
t4 *= scale
t5 *= scale
return scale, t4, None, True, t5, "bar", t1
@staticmethod
@once_differentiable
def backward(ctx, *args):
return (args[0], args[1], None, args[2])
t1 = random.random()
t2 = random.random()
t3 = random.random()
scale = random.randint(0, 10)
res = MyFunction.apply(t1, t2, scale, t3)
self.assertEqual(scale, res[0])
self.assertEqual((t1 + t2 * t3) * scale, res[1])
self.assertEqual(None, res[2])
self.assertEqual(True, res[3])
self.assertEqual((t1 * t2 + t3) * scale, res[4])
self.assertEqual("bar", res[5])
self.assertEqual(t1, res[6])
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()
def test_unrelated_inputs(self):
# test to ensure grad(grad)check runs successfully even if there is an
# unrelated (but differentiable) inputs
def my_function(x, y):
return x * x
x = torch.rand(10, dtype=torch.double, requires_grad=True)
y = torch.rand(10, dtype=torch.double, requires_grad=True)
gradcheck(my_function, (x, y))
gradgradcheck(my_function, (x, y))
def test_not_implemented_grad(self):
a = torch.rand(2, requires_grad=True)
# if grad for nextafter ends up being implemented, this should be changed
y = torch.nextafter(a, a).sum()
with self.assertRaisesRegex(
NotImplementedError,
'the derivative for .* is not implemented'):
y.backward()
def test_not_implemented_fwad(self):
x = torch.randn(3)
v = torch.rand(3)
with fwAD.dual_level():
dual_x = fwAD.make_dual(x, v)
err_msg = r"Trying to use forward AD with .* that does not support it"
hint_msg = "Running forward AD for an OP that does not implement it should raise a NotImplementedError"
with self.assertRaisesRegex(NotImplementedError, err_msg, msg=hint_msg):
# if forward AD ends up being implemented for torch.igamma, choose a different op
torch.igamma(dual_x, dual_x)
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_accumulate_grad_tensor_reference(self):
def _test_grad_tensor(params_grad_tensor, backward_grad_tensor, should_preserve_reference, create_graph):
params = torch.tensor([1.5, 1.5]).requires_grad_()
params.grad = params_grad_tensor
grad_saved = params.grad
params.backward(backward_grad_tensor, create_graph=create_graph)
self.assertEqual(id(grad_saved) == id(params.grad), should_preserve_reference)
for create_graph in (False, True):
# Accumulate dense gradient to sparse gradient will change the `params.grad` reference
_test_grad_tensor(
torch.sparse_coo_tensor(torch.tensor([[1, 1]]).long(), torch.tensor([1., 1.])),
torch.tensor([1.5, 1.5]),
False, # never accumulates in-place
create_graph)
# Accumulate dense gradient to dense gradient will preserve the `params.grad` reference,
# but only if create_graph=False.
_test_grad_tensor(
torch.tensor([1.5, 1.5]),
torch.tensor([1.5, 1.5]),
not create_graph,
create_graph)
# Accumulate sparse gradient to sparse gradient will preserve the `params.grad` reference,
# but only if create_graph=False.
_test_grad_tensor(
torch.sparse_coo_tensor(torch.tensor([[1, 1]]).long(), torch.tensor([1., 1.])),
torch.sparse_coo_tensor(torch.tensor([[1, 1]]).long(), torch.tensor([1., 1.])),
not create_graph,
create_graph)
def test_accumulate_grad_with_zero_numel_grad(self):
a = torch.rand(4, 0, requires_grad=True)
b = torch.rand(4, 1, requires_grad=True)
c = a + b
assert c.shape == (4, 0)
c.sum().backward()
self.assertEqual(b.grad, torch.zeros(4, 1))
self.assertEqual(a.grad, torch.zeros(4, 0))
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)
with torch.no_grad():
x_grad = 2 * x + y
y_grad = x + 2 * y
self.assertEqual(x.grad, x_grad)
self.assertEqual(y.grad, 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, x_grad + x_hv)
self.assertEqual(y.grad, 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 + y
y_grad = x + 2 * y
self.assertEqual(x.grad, x_grad)
self.assertEqual(y.grad, 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], expected_x_hv)
self.assertEqual(x.grad, x_grad)
self.assertEqual(y.grad, y_grad)
# Test that grad_outputs and outputs have the same shape
grad_out = torch.ones(2)
try:
torch.autograd.grad(
outputs=[grad_sum], grad_outputs=[grad_out],
inputs=[x], create_graph=True)
self.assertFail()
except RuntimeError as error:
self.assertEqual(str(error), "Mismatch in shape: grad_output[0] has a shape of "
+ str(grad_out.shape) + " and output[0] has a shape of "
+ str(grad_sum.shape) + ".")
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 _ in range(5):
grad_x, = torch.autograd.grad(
fn(x), x, grad_outputs=grad_output, create_graph=True)
grad_x_expected = 2 * x + y
self.assertIsNone(y.grad)
self.assertIsNone(x.grad)
self.assertEqual(grad_x, grad_x_expected)
x = x + 0.05 * grad_x
val_init = fn(x_init).sum()
val_final = fn(x).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, go)
self.assertEqual(grad_b, 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_hook_with_no_name(self):
# Create a hook that do not have a __name__ attribute
class MyHookClass:
def __call__(self, grad):
return grad.clone()
x = torch.randn(5, requires_grad=True).clone()
x.register_hook(MyHookClass())
x.sum().backward()
# Should run fine
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, i * i * (1 + l))
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_empty_inputs(self):
x = torch.tensor([1.0], requires_grad=True)
with self.assertRaisesRegex(ValueError, "grad requires non-empty inputs."):
torch.autograd.grad(2 * x, [], grad_outputs=torch.tensor([1.0]))
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)
# allow_unused=False, but grads contains None inside, should throw
with self.assertRaisesRegex(RuntimeError,
"Set allow_unused=True"):
grad_x, grad_y = torch.autograd.grad(x * 2, [x, y], allow_unused=False)
def test_grad_unreachable_discovery(self):
# Test that certain nodes are not erroneously executed when an input
# is unreachable. See #39784
class MyFunc(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
return x
@staticmethod
def backward(ctx, x):
self.fail("This node should not be executed!")
x = MyFunc.apply(torch.randn(1, requires_grad=True) * 2)
y = torch.randn(1, requires_grad=True)
(gY,) = torch.autograd.grad(x, (y, ), allow_unused=True)
self.assertIsNone(gY)
x = MyFunc.apply(torch.randn(1, requires_grad=True) * 2)
y = torch.randn(1, requires_grad=True)
z = torch.randn(1, requires_grad=True)
(gY, gZ) = torch.autograd.grad(x + z, (y, z), allow_unused=True)
self.assertIsNone(gY)
self.assertIsNotNone(gZ)
x = MyFunc.apply(torch.randn(1, requires_grad=True) * 2)
y = torch.randn(1, requires_grad=True)
torch.autograd.backward(x, inputs=(y, )) # allow_unused is implicitly True!
self.assertIsNone(y.grad)
def test_grad_batched_grad(self):
x = torch.randn(2, 2, requires_grad=True)
out = x.clone() # Size([2, 2])
batched_grad = torch.arange(3).expand(2, 2, 3).transpose(0, 2) # Size([3, 2, 2])
grad, = torch.autograd.grad(out, (x,), (batched_grad,), is_grads_batched=True)
self.assertEqual(grad, torch.arange(3).expand(2, 2, 3).transpose(0, 2).to(dtype=grad.dtype))
# Detect shape mismatch
grad_out = torch.ones(2, 2)
with self.assertRaisesRegex(RuntimeError, "If `is_grads_batched=True`, we interpret the first"):
torch.autograd.grad(outputs=out, grad_outputs=(grad_out,), inputs=(x,), is_grads_batched=True)
# Scalar outputs
out = x.sum() # Size([])
batched_grad = torch.arange(3) # Size([3])
grad, = torch.autograd.grad(out, (x,), (batched_grad,), is_grads_batched=True)
self.assertEqual(grad, torch.arange(3).expand(2, 2, 3).transpose(0, 2).to(dtype=grad.dtype))
# We consider scalar and sized-1 to be a mismatch. This is consistent with current non-batched behavior.
grad_out = torch.ones(2).unsqueeze(1)
with self.assertRaisesRegex(RuntimeError, "If `is_grads_batched=True`, we interpret the first"):
torch.autograd.grad(outputs=out, grad_outputs=(grad_out,), inputs=(x,), is_grads_batched=True)
def test_hooks(self):
x = torch.ones(5, 5, requires_grad=True)
y = torch.ones(5, 5) * 4
y.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)
with torch.no_grad():
y.grad.zero_()
z.backward(torch.ones(5, 5), retain_graph=True)
self.assertEqual(y.grad, (x + 1) * 2)
y.register_hook(bw_hook_modify)
with torch.no_grad():
y.grad.zero_()
z.backward(torch.ones(5, 5))
self.assertEqual(y.grad, (x + 1) * 4)
def test_hooks_cpp(self):
# Tests hooks for autograd function implemented in C++
bn = torch.nn.BatchNorm1d(5, affine=False)
bn.double()
bn.eval()
counter = [0]
def bw_hook(grad):
counter[0] += 1
return grad * 2
x = torch.ones(5, 5, dtype=torch.double, requires_grad=True)
z = bn(x)
z.register_hook(bw_hook)
z.sum().backward()
self.assertEqual(counter[0], 1, msg='bw_hook not called')
self.assertEqual(x.grad, torch.ones(5, 5, dtype=torch.double) * 2, atol=1e-5, rtol=0)
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):
@staticmethod
def forward(ctx, x, y):
assert ctx.needs_input_grad[0]
assert not ctx.needs_input_grad[1]
return x, y
@staticmethod
def backward(ctx, grad_x, grad_y):
return grad_x, None
was_called = [False]
def hook(grad):
self.assertIsNotNone(grad)
was_called[0] = True
x = torch.randn(5, 5, requires_grad=True)
y = torch.randn(5, 5)
rx, ry = NoneGradientFunction.apply(x, y)
rx.register_hook(hook)
ry.register_hook(hook)
sum(rx, ry).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 * 2, h1.grad)
out.backward(retain_graph=True)
self.assertEqual(h1 * 4, h1.grad)
with torch.no_grad():
input.grad.zero_()
# It should be a no-op for leaves
input.retain_grad()
input.retain_grad()
out.backward()
self.assertEqual(input * 18, input.grad)
# NB: See test/cpp/api/autograd.cpp for more tests on the interaction between
# retains_grad and hooks in cpp. There's no point testing in python because
# Python hooks use a completely different mechanism.
def test_retain_grad_inplace(self):
a = torch.tensor([1.], requires_grad=True).clone()
a.retain_grad()
a.mul_(2)
a.sum().backward()
self.assertEqual(a.grad, torch.tensor([1.]))
a = torch.tensor([1.], requires_grad=True).clone()
a.retain_grad()
# Inplace multiple times is OK, the real test here would be in cpp though
# because the index here is always zero, having cpp hooks in addition,
# will force us to properly update the index
a.mul_(2)
a.mul_(2)
a.sum().backward()
self.assertEqual(a.grad, torch.tensor([1.]))
def test_retain_grad_inplace_over_view(self):
base = torch.tensor([1.], requires_grad=True).clone()
view = base[:]
view2 = base[:]
view.retain_grad()
view2.retain_grad()
view.mul_(2)
(view + view2).sum().backward()
# The old grad_fn, slice, wouldn't be part of the graph during backward
# so if the retains grad were not properly updated to the new grad_fn,
# the grad would still be None
self.assertEqual(view.grad, view2.grad)
self.assertEqual(view.grad, torch.tensor([1.]))
def test_retain_grad_cycle(self):
x = torch.ones(5, 5, requires_grad=True)
def run_test():
y = x * 2
y.retain_grad()
return y / 2, torch._C._WeakTensorRef(y)
z, ref = run_test()
self.assertTrue(ref.expired())
z.sum().backward()
def test_backward(self):
v = torch.randn(5, 5, requires_grad=True)
x = torch.randn(5, 5, requires_grad=True)
y = (torch.rand(5, 5) + 0.1).requires_grad_(True)
z = torch.randn(5, 5, requires_grad=True)
grad_output = torch.randn(5, 5)
v.backward(grad_output)
self.assertEqual(v.grad, grad_output)
a = x + (y * z) + 4 * z ** 2 * x / y
a.backward(grad_output)
x_grad = 4 * z.pow(2) / y + 1
y_grad = z - 4 * x * z.pow(2) / y.pow(2)
z_grad = 8 * x * z / y + y
self.assertEqual(x.grad, x_grad * grad_output)
self.assertEqual(y.grad, y_grad * grad_output)
self.assertEqual(z.grad, z_grad * grad_output)
def test_sparse_mm_backward(self):
size = (3, 3)
sparse = torch.sparse_coo_tensor(size, requires_grad=True)
dense = torch.randn(size, requires_grad=True)
with self.assertRaisesRegex(
RuntimeError,
"The backward pass for this operation requires the 'mat1' tensor to be strided,"):
z = dense.addmm(sparse, dense)
mm_test_cases = [
# a requires grad, a is sparse, b requires grad, b is sparse, error message
(False, True, True, False, None),
(False, False, True, True, "The backward pass for this operation requires the 'mat2'"),
(False, True, True, True, "The backward pass for this operation requires the 'mat2'"),
(True, False, True, True, "The backward pass for this operation requires the 'mat2'"),
(True, True, False, False, "The backward pass for this operation requires the 'self'"),
(True, True, True, False, "The backward pass for this operation requires the 'self'"),
(True, True, True, True, "The backward pass for this operation requires the 'mat2'"),
]
for a_req_grad, a_is_sparse, b_req_grad, b_is_sparse, err_msg in mm_test_cases:
# We should only be testing cases with sparse inputs, and at least one
# input needs to require grad so we can call a backward pass
assert a_is_sparse or b_is_sparse
assert a_req_grad or b_req_grad
a = torch.randn(size, requires_grad=a_req_grad)
if a_is_sparse:
a = a.to_sparse()
b = torch.randn(size, requires_grad=b_req_grad)
if b_is_sparse:
b = b.to_sparse()
# If no error expected, check that sparse and dense cases match
if err_msg is None:
r = a.mm(b)
r.sum().backward()
a_grad = None if a.grad is None else a.grad.clone().detach()
b_grad = None if b.grad is None else b.grad.clone().detach()
# Redo with only dense tensors
a = (a.to_dense() if a.is_sparse else a).clone().detach()
a.requires_grad = a_req_grad
b = (b.to_dense() if b.is_sparse else b).clone().detach()
b.requires_grad = b_req_grad
r = a.mm(b)
r.sum().backward()
self.assertEqual(a_grad, a.grad)
self.assertEqual(b_grad, b.grad)
else:
with self.assertRaisesRegex(RuntimeError, err_msg):
a.mm(b)
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, grad_z)
self.assertEqual(y.grad, grad_z)
self.assertEqual(a.grad, grad_c * b)
self.assertEqual(b.grad, grad_c * a)
self.assertEqual(q.grad, (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_backward_with_inputs(self):
x = torch.randn(2, 2, dtype=torch.double, requires_grad=True)
y = torch.randn(2, 2, dtype=torch.double, requires_grad=True)
def fn():
return x ** 2 + y * x + y ** 2
gradient = torch.ones(2, 2)
x_grad_expected = 2 * x + y
y_grad_expected = x + 2 * y
@torch.no_grad()
def reset_grad():
x.grad.zero_()
y.grad.zero_()
torch.autograd.backward(fn(), gradient, inputs=[x, y])
self.assertEqual(x.grad, x_grad_expected)
self.assertEqual(y.grad, y_grad_expected)
reset_grad()
torch.autograd.backward(fn(), gradient, inputs=[x])
self.assertEqual(x.grad, x_grad_expected)
self.assertEqual(y.grad, torch.zeros(2, 2), exact_dtype=False)
reset_grad()
torch.autograd.backward(fn(), gradient, inputs=[y])
self.assertEqual(y.grad, y_grad_expected)
self.assertEqual(x.grad, torch.zeros(2, 2), exact_dtype=False)
reset_grad()
torch.autograd.backward(fn(), gradient, inputs=y)
self.assertEqual(y.grad, y_grad_expected)
self.assertEqual(x.grad, torch.zeros(2, 2), exact_dtype=False)
reset_grad()
self.assertRaisesRegex(RuntimeError, 'cannot be empty',
lambda: torch.autograd.backward(fn(), gradient, inputs=[]))
def test_backward_with_nonleaf_inputs(self):
x = torch.randn(2, 2, dtype=torch.double, requires_grad=True)
x_nonleaf = x * 1
y = torch.randn(2, 2, dtype=torch.double, requires_grad=True)
z = torch.randn(2, 2, dtype=torch.double, requires_grad=True)
out = x_nonleaf ** 2 + y * x_nonleaf + y ** 2
out.backward(torch.ones(2, 2, dtype=torch.double), create_graph=True, inputs=[x, y, x_nonleaf])
x_grad_expected = 2 * x + y
y_grad_expected = x + 2 * y
x_non_leaf_expected = 2 * x_nonleaf + y
self.assertEqual(y.grad, y_grad_expected)
self.assertEqual(x.grad, x_grad_expected)
self.assertEqual(x_nonleaf.grad, x_non_leaf_expected)
# backward doesn't have an allow_unused flag, so the behavior of backward
# when variable is not part of the graph is as if allow_used were true
# x.grad will simply be None.
out.backward(torch.ones(2, 2, dtype=torch.double), create_graph=True, inputs=[z])
self.assertIsNone(z.grad)
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
self.assertEqual(x.grad, 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 _ 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 _ 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 _ 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_unnecessary_save(self):
# If we kept x in the derivative Function of x * 2 we would
# get an error in the backward that would complain that we've
# modified x, which was needed for gradient computation.
# Since we should elide unnecessary saves, this test should pass.
mu = torch.ones(1, requires_grad=True)
x = torch.empty(1)
loss = 0
for i in range(3):
x.detach_()
x.copy_(mu + i)
ft = torch.tensor([float(i)])
multiplied = x * ft
s = multiplied.sum()
loss += s
loss.backward()
def test_no_grad(self):
x = torch.ones(5, 5, requires_grad=True)
y = 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_set_grad_generator_functions(self):
@torch.no_grad()
def gen_no_grad():
for i in range(10):
self.assertEqual(torch.is_grad_enabled(), False)
yield i
with torch.enable_grad():
for _ in gen_no_grad():
self.assertEqual(torch.is_grad_enabled(), True)
@torch.enable_grad()
def gen_enable_grad():
for i in range(10):
self.assertEqual(torch.is_grad_enabled(), True)
yield i
with torch.no_grad():
for _ in gen_enable_grad():
self.assertEqual(torch.is_grad_enabled(), False)
def test_set_grad_generator_functions_recursive(self):
# enable_grad_decorator_recursive and no_grad_decorator_recursive call each other
# recursively, to ensure that the decorators preserve the caller's setting
@torch.enable_grad()
def enable_grad_decorator_recursive(depth):
self.assertTrue(torch.is_grad_enabled())
if depth > 0:
no_grad_decorator_recursive(depth - 1)
self.assertTrue(torch.is_grad_enabled())
@torch.no_grad()
def no_grad_decorator_recursive(depth):
self.assertFalse(torch.is_grad_enabled())
if depth > 0:
enable_grad_decorator_recursive(depth - 1)
self.assertFalse(torch.is_grad_enabled())
# enable_grad_context_manager_recursive and no_grad_context_manager_recursive call
# each other recursively, to ensure that the decorators preserve the caller's setting
def enable_grad_context_manager_recursive(depth):
with torch.enable_grad():
self.assertTrue(torch.is_grad_enabled())
if depth > 0:
no_grad_context_manager_recursive(depth - 1)
self.assertTrue(torch.is_grad_enabled())
def no_grad_context_manager_recursive(depth):
with torch.no_grad():
self.assertFalse(torch.is_grad_enabled())
if depth > 0:
enable_grad_context_manager_recursive(depth - 1)
self.assertFalse(torch.is_grad_enabled())
with torch.enable_grad():
self.assertTrue(torch.is_grad_enabled())
enable_grad_decorator_recursive(10)
self.assertTrue(torch.is_grad_enabled())
enable_grad_context_manager_recursive(10)
self.assertTrue(torch.is_grad_enabled())
with torch.no_grad():
self.assertFalse(torch.is_grad_enabled())
enable_grad_decorator_recursive(10)
self.assertFalse(torch.is_grad_enabled())
enable_grad_context_manager_recursive(10)
self.assertFalse(torch.is_grad_enabled())
def test_set_grad_coroutines(self):
@torch.no_grad()
def coro_no_grad(n=10):
self.assertFalse(torch.is_grad_enabled())
for i in range(n):
self.assertFalse(torch.is_grad_enabled())
r = yield i
self.assertFalse(torch.is_grad_enabled())
self.assertEqual(i, r)
self.assertFalse(torch.is_grad_enabled())
@torch.enable_grad()
def coro_enable_grad(n=10):
self.assertTrue(torch.is_grad_enabled())
for i in range(n):
self.assertTrue(torch.is_grad_enabled())
r = yield i
self.assertTrue(torch.is_grad_enabled())
self.assertEqual(i, r)
self.assertTrue(torch.is_grad_enabled())
with torch.enable_grad():
self.assertTrue(torch.is_grad_enabled())
coro, r = coro_no_grad(), None
try:
while True:
self.assertTrue(torch.is_grad_enabled())
r = coro.send(r)
self.assertTrue(torch.is_grad_enabled())
except StopIteration:
pass
with torch.no_grad():
self.assertFalse(torch.is_grad_enabled())
coro, r = coro_enable_grad(), None
try:
while True:
self.assertFalse(torch.is_grad_enabled())
r = coro.send(r)
self.assertFalse(torch.is_grad_enabled())
except StopIteration:
pass
def test_set_grad_coroutines_benign_exceptions(self):
class RecoverableException(Exception):
pass
@torch.no_grad()
def coro_no_grad(n=10):
has_raised = False
for i in range(n):
try:
self.assertFalse(torch.is_grad_enabled())
yield (-i if has_raised else i)
except RecoverableException:
self.assertFalse(torch.is_grad_enabled())
has_raised = True
@torch.enable_grad()
def coro_enable_grad(n=10):
has_raised = False
for i in range(n):
try:
self.assertTrue(torch.is_grad_enabled())
yield (-i if has_raised else i)
except RecoverableException:
self.assertTrue(torch.is_grad_enabled())
has_raised = True
with torch.enable_grad():
coro = coro_no_grad()
assert 0 == next(coro)
try:
while True:
r = coro.throw(RecoverableException)
self.assertLess(r, 0)
except StopIteration:
pass
with torch.no_grad():
coro = coro_enable_grad()
assert 0 == next(coro)
try:
while True:
r = coro.throw(RecoverableException)
self.assertLess(r, 0)
except StopIteration:
pass
def test_set_grad_coroutines_critical_exceptions(self):
class UnrecoverableException(Exception):
pass
class SecondaryException(Exception):
pass
@torch.no_grad()
def coro_no_grad(n=10):
has_raised = False
for i in range(n):
try:
self.assertFalse(torch.is_grad_enabled())
yield (-i if has_raised else i)
except UnrecoverableException:
self.assertFalse(torch.is_grad_enabled())
raise SecondaryException
@torch.enable_grad()
def coro_enable_grad(n=10):
has_raised = False
for i in range(n):
try:
self.assertTrue(torch.is_grad_enabled())
yield (-i if has_raised else i)
except UnrecoverableException:
self.assertTrue(torch.is_grad_enabled())
raise SecondaryException
with torch.enable_grad():
coro = coro_no_grad()
assert 0 == next(coro)
with self.assertRaises(SecondaryException):
coro.throw(UnrecoverableException)
with torch.no_grad():
coro = coro_enable_grad()
assert 0 == next(coro)
with self.assertRaises(SecondaryException):
coro.throw(UnrecoverableException)
def test_set_grad_coroutines_exit(self):
@torch.no_grad()
def coro_no_grad(state):
for i in range(10):
try:
self.assertFalse(torch.is_grad_enabled())
yield i
except GeneratorExit:
self.assertFalse(torch.is_grad_enabled())
state.add('GeneratorExit')
raise
@torch.enable_grad()
def coro_enable_grad(state):
for i in range(10):
try:
self.assertTrue(torch.is_grad_enabled())
yield i
except GeneratorExit:
self.assertTrue(torch.is_grad_enabled())
state.add('GeneratorExit')
raise
state = set()
with torch.enable_grad():
coro = coro_no_grad(state)
for i in range(5):
next(coro)
coro.close()
self.assertTrue('GeneratorExit' in state)
state = set()
with torch.no_grad():
coro = coro_enable_grad(state)
for i in range(5):
next(coro)
coro.close()
self.assertTrue('GeneratorExit' in state)
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.empty(x.size()).fill_(0)
expected_grad[idx] = 1
self.assertEqual(y.grad, expected_grad)
def check_index(x, y, idx):
if y.grad is not None:
with torch.no_grad():
y.grad.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().bool())
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:
with torch.no_grad():
y.grad.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, 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, 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, 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.empty(4, 4, 4).zero_()
expected_grad[1].fill_(3)
self.assertEqual(y.grad, expected_grad)
def test_index_backward_does_not_save_tensor(self):
# Example from https://github.com/pytorch/pytorch/issues/24853.
# if `index(tensor, indices)` saves `tensor` for backwards, then it will
# trigger a version check on `tensor` during the backward pass, which
# will cause the following code to error because `tensor` gets modified
# by the indexing line.
a = torch.tensor([1., 0, 0])
b = torch.zeros(3, requires_grad=True)
tensor = b + 0
tensor[a != 0] = tensor[a != 0]
tensor.backward(torch.zeros_like(tensor))
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)
MyFunction.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
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, 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, 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)
def test_attribute_deletion(self):
x = torch.randn((5, 5), requires_grad=True)
del x.grad
self.assertIsNone(x.grad)
with self.assertRaises(RuntimeError):
del x.data
with self.assertRaises(TypeError):
x.data = None
with self.assertRaises(RuntimeError):
del x.requires_grad
with self.assertRaises(RuntimeError):
del x._grad_fn
with self.assertRaises(RuntimeError):
del x._backward_hooks
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, b * grad_output * 2)
self.assertEqual(b.grad, a * 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_backward_twice_with_saved_values(self):
b = torch.randn(3, requires_grad=True, dtype=torch.double)
c = torch.zeros(3, dtype=torch.double)
c[[1, 2]] = b[[1, 1]]
c.backward(torch.tensor([1, 1, 1], dtype=torch.double))
self.assertRaisesRegex(RuntimeError, 'Specify retain_graph=True',
lambda: c.backward(torch.tensor([1, 1, 1], dtype=torch.double)))
def test_backward_twice_retained_graph_with_saved_values(self):
b = torch.randn(3, requires_grad=True, dtype=torch.double)
c = torch.zeros(3, dtype=torch.double)
c[[1, 2]] = b[[1, 1]]
c.backward(torch.tensor([1, 1, 1], dtype=torch.double), retain_graph=True)
c.backward(torch.tensor([1, 1, 1], dtype=torch.double))
def test_backward_twice_without_saved_values(self):
b = torch.randn(3, requires_grad=True, dtype=torch.double)
c = b + 1
c.backward(torch.tensor([1, 1, 1], dtype=torch.double))
c.backward(torch.tensor([1, 1, 1], dtype=torch.double))
def test_backward_twice_retained_graph_without_saved_values(self):
b = torch.randn(3, requires_grad=True, dtype=torch.double)
c = torch.zeros(3, dtype=torch.double)
c[[1, 2]] = b[[1, 1]]
c.backward(torch.tensor([1, 1, 1], dtype=torch.double), retain_graph=True)
c.backward(torch.tensor([1, 1, 1], dtype=torch.double))
def test_backward_create_graph_warns(self):
try:
prev = torch.is_warn_always_enabled()
torch.set_warn_always(True)
b = torch.randn(3, requires_grad=True, dtype=torch.double)
c = b * b
with warnings.catch_warnings(record=True) as ws:
c.backward(torch.ones_like(c), create_graph=True)
b.grad = None
self.assertTrue(any('Using backward() with create_graph=True' in str(w.message) for w in ws))
# Should not warn for grad
with warnings.catch_warnings(record=True) as ws:
torch.autograd.grad(c, b, torch.ones_like(c), create_graph=True)
self.assertFalse(any('Using backward() with create_graph=True' in str(w.message) for w in ws))
finally:
torch.set_warn_always(prev)
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)))
with torch.no_grad():
x.grad.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, torch.ones(5, 5) / 2)
w.backward(torch.ones(5, 5), retain_graph=True)
self.assertEqual(x.grad, torch.empty(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, torch.ones(5, 5) * 11)
# x should be still usable
y = x + 2
y.backward(torch.ones(5, 5))
self.assertEqual(leaf.grad, 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).to(torch.double)
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, 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, dtype=torch.double, 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, dtype=torch.double, 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]))
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.BoolTensor(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, dtype=torch.double, requires_grad=True)
def func(x):
return x.select(0, 1).sum()
gradcheck(func, [x])
gradgradcheck(func, [x])
def test_diagonal_expanded_v(self):
value = torch.rand([])
v_expanded = torch.tensor(value).expand(10)
a = torch.rand(10, 10, dtype=torch.double, requires_grad=True)
result, = torch.autograd.grad(a.diagonal(), a, v_expanded)
self.assertEqual(result, torch.eye(10, dtype=torch.double) * value)
def test_select_expanded_v(self):
v_expanded = torch.rand(10).expand(10, 10)
a = torch.rand(10, 10, 10, requires_grad=True)
result, = torch.autograd.grad(a[0], a, v_expanded)
expected = torch.zeros(10, 10, 10)
expected[0] = v_expanded
self.assertEqual(result, expected)
def test_slice_expanded_v(self):
v_expanded = torch.rand(10, 1).expand(2, 10, 10)
a = torch.rand(10, 10, 10, requires_grad=True)
result, = torch.autograd.grad(a[3:5], a, v_expanded)
expected = torch.zeros(10, 10, 10)
expected[3:5] = v_expanded
self.assertEqual(result, expected)
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, expected_grad)
with torch.no_grad():
x.grad.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, expected_grad)
# TODO: opinfo this or move to the sparse test suite
def _test_sparse_gather(self, size_x, size_ind, dim):
x = torch.randn(size_x, requires_grad=True)
if len(size_ind) > 0 and len(size_x) > 0:
ind = torch.randint(x.size(dim), size_ind)
else:
ind = torch.zeros(size_ind, dtype=torch.int64)
out = torch.gather(x, dim, ind, sparse_grad=False)
grad = torch.rand_like(out)
out.backward(grad)
grad_dense = x.grad.clone()
x.grad = None
out = torch.gather(x, dim, ind, sparse_grad=True)
out.backward(grad)
self.assertEqual(grad_dense, x.grad.to_dense())
def test_sparse_gather_dim0(self):
self._test_sparse_gather((10, 10), (5, 10), 0)
def test_sparse_gather_dim1(self):
self._test_sparse_gather((10, 10, 5), (10, 5, 5), 1)
def test_sparse_gather_dim_neg(self):
self._test_sparse_gather((10, 10, 5), (10, 10, 2), -1)
def test_sparse_gather_ind_scalar(self):
self._test_sparse_gather((10,), (), 0)
def test_sparse_gather_x_scalar(self):
self._test_sparse_gather((), (2,), 0)
def test_sparse_gather_both_scalar(self):
self._test_sparse_gather((), (), 0)
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 forward(self, x):
return x
def backward(self, grad_output):
return grad_output
def __del__(self):
gc.collect()
for _ in range(10):
CollectOnDelete().forward(torch.randn(1, requires_grad=True)).backward()
def test_naughty_autograd_function_attribute_access(self):
class Id(Function):
@staticmethod
def forward(ctx, x):
return x
@staticmethod
def backward(ctx, grad_x):
return grad_x
with self.assertWarnsRegex(DeprecationWarning, "should not be instantiated"):
f = Id()
# # After raising warning, should still return an instance
self.assertIsInstance(f, Id)
x = torch.zeros(1, requires_grad=True)
with self.assertRaisesRegex(RuntimeError, "non-static forward method is deprecated"):
f(x)
t = Id.apply(x)
self.assertEqual(t.grad_fn.name(), "IdBackward")
# THPFunction is the base class of both grad_fn and autograd functions,
# which means that a lot of accessors on them may segfault. Test that we
# properly error in this case.
t = torch.ones(1, requires_grad=True)
t._backward_hooks = dict()
with self.assertRaisesRegex(RuntimeError, "Attribute '_register_hook_dict' is invalid"):
f._register_hook_dict(t)
with self.assertRaisesRegex(RuntimeError, "Attribute 'register_hook' is invalid"):
f.register_hook(lambda x, y: None)
with self.assertRaisesRegex(RuntimeError, "Attribute 'next_functions' is invalid"):
f.next_functions
with self.assertRaisesRegex(RuntimeError, "Attribute 'name' is invalid"):
f.name()
with self.assertRaisesRegex(RuntimeError, "underlying PyNode has already been deallocated"):
f.metadata
@unittest.expectedFailure
def test_naughty_anomaly_access(self):
class MyFunction(Function):
@staticmethod
def forward(ctx, x):
return x
@staticmethod
def backward(ctx, g):
return g
x = torch.zeros(1, requires_grad=True)
y = MyFunction.apply(x)
y.backward()
y.grad_fn.metadata
g = y.grad_fn
del y
g.metadata # this currently fails, but shouldn't
def test_naughty_autograd_function_stashing_ctx(self):
saved_ctx = []
class Id(Function):
@staticmethod
def forward(ctx, x):
ctx.save_for_backward(x)
return x
@staticmethod
def backward(ctx, grad_x):
saved_ctx.append(ctx)
return ctx.saved_tensors
p = torch.zeros(1, requires_grad=True)
loss = Id.apply(p)
loss.backward(retain_graph=True)
del loss
# At this point in time, it complains that the graph has been freed
# (which indeed true, although a somewhat indirect way of stating the
# problem).
self.assertRaises(RuntimeError, lambda: saved_ctx[0].saved_tensors)
def test_custom_autograd_repeated_grad_grad(self):
# This test failed the equality check in PR #22983; it's an interesting
# and different test case worth enshrining. mult1 is not testing
# anything that interesting, but mult2 is the interesting case.
def mult1(x):
return x.prod(dim=-1).prod(dim=-1)
class Mult(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
y = mult1(x)
ctx.save_for_backward(x, y)
return y
@staticmethod
def backward(ctx, grad_output):
x, y = ctx.saved_tensors
return (grad_output * y)[:, None, None] / x
mult2 = Mult.apply
def check_gradgrad_repeated(x, y):
gy, = torch.autograd.grad(y[0], x, create_graph=True)
ggy_1, = torch.autograd.grad(gy[0, 0, 0], x, retain_graph=True)
gy, = torch.autograd.grad(y[0], x, create_graph=True)
ggy_2, = torch.autograd.grad(gy[0, 0, 0], x, retain_graph=True)
self.assertEqual(ggy_1[0, 0, 1], ggy_2[0, 0, 1])
x = torch.ones(2, 4, 4).requires_grad_()
check_gradgrad_repeated(x, mult1(x))
check_gradgrad_repeated(x, mult2(x))
def test_custom_autograd_no_early_free(self):
# This test failed complaining that buffers had already been freed
# prior to #22983. Also pretty interesting test case.
class Double(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
y = x ** 2
ctx.save_for_backward(x, y)
return y
@staticmethod
def backward(ctx, grad_output):
x, _ = ctx.saved_tensors
return grad_output * 2 * x
# this is equivalent, but uses the output of .forward() in .backward()
class Double2(Double):
@staticmethod
def backward(ctx, grad_output):
x, y = ctx.saved_tensors
return grad_output * 2 * y / x
double = Double.apply
double2 = Double2.apply
x = torch.tensor(2).double().requires_grad_()
self.assertTrue(gradcheck(double, x))
self.assertTrue(gradgradcheck(double, x))
self.assertTrue(gradcheck(double2, x))
self.assertTrue(gradgradcheck(double2, x))
y = double(x)
torch.autograd.grad(y, x, create_graph=True)
torch.autograd.grad(y, x)
y = double2(x)
torch.autograd.grad(y, x, create_graph=True)
torch.autograd.grad(y, x) # should not error!
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, 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, torch.ones(10, 10) * 2)
self.assertEqual(y.grad, 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)
def test_detach_then_inplace_raises_in_autograd(self):
x = torch.randn([], requires_grad=True)
orig_x = x.detach().clone()
y = x ** 2 # saves x
z = x.detach()
z.zero_()
with self.assertRaisesRegex(RuntimeError, "has been modified by an inplace"):
y.backward()
def test_detach_disallows_metadata_change(self):
x = torch.randn([], requires_grad=True)
detached = x.detach()
with self.assertRaisesRegex(
RuntimeError, "not allowed on a Tensor created from .data or .detach()"):
detached.resize_(3, 3)
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), type(fvar))
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), type(dvar))
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, 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().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_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)
err_msg_outputs = r"Can't call numpy\(\) on Tensor that requires grad. Use tensor.detach\(\).numpy\(\) instead."
with self.assertRaisesRegex(RuntimeError, err_msg_outputs):
x.numpy()
with torch.no_grad():
x.numpy()
x = torch.randn(2, 2)
x.numpy()
with torch.no_grad():
x.numpy()
def test_return_leaf(self):
class Identity(Function):
@staticmethod
def forward(ctx, a, b):
return a, a + b
@staticmethod
def backward(ctx, 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.apply(x, y)
# Make sure hooks only receive grad from usage of q, not x.
def hook(grad):
hook_called[0] = True
self.assertEqual(grad, torch.ones(5, 5))
q.register_hook(hook)
(q + p + x).sum().backward()
self.assertEqual(x.grad, torch.ones(5, 5) * 3)
self.assertEqual(y.grad, torch.ones(5, 5))
self.assertTrue(hook_called[0])
def test_return_leaf_inplace(self):
class Inplace(InplaceFunction):
@staticmethod
def forward(ctx, a, b):
ctx.mark_dirty(a)
return a.add_(b), b + 2
@staticmethod
def backward(ctx, grad_a, grad_b):
return grad_a, grad_a + grad_b
x = torch.randn(5, 5)
y = torch.randn(5, 5, requires_grad=True)
q, p = Inplace.apply(x, y)
self.assertIs(q, x)
self.assertIs(q.grad_fn.__class__, Inplace._backward_cls)
self.assertTrue(q.requires_grad)
q.sum().backward()
self.assertEqual(y.grad, 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, torch.ones(5))
self.assertEqual(z.grad, 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 _ 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):
@staticmethod
def forward(ctx, input):
ctx.save_for_backward(None, input, None)
return input * input
@staticmethod
def backward(ctx, grad_output):
n1, input, n2 = ctx.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.apply(x)
y.sum().backward()
self.assertEqual(x.grad, 2 * x)
def test_too_many_grads(self):
class MyFn(Function):
@staticmethod
def forward(ctx, input):
return input
@staticmethod
def backward(ctx, grad_output):
return grad_output, None, None
x = torch.randn(5, 5, requires_grad=True)
y = MyFn.apply(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, var2)
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):
@staticmethod
def forward(ctx, input):
out = torch.randn(input.size())
ctx.mark_non_differentiable(out)
return input, out
@staticmethod
def backward(ctx, grad_output, ignored):
return grad_output
class F2(Function):
@staticmethod
def forward(ctx, input, ignored):
return input
@staticmethod
def backward(ctx, grad_output):
return grad_output, None
x = torch.randn(5, requires_grad=True)
a, b = F1.apply(x)
b = b + 1 # separate F1 from F2 by another op
self.assertTrue(a.requires_grad)
self.assertFalse(b.requires_grad)
c = F2.apply(a, b)
c.backward(torch.ones(c.size()))
self.assertEqual(x.grad, 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_simple_reentrant(self):
y_data = torch.randn(2, 2)
class Reenter(Function):
@staticmethod
def forward(ctx, x):
with torch.enable_grad():
ctx.x = Variable(x, 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
# Reentrant starts on CPU thread, finishs on GPU thread
x = torch.randn(2, 2, requires_grad=True)
out = Reenter.apply(x)
out.sum().backward()
self.assertEqual(x.grad, y_data)
def test_reentrant_child_error(self):
# Parent graph.
a = torch.rand(3, 3, requires_grad=True)
c = a * a
# Reentrant child graph.
b = torch.rand(3, 3, requires_grad=True)
e = b * b
f = TestAutograd.SimulateBackwardError.apply(e)
reentrant_root = f.sum()
class ReentrantFunc(Function):
@staticmethod
def forward(ctx, inp):
return inp.clone()
@staticmethod
def backward(ctx, grad):
# Reentrant backward in child will throw an error.
reentrant_root.backward()
return grad
d = ReentrantFunc.apply(c)
with self.assertRaisesRegex(Exception, 'Simulate error'):
d.sum().backward()
def test_var_mean_differentiable(self):
dim = [2, 4]
keepdim = False
input1 = torch.randn(3, 4, 5, 6, 2, 3, requires_grad=True)
input2 = deepcopy(input1)
var1, mean1 = torch.var_mean(input1, dim=dim, keepdim=keepdim)
var2 = input2.var(dim=dim, keepdim=keepdim)
mean2 = input2.mean(dim=dim, keepdim=keepdim)
grad = torch.randn(3, 4, 6, 3, requires_grad=True)
r1 = var1 * var1 * mean1 * mean1
r2 = var2 * var2 * mean2 * mean2
self.assertEqual(r1, r2, rtol=0.01, atol=0.0)
torch.autograd.backward(r1, grad)
torch.autograd.backward(r2, grad)
self.assertEqual(input1.grad, input2.grad, rtol=0.01, atol=0.0)
@skipIfNoLapack
def test_lobpcg(self):
def func(k, A, largest=True, B=None):
X_shape = list(A.shape)
X_shape[-1] = k
X = torch.eye(A.size(-2), k, dtype=A.dtype, device=A.device)
if A.dim() > 2:
X = X.expand(X_shape)
D, U = torch.lobpcg(A=A, k=k, B=B, X=X, largest=largest)
# LOBPCG uses a random initial eigenspace approximation
# if parameter `X` is not provided.
# This may cause a non-deterministic behavior
# when it comes to the sign of an eigenvector
# (note if v is an eigenvector, so is -v),
# hence we eliminate this non-determinism
# by making sure that each column of U
# gets multiplied by the sign of its max (in absolute value) element.
# Also, gradcheck changes the content of the input by +/- eps (default to 1e-06)
# to compute the numerical gradient which can also cause the signs to flip.
_, idx = U.abs().max(-2, keepdim=True)
sign = U.gather(-2, idx).sign()
U = U * sign
return D, U
# TODO: review if this can be ported to OpInfos or moved to test_linalg.py
def run_symeig_test(k, sizes, largest=True):
A = torch.rand(*sizes).double()
A = (A @ A.mT) / 10
A.requires_grad_(True)
gradcheck(lambda A: func(k, A, largest), A, check_batched_grad=False)
# Custom gradient vectors for better stability due to some
# non-determinism in the lobpcg's forward.
# Note it is not required if symeig is in forward instead (tested).
D_grad = torch.rand(*A.shape[:-2], k) / 100
U_grad = torch.rand(*A.shape[:-1], k) / 100
gradgradcheck(lambda A: func(k, A, largest), A, [D_grad, U_grad], atol=1e-4, check_batched_grad=False)
# check whether A.grad is symmetric
A = A.detach().requires_grad_(True)
D, U = func(k, A, largest)
(D.sum() + U.sum()).backward()
self.assertEqual(A.grad, A.grad.mT)
for largest in [True, False]:
run_symeig_test(1, (6, 6), largest=largest)
run_symeig_test(1, (2, 6, 6), largest=largest)
run_symeig_test(1, (2, 2, 6, 6), largest=largest)
run_symeig_test(2, (6, 6), largest=largest)
run_symeig_test(2, (2, 6, 6), largest=largest)
run_symeig_test(2, (2, 2, 6, 6), largest=largest)
run_symeig_test(3, (9, 9), largest=largest)
run_symeig_test(3, (2, 9, 9), largest=largest)
run_symeig_test(3, (2, 2, 9, 9), largest=largest)
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()))
# TODO: review porting these to OpInfo tests
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.abs().sum(), 0)
run_test((10,), torch.zeros(10))
run_test((10, 10), torch.zeros(10, 10))
run_test((10,), 0)
def test_profiler(self):
x = torch.randn(10, 10)
with profile(use_kineto=kineto_available()) as p:
self.assertTrue(torch.autograd._profiler_enabled())
y = x * 2 + 4
self.assertFalse(torch.autograd._profiler_enabled())
names = ['aten::mul', 'aten::add']
found_indices = set()
for evt in p.function_events:
if evt.name in names:
found_indices.add(names.index(evt.name))
self.assertEqual(len(found_indices), len(names))
def test_profiler_seq_nr(self):
with profile(use_kineto=kineto_available()) as p:
x = torch.randn(10, 10, requires_grad=True)
y = torch.randn(10, 10, requires_grad=True)
z = x + y
s = z.sum()
s.backward()
print(p.key_averages().table(
sort_by="self_cpu_time_total", row_limit=-1))
# expecting aten::add, aten::sum to have the sequence numbers,
# expecting the corresponding backward nodes to have the same numbers
# as the forward ops
autograd_ops = {
("aten::add", "Add"): [],
("aten::sum", "Sum"): [],
}
accumulate_ops = []
found_empty = False
for e in p.function_events:
for (fwd_name, bwd_name), ops in autograd_ops.items():
if e.name == fwd_name or (bwd_name in e.name and "Backward" in e.name):
ops.append(e)
if "AccumulateGrad" in e.name:
accumulate_ops.append(e)
# check that nested ops (e.g. empty) don't have
# sequence number
if e.name == "aten::empty":
self.assertEqual(e.sequence_nr, -1)
found_empty = True
for (fwd_name, bwd_name), ops in autograd_ops.items():
self.assertEqual(len(ops), 3)
self.assertEqual(ops[0].name, fwd_name)
self.assertEqual(ops[1].name, f"autograd::engine::evaluate_function: {bwd_name}Backward0")
self.assertEqual(ops[2].name, f"{bwd_name}Backward0")
self.assertGreaterEqual(ops[0].sequence_nr, 0)
self.assertEqual(ops[1].sequence_nr, ops[0].sequence_nr)
self.assertEqual(ops[2].sequence_nr, ops[0].sequence_nr)
self.assertEqual(ops[0].fwd_thread, 0)
self.assertEqual(ops[1].fwd_thread, ops[0].thread)
self.assertEqual(ops[2].fwd_thread, ops[0].thread)
self.assertTrue(found_empty)
def test_profiler_unboxed_only(self):
x = torch.rand(3, 4)
with torch.autograd.profiler.profile(use_kineto=kineto_available()) as prof:
x.resize_([3, 2])
def test_profiler_propagation(self):
def foo(x):
with record_function("in_foo") as rf:
return x * 2
x = torch.rand(3, 4)
traced_foo = torch.jit.trace(foo, x)
def bar(x):
with record_function("in_bar") as rf:
# we expect that profiler will be able
# propagate across fork
fut = torch.jit._fork(traced_foo, x)
y = torch.jit._wait(fut)
# note: continuation (and rf's end) can
# be executed in a different thread
with record_function("in_bar_after_wait") as rf2:
y = y * 2
return y
traced_bar = torch.jit.trace(bar, x)
with profile(use_kineto=kineto_available()) as p:
traced_bar(x)
found_foo = False
found_bar = False
found_bar_after_wait = False
for info in p.function_events:
if info.name == "in_foo":
self.assertFalse(found_foo)
found_foo = True
elif info.name == "in_bar":
self.assertFalse(found_bar)
found_bar = True
elif info.name == "in_bar_after_wait":
self.assertFalse(found_bar_after_wait)
found_bar_after_wait = True
self.assertTrue(found_foo)
self.assertTrue(found_bar)
self.assertTrue(found_bar_after_wait)
def test_record_function_callbacks(self):
x = torch.randn(10, 10)
with profile(use_kineto=kineto_available()) as p:
with record_function("foo"):
y = x * 2 + 4
function_events = p.function_events
foo_event = [event for event in function_events if "foo" in event.name][0]
self.assertEqual(foo_event.count, 1)
def test_record_function_new_signatures(self):
# Test the new _record_function ops work
# Note: Remove once record_function uses these directly
x = torch.randn(10, 10)
with profile(use_kineto=kineto_available()) as p:
record = torch.ops.profiler._record_function_enter_new("bar", None)
try:
y = x * 2 + 4
finally:
torch.ops.profiler._record_function_exit(record)
function_events = p.function_events
foo_event = [event for event in function_events if "bar" in event.name][0]
self.assertEqual(foo_event.count, 1)
def test_profiler_aggregation_fake(self):
events = EventList()
id = [0]
def get_id():
id[0] = id[0] + 1
return id[0]
# [[thread_id, [(start, end, id), ....]], ...]
# Using list instead of a dict so order is guaranteed for any Python
# version
threads = [
[1, [(0, 1, get_id()), (1, 2, get_id())]],
[0, [(0, 2, get_id()), (1, 2, get_id()), (1, 3, get_id())]],
]
for thread, ranges in threads:
for range in ranges:
assert(len(range) == 3)
events.append(
FunctionEvent(
id=range[2],
node_id=0,
name="",
thread=thread,
start_us=range[0],
end_us=range[1],
)
)
events._populate_cpu_children()
# Note that [1, 3] pushes out [0, 2] first. Then we record [1, 2]
# as a child of [1, 3]
res = [[], [], [], [], [4]]
def get_children_ids(event):
return [child.id for child in event.cpu_children]
assert([get_children_ids(event) for event in events] == res)
def test_profiler_aggregation_table(self):
"""
Test if the profiling result is aggregated for `str(prof)`
See: https://github.com/pytorch/pytorch/issues/37500
"""
x = torch.randn(1024)
with torch.autograd.profiler.profile(use_kineto=kineto_available()) as prof:
torch.einsum("i->", x)
prof_str = str(prof)
prof_table = prof.table()
self.assertEqual(prof_table, prof_str)
def test_profiler_function_event_avg(self):
avg = FunctionEventAvg()
avg.add(FunctionEvent(id=0, node_id=0, name="foo", thread=0, start_us=10, end_us=15))
avg.add(FunctionEvent(id=1, node_id=0, name="foo", thread=0, start_us=20, end_us=30))
avg.add(avg)
self.assertEqual(avg.key, "foo")
# aggregate stats
self.assertEqual(avg.count, 4)
self.assertEqual(avg.cpu_time_total, 30)
self.assertEqual(avg.self_cpu_time_total, 30)
self.assertEqual(avg.cuda_time_total, 0)
# average stats
self.assertEqual(avg.cpu_time, 7.5)
self.assertEqual(avg.cuda_time_total, 0)
def test_profiler_shapes(self):
print("")
layer1 = torch.nn.Linear(20, 30)
layer2 = torch.nn.Linear(30, 40)
input = torch.randn(128, 20)
with profile(record_shapes=True, use_kineto=kineto_available()) as prof:
layer2(layer1(input))
print(prof.function_events)
linear_expected_shapes = [
[[128, 20], [30, 20], [30]],
[[128, 30], [40, 30], [40]],
]
found_indices = set()
for event in prof.function_events:
if event.name == "aten::linear":
self.assertTrue(event.input_shapes in linear_expected_shapes)
found_indices.add(linear_expected_shapes.index(event.input_shapes))
self.assertEqual(len(found_indices), len(linear_expected_shapes))
def test_profiler_aggregation_lstm(self):
print("")
rnn = torch.nn.LSTM(10, 20, 2)
total_time_s = 0
with profile(record_shapes=True, use_kineto=kineto_available()) as prof:
for i in range(20):
input = torch.randn(5, 3, 10)
h = torch.randn(2, 3, 20)
c = torch.randn(2, 3, 20)
start = time.time()
rnn(input, (h, c))
end = time.time()
total_time_s += end - start
print(prof.table(
sort_by="self_cpu_time_total", row_limit=10, header="TEST"))
print(prof.key_averages(group_by_input_shape=True).table(
sort_by="self_cpu_time_total", row_limit=10))
print(prof.table(
sort_by="self_cpu_time_total", row_limit=10, max_src_column_width=300, header="TEST", top_level_events_only=True))
print(prof.key_averages(group_by_input_shape=True).table(
sort_by="self_cpu_time_total", row_limit=10, top_level_events_only=True))
total_time_us = total_time_s * 1000.0 * 1000.0 # make it us which is profiler default
print(
"Total time based on python measurements: ",
_format_time(total_time_us)
)
print(
"CPU time measurement python side overhead: {:.2f}%".format(
(total_time_us / prof.self_cpu_time_total - 1.0) * 100.0
)
)
if sys.platform != "win32":
with tempfile.NamedTemporaryFile() as trace_file:
prof.export_chrome_trace(trace_file.name)
def test_record_function(self):
x = torch.randn(10, 10)
def forward(x):
with record_function("outer"):
y = x * 2 + 4
with record_function("inner"):
y = y - 1
y = y / 1
forward(x)
with profile(use_kineto=kineto_available()) as p:
forward(x)
events = p.function_events
important_events = [
'outer',
'aten::mul',
'aten::add',
'inner',
'aten::sub',
'aten::div'
]
idx = 0
for info in events:
if info.name == important_events[idx]:
idx = idx + 1
if idx == len(important_events):
break
self.assertEqual(idx, len(important_events))
# We can also use record_function to decorate arbitrary function
@record_function('my_func')
def f(x, y):
return x + y
with profile(use_kineto=kineto_available()) as p:
f(1, 2)
self.assertTrue('my_func' in str(p))
def test_record_function_multithreaded(self):
rf = record_function("outer")
rf.__enter__()
with record_function("inner"):
# test that exiting the record function after starting another one
# doesn't throw.
rf.__exit__(None, None, None)
with record_function("inner"):
rf.__enter__()
# test that exiting the record function after ending another one
# doesn't throw.
rf.__exit__(None, None, None)
def test_dir(self):
x = torch.randn(10, 10)
keys = dir(x)
self.assertIn('shape', keys)
# real and imag are only implemented for complex tensors.
y = torch.randn(10, 10, dtype=torch.cfloat)
imag_key = 'imag'
self.assertRaises(RuntimeError, lambda: hasattr(x, imag_key))
self.assertTrue(hasattr(y, imag_key))
keys.remove(imag_key)
for key in keys:
self.assertTrue(hasattr(x, key))
def test_inplace_on_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_inplace_on_view_leaf_errors(self):
# Issue #21875: Fail faster (when we try to modify the view vs. in backward())
x = torch.zeros(1, requires_grad=True)
y = x.view_as(x)
with self.assertRaisesRegex(RuntimeError,
"a view of a leaf Variable that "
"requires grad is being used in "
"an in-place operation."):
y.add_(1)
def test_inplace_on_view_backward(self):
# Issue #10532: Make sure that this does not raise RuntimeError.
net = nn.Sequential(
nn.InstanceNorm2d(2),
nn.ReLU(True)
)
x = torch.tensor([[[[1.0, 1.0]]]], requires_grad=True)
g, = torch.autograd.grad(net(x).pow(2), [x], grad_outputs=x.new_ones(x.shape) , create_graph=True)
torch.autograd.grad(g.sum(), [x])
self.assertEqual(x, torch.tensor([[[[1.0, 1.0]]]]))
# https://discuss.pytorch.org/t/freeing-buffer-strange-behavior/31955/8
inputs = torch.ones((1, 3, 256, 256), requires_grad=True)
tmp1 = (inputs + 1).view_as(inputs)
tmp2 = torch.nn.functional.threshold(tmp1, 0., 0., True)
prob_interpolated = torch.sigmoid(tmp2)
gradients = torch.autograd.grad(outputs=prob_interpolated, inputs=inputs,
grad_outputs=torch.ones(prob_interpolated.size()),
create_graph=True, retain_graph=True)[0]
gradient_penalty = gradients.sum()
gradient_penalty.backward()
fn = gradient_penalty.grad_fn.next_functions[0][0].next_functions[1][0]
self.assertEqual(fn.name(), "ThresholdBackwardBackward0")
def test_inplace_on_view_weak_grad_fn(self):
# Issue 23502: Test that b's grad_fn is preserved.
a = torch.arange(10.0, requires_grad=True)
b = a.narrow(0, 0, 2).clone().view(-1)
b.relu_()
c = b.clone()
del b
gc.collect()
s = c.sum()
s.backward()
self.assertEqual(s, torch.tensor(1.0))
# Issue #21875: Fail faster (when we try to modify the view vs. in backward())
a = torch.rand(10, requires_grad=True).narrow(0, 0, 10)
with self.assertRaises(RuntimeError):
b = a.relu_()
def test_out_variant_raises_when_inputs_require_grad(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)
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))
# TODO: see if this test can be OpInfo'd or moved to diagonal's test suite
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)
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_calculate_shape_util(self):
out = torch.randn(10, 5, requires_grad=True)
grad = torch.randn(5, 10, requires_grad=True)
out_shape, grad_shape = _calculate_shape(out, grad, False)
assert out_shape == torch.Size([10, 5])
assert grad_shape == torch.Size([5, 10])
out = torch.nested_tensor([
torch.randn(10, 5, requires_grad=True),
torch.randn(10, 5, requires_grad=True),
torch.randn(10, 5, requires_grad=True)]
)
grad = torch.nested_tensor([torch.randn(5, 10, requires_grad=True), torch.randn(5, 10, requires_grad=True)])
out_shape, grad_shape = _calculate_shape(out, grad, False)
assert torch.equal(out_shape, torch.tensor([[10, 5], [10, 5], [10, 5]]))
assert torch.equal(grad_shape, torch.tensor([[5, 10], [5, 10]]))
def test_nested_anomaly_detect_nan(self):
size = 10
class MyFunc(Function):
@staticmethod
def forward(ctx, inp1, fail_0th):
ctx.fail_0th = fail_0th
ctx.save_for_backward(inp1)
return inp1.sum(0, keepdim=True)
@staticmethod
def backward(ctx, gO):
inp, = ctx.saved_tensors
fail_0th = ctx.fail_0th
g = gO.clone().expand(size)
gI = MyFunc2.apply(g * inp, g + inp, fail_0th)
return gI, None
class MyFunc2(Function):
@staticmethod
def forward(ctx, inp1, inp2, fail_0th):
ctx.fail_0th = fail_0th
return inp1 * 2.0 + inp2
@staticmethod
def backward(ctx, gO):
fail_0th = ctx.fail_0th
g1 = gO.clone()
g2 = gO.clone()
g1[0] = 0
g2[0] = 0
# generate a nan
if fail_0th:
g1[0] /= 0
else:
g2[0] /= 0
return g1, g2, None
inp = torch.rand(size, requires_grad=True)
out = MyFunc.apply(inp, True)
ginp, = torch.autograd.grad(out, (inp,), create_graph=True)
gsum = ginp.sum()
gsum.backward() # should not fail
inp = torch.rand(size, requires_grad=True)
out = MyFunc.apply(inp, True)
ginp, = torch.autograd.grad(out, (inp,), create_graph=True)
gsum = ginp.sum()
with warnings.catch_warnings(record=True) as w:
with self.assertRaisesRegex(RuntimeError, "Function 'MyFunc2Backward' returned nan values in its 0th output."):
with detect_anomaly():
gsum.backward()
self.assertIn('No forward pass information', str(w[1].message))
inp = torch.rand(size, requires_grad=True)
with warnings.catch_warnings(record=True) as w:
with self.assertRaisesRegex(RuntimeError, "Function 'MyFunc2Backward' returned nan values in its 1th output."):
with detect_anomaly():
out = MyFunc.apply(inp, False)
ginp, = torch.autograd.grad(out, (inp,), create_graph=True)
gsum = ginp.sum()
gsum.backward()
self.assertIn('MyFunc2.apply', str(w[1].message))
self.assertIn('MyFunc.apply', str(w[2].message))
def test_anomaly_grad_warnings(self):
# PyTorch won't throw warnings if there is an error
# but we'd want to at least see them in stderr
class StdErrDiverter:
def __enter__(self):
self.stderr_orig = sys.stderr
self.stderr_new = io.StringIO()
sys.stderr = self.stderr_new
return self
def __exit__(self, *args):
self.captured = self.stderr_new.getvalue()
sys.stderr = self.stderr_orig
# if the warnings don't throw, they will be handled as regular warnings
with self.assertRaisesRegex(RuntimeError,
"one of the variables needed for gradient computation has been "
"modified by an inplace operation"):
with warnings.catch_warnings(record=True) as w:
with detect_anomaly():
a = torch.randn(5, requires_grad=True)
d1 = a + 1
d2 = d1 ** 2
d1 += 1
torch.autograd.grad(d2.sum(), a)
self.assertEqual(len(w), 2)
self.assertIn('Anomaly Detection has been enabled', str(w[0].message))
self.assertIn('Error detected in PowBackward0', str(w[1].message))
# if the warning throws, it will be printed to sys.stderr
with self.assertRaisesRegex(RuntimeError,
"one of the variables needed for gradient computation has been "
"modified by an inplace operation"):
with warnings.catch_warnings(record=True) as w:
with detect_anomaly():
warnings.simplefilter("error")
with StdErrDiverter() as s:
a = torch.randn(5, requires_grad=True)
d1 = a + 1
d2 = d1 ** 2
d1 += 1
torch.autograd.grad(d2.sum(), a)
self.assertEqual(len(w), 1)
self.assertIn('Anomaly Detection has been enabled', str(w[0].message))
self.assertIn('Error detected in PowBackward0', s.captured)
def test_anomaly_assign_parent_cleanup(self):
# Test that python objects created are properly cleaned up when assign_parent is called
import weakref
def get_ref():
# we use torch.exp here but any function that will construct a new node in its
# backward call in grad mode will work
x = torch.randn(2, 2, requires_grad=True)
t = x.exp()
# ExpBackward calls mul, creating the MulBackward node when create_graph=True.
# In anomaly mode, a PyObject referencing MulBackward's "parent" ExpBackward is added to
# MulBackward's anomaly metadata dict, creating the following reference chain:
#
# grad -> MulBackward -> PyObject -> ExpBackward
#
with detect_anomaly():
grad = torch.autograd.grad(t, x, torch.ones_like(t), create_graph=True)
# We add a weak reference to a new Foo object, which we insert into ExpBackward's metadata dict
#
# (PyObject) -> ExpBackward -> dict -> *Foo*
# t ----^ WeakRef ---^
#
# We want to test that when grad goes out of scope at the end of this function that PyObject is destroyed
# We can test this by seeing whether Foo is not kept alive once t is destroyed
class Foo(object):
pass
my_obj = Foo()
meta_dict = t.grad_fn.metadata
meta_dict[0] = my_obj
ref = weakref.ref(my_obj)
return t, ref
t, ref = get_ref()
self.assertIsNotNone(ref())
del t
self.assertIsNone(ref())
def test_nested_anomaly_printstack_cleanup(self):
# Test if metadata dict PyObject is properly destroyed
import weakref
def get_ref():
# This is similar to the construction in test_anomaly_assign_parent_cleanup:
#
# MyFuncBackward2 -> PyObject -> MyFuncBackward -> dict -> Foo
# out ---^ WeakRef ---^
#
# We want to check that Foo is still properly destroyed even when MyFunc2Backward's
# AnomalyMetadata calls printstack, which does some python object manipulation.
#
# You might be wondering why we still have to test_anomaly_assign_parent_cleanup,
# since if PyObject is not destroyed here, wouldn't this test would detect that also?
# The answer is that custom function's PyObject (THPFunction) actually only hold
# a weak reference to the c++ node!
class MyFunc(Function):
@staticmethod
def forward(ctx, x):
ctx.save_for_backward(x)
return x
@staticmethod
def backward(ctx, gO):
x, = ctx.saved_tensors
return MyFunc2.apply(x)
class MyFunc2(Function):
@staticmethod
def forward(ctx, x):
return x
@staticmethod
def backward(ctx, gO):
return gO + float("NaN")
inp = torch.rand(1, requires_grad=True)
out = MyFunc.apply(inp)
ginp, = torch.autograd.grad(out, (inp,), create_graph=True)
with warnings.catch_warnings(record=True) as w:
with self.assertRaisesRegex(RuntimeError, "Function 'MyFunc2Backward' returned nan values in its 0th output."):
with detect_anomaly():
ginp.backward()
class Foo(object):
pass
my_obj = Foo()
meta_dict = out.grad_fn.metadata
meta_dict[0] = my_obj
ref = weakref.ref(my_obj)
return out, ref
t, ref = get_ref()
self.assertIsNotNone(ref())
del t
self.assertIsNone(ref())
# TODO: update these tests to use the linalg module and move to test_linalg.py
@skipIfNoLapack
def test_eig_no_eigenvectors(self):
A = torch.tensor([[1., 2.], [2., 4.]], dtype=torch.float32, requires_grad=True)
w, v = torch.eig(A, eigenvectors=False)
with self.assertRaisesRegex(RuntimeError, 'is not differentiable'):
torch.autograd.backward([w, v], [torch.ones_like(w), torch.ones_like(v)])
@skipIfNoLapack
def test_eig_complex_eigenvalues(self):
A = torch.tensor([[0., -1.], [1., 0.]], dtype=torch.float32, requires_grad=True)
w, v = torch.eig(A, eigenvectors=True)
with self.assertRaisesRegex(RuntimeError, 'does not support complex eigenvalues'):
torch.autograd.backward([w, v], [torch.ones_like(w), torch.ones_like(v)])
@skipIfNoLapack
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, 'is not differentiable'):
torch.autograd.backward([w, v], [torch.ones_like(w), 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 test_no_grad_copy_sparse(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._values().data_ptr()
return grad, grad
class NonContGradFunc(Function):
static_grad_ptr = None
@staticmethod
def forward(ctx, inp1, inp2):
return inp1 + inp2
@staticmethod
def backward(ctx, grad):
# Create a sparse tensor with non-contigous indices and values
# and return as grad.
v = torch.rand(1, 3)
i = torch.ones(1, 1, dtype=torch.long)
nv = v.expand(8, 3)
ni = i.expand(1, 8)
ngrad = torch.sparse.FloatTensor(ni, nv, torch.Size([10, 3]))
NonContGradFunc.static_grad_ptr = ngrad._values().data_ptr()
return ngrad, ngrad
a = torch.randn(10, 3, requires_grad=True)
b = torch.randn(10, 3, requires_grad=True)
input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9])
offsets = torch.tensor([0, 4])
import torch.nn.functional as F
# test case that should trigger no copy for one of a,b
emb_matrix = MyFunc.apply(a, b)
loss = F.embedding_bag(emb_matrix, input, offsets, sparse=True).sum()
loss.backward(retain_graph=True)
p_g = MyFunc.static_grad_ptr
p_a = a.grad._values().data_ptr()
p_b = b.grad._values().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)
# Run backwards multiple times to ensure accumulation works.
for i in range(10):
loss.backward(retain_graph=True)
# non-contiguous indices and value, we should trigger a copy.
a.grad = b.grad = None
emb_matrix = NonContGradFunc.apply(a, b)
loss = F.embedding_bag(emb_matrix, input, offsets, sparse=True).sum()
loss.backward(retain_graph=True)
p_g = NonContGradFunc.static_grad_ptr
p_a = a.grad._values().data_ptr()
p_b = b.grad._values().data_ptr()
# check a,b uses different grad buffer
self.assertFalse(p_a == p_b)
# Verify we cloned both grads.
self.assertFalse(p_a == p_g)
self.assertFalse(p_b == p_g)
# Run backwards multiple times to ensure accumulation works.
for i in range(10):
loss.backward(retain_graph=True)
def test_gradcheck_single_input(self):
def check(fast_mode):
def f(inp):
return inp.mul(5)
gradcheck(f, torch.rand(10, dtype=torch.float64, requires_grad=True), fast_mode=fast_mode)
gradgradcheck(f, torch.rand(10, dtype=torch.float64, requires_grad=True), fast_mode=fast_mode)
check(fast_mode=True)
check(fast_mode=False)
def test_gradcheck_sparse_input(self):
def check(fast_mode):
def fn(sparse):
return torch.sparse.sum(sparse)
gradcheck(fn, torch.rand(10, dtype=torch.double).to_sparse().requires_grad_(True), check_sparse_nnz=True,
check_batched_grad=False, fast_mode=fast_mode)
with self.assertRaisesRegex(RuntimeError, 'gradcheck expects all tensor inputs are dense'):
gradcheck(fn, torch.rand(10, dtype=torch.double).to_sparse().requires_grad_(True), check_sparse_nnz=False,
check_batched_grad=False, fast_mode=fast_mode)
check(fast_mode=True)
check(fast_mode=False)
@unittest.expectedFailure
def test_gradcheck_sparse_csr_input(self):
def check(fast_mode):
def fn(sparse_csr):
return torch.clone(sparse_csr).to_dense()
# Fails because gradcheck can't work with sparse csr inputs yet
gradcheck(fn, torch.rand(2, 2, dtype=torch.double).to_sparse_csr().requires_grad_(True), check_sparse_nnz=True,
check_batched_grad=False, fast_mode=fast_mode)
with self.assertRaisesRegex(RuntimeError, 'gradcheck expects all tensor inputs are dense'):
gradcheck(fn, torch.rand(2, 2, dtype=torch.double).to_sparse_csr().requires_grad_(True), check_sparse_nnz=False,
check_batched_grad=False, fast_mode=fast_mode)
# check(fast_mode=True) # Segmentation fault
check(fast_mode=False)
def test_gradcheck_nondeterministic(self):
class NonDetFunc(Function):
@staticmethod
def forward(ctx, x, jitter=0.0):
ctx._jitter = jitter
return x
@staticmethod
def backward(ctx, grad_out):
return NonDetFunc.apply(grad_out, ctx._jitter) * (1 + torch.rand_like(grad_out) * ctx._jitter), None
def check(fast_mode):
inp = torch.randn(5, 5, dtype=torch.double, requires_grad=True)
gradcheck(lambda x: NonDetFunc.apply(x, 0.0), inp, check_batched_grad=False, fast_mode=fast_mode)
with self.assertRaisesRegex(RuntimeError, 'Backward is not reentrant'):
gradcheck(lambda x: NonDetFunc.apply(x, 1e-6), inp, check_batched_grad=False, fast_mode=fast_mode)
with self.assertRaisesRegex(RuntimeError, 'Backward is not reentrant'):
gradgradcheck(lambda x: NonDetFunc.apply(x, 1e-12), inp, check_batched_grad=False, fast_mode=fast_mode)
gradcheck(lambda x: NonDetFunc.apply(x, 0.0), inp, nondet_tol=1e-5, check_batched_grad=False,
fast_mode=fast_mode)
gradcheck(lambda x: NonDetFunc.apply(x, 1e-6), inp, nondet_tol=1e-5, check_batched_grad=False,
fast_mode=fast_mode)
gradgradcheck(lambda x: NonDetFunc.apply(x, 1e-12), inp, nondet_tol=1e-5, check_batched_grad=False,
fast_mode=fast_mode)
check(fast_mode=True)
check(fast_mode=False)
def test_gradcheck_validates_inputs(self):
def check(fast_mode):
# when inputs are not dense, but check_sparse_nnz is false
x = torch.rand(10, requires_grad=True).to_sparse()
with self.assertRaisesRegex(RuntimeError, 'dense when check_sparse_nnz is set to False.'):
gradcheck(lambda x: x.to_dense(), (x,), check_sparse_nnz=False, check_batched_grad=False,
fast_mode=fast_mode)
self.assertFalse(gradcheck(lambda x: x.to_dense(), (x,), check_sparse_nnz=False,
check_batched_grad=False, raise_exception=False, fast_mode=fast_mode))
# when none of the inputs require grad (always raises even if raise_exception=False)
x = torch.rand(10, requires_grad=False)
with self.assertRaisesRegex(ValueError, 'at least one input tensor to require gradient'):
gradcheck(lambda x: x, (x,), raise_exception=False, fast_mode=fast_mode)
# (warning) when inputs are not double precision
x = torch.ones(1, dtype=torch.float32, requires_grad=True)
with self.assertWarnsRegex(UserWarning, "Input #0 requires gradient and is not a double precision"):
self.assertTrue(gradcheck(lambda x: x, (x,), atol=1e-1, fast_mode=fast_mode))
# when layout is not mkldnn(aka has strides) and input has a dimension with stride 0. (always raises
# even if raise_exception=False)
x = torch.ones(1, dtype=torch.float64, requires_grad=True)
x = x.expand((2, 2))
with self.assertRaisesRegex(RuntimeError, 'The 0th input has a dimension with stride 0'):
gradcheck(lambda x: x, (x,), raise_exception=False, fast_mode=fast_mode)
check(fast_mode=True)
check(fast_mode=False)
@unittest.skipIf(not torch._C.has_mkldnn, "MKL-DNN build is disabled")
def test_gradcheck_validates_input_mkldnn(self):
# when mkldnn inputs, forward mode testing is not allowed
# Update tolerances below to make sure the gradient match even in single precision floats
# Use the warning assert to hide the float32 warning
x = torch.ones(1).to_mkldnn().requires_grad_()
with self.assertWarnsRegex(UserWarning, "Input #0 requires gradient and is not a double precision"):
with self.assertRaisesRegex(ValueError, 'MKLDNN inputs are not support for forward AD gradcheck.'):
gradcheck(lambda x: x.to_dense(), (x,), raise_exception=False, fast_mode=False, check_forward_ad=True,
atol=1e-1, rtol=1e-1)
with self.assertWarnsRegex(UserWarning, "Input #0 requires gradient and is not a double precision"):
with self.assertRaisesRegex(ValueError, 'MKLDNN inputs are not support for forward AD gradcheck.'):
gradcheck(lambda x: x.to_dense(), (x,), raise_exception=False, fast_mode=True, check_forward_ad=True,
atol=1e-1, rtol=1e-1)
@unittest.skipIf(not torch._C.has_mkldnn, "MKL-DNN build is disabled")
def test_gradcheck_test_outputs(self):
def check(fast_mode):
# when sparse outputs (always raise even if raise_exception=False)
x = torch.rand(10, requires_grad=True).to_sparse()
with self.assertRaisesRegex(ValueError, 'Sparse output is not supported at gradcheck yet'):
gradcheck(lambda x: x, (x,), check_sparse_nnz=True, check_batched_grad=False, raise_exception=False,
fast_mode=fast_mode)
# when mkldnn outputs (always raise even if raise_exception=False)
root = torch.randn(4, 5, dtype=torch.float32, requires_grad=True)
with self.assertRaisesRegex(ValueError, 'MKLDNN output is not supported at gradcheck yet'):
gradcheck(lambda x: x.to_mkldnn(), (root,), check_batched_grad=False, raise_exception=False, fast_mode=fast_mode)
check(fast_mode=True)
check(fast_mode=False)
def test_gradcheck_check_no_differentiable_outputs(self):
def check(fast_mode):
# When none of the outputs are differentiable, but numerical gradient is not zero
x = torch.ones((1,), requires_grad=True)
with self.assertRaisesRegex(RuntimeError, 'Numerical gradient for function expected to be zero'):
gradcheck(lambda x: torch.tensor([x]), x)
self.assertFalse(gradcheck(lambda x: torch.tensor([x]), x, raise_exception=False, fast_mode=fast_mode))
# succeed when no outputs at all
self.assertTrue(gradcheck(lambda x: (), (x,), fast_mode=fast_mode))
check(fast_mode=True)
check(fast_mode=False)
def test_gradcheck_check_batched_grad(self):
def check(fast_mode):
x = torch.rand(10, dtype=torch.double, requires_grad=True).to_sparse()
# runtime error while compute batched grad (print big error)
with self.assertRaisesRegex(RuntimeError, 'gradcheck or gradgradcheck failed while testing batched gradient'):
gradcheck(lambda x: x.to_dense(), (x,), check_sparse_nnz=True, check_batched_grad=True, fast_mode=fast_mode)
self.assertFalse(gradcheck(lambda x: x.to_dense(), (x,), check_sparse_nnz=True, check_batched_grad=True,
raise_exception=False, fast_mode=fast_mode))
check(fast_mode=True)
check(fast_mode=False)
def test_gradcheck_backward_mul_by_grad_output(self):
# when grad_input is sparse and has incorrect sparse_dim/dense_dim
def check(fast_mode):
def fn(x):
def hook(grad):
if grad is not None:
return grad.to_dense().to_sparse(1)
return grad
y = x.clone()
y.register_hook(hook)
return y.to_dense()
x = torch.ones((2, 2), dtype=torch.double, requires_grad=True).to_sparse()
with self.assertRaisesRegex(RuntimeError, 'grad is sparse tensor, but has incorrect sparse_dim'):
gradcheck(fn, (x,), atol=1e-1, check_sparse_nnz=True, check_batched_grad=False, fast_mode=fast_mode)
self.assertFalse(gradcheck(fn, (x,), atol=1e-1, check_sparse_nnz=True, check_batched_grad=False,
raise_exception=False, fast_mode=fast_mode))
# when backward not multiplied by grad_output (non-sparse case)
def fn2(x):
y = x.clone()
y.register_hook(lambda x: x + 1e-2)
return y
x = torch.ones(1, dtype=torch.double, requires_grad=True)
with self.assertRaisesRegex(RuntimeError, 'backward not multiplied by grad_output'):
gradcheck(fn2, (x,), atol=1e-1, fast_mode=fast_mode)
self.assertFalse(gradcheck(fn2, (x,), atol=1e-1, raise_exception=False, fast_mode=fast_mode))
# when backward not multiplied by grad_output (sparse case)
def fn3(x):
y = x.clone().to_dense()
y.register_hook(lambda x: x + 1e-2)
return y
x = torch.ones(1, dtype=torch.double, requires_grad=True).to_sparse()
with self.assertRaisesRegex(RuntimeError, 'backward not multiplied by grad_output'):
gradcheck(fn3, (x,), atol=1e-1, check_sparse_nnz=True, check_batched_grad=False, fast_mode=fast_mode)
self.assertFalse(gradcheck(fn3, (x,), atol=1e-1, check_sparse_nnz=True, check_batched_grad=False,
raise_exception=False, fast_mode=fast_mode))
# when layout of grad_input is not the same as input
class Test(Function):
@staticmethod
def forward(ctx, x):
return x
@staticmethod
def backward(ctx, x):
return x.to_sparse()
x = torch.ones(1, dtype=torch.double, requires_grad=True)
with self.assertRaisesRegex(RuntimeError, 'grad is incorrect layout'):
gradcheck(Test.apply, (x,), check_batched_grad=False, fast_mode=fast_mode)
self.assertFalse(gradcheck(Test.apply, (x,), check_batched_grad=False, raise_exception=False, fast_mode=fast_mode))
check(fast_mode=True)
check(fast_mode=False)
def test_gradcheck_undefined_grad(self):
def check(fast_mode):
# when encounter runtime error while running backward
def fn(x):
def hook(x):
if x is None:
raise RuntimeError("x is undefined")
y = x.clone()
y.register_hook(hook)
return y
x = torch.ones(1, dtype=torch.double, requires_grad=True)
with self.assertWarnsRegex(UserWarning, "Backwards compatibility: New undefined gradient support checking feature"):
with self.assertRaisesRegex(RuntimeError, 'Expected backward function to handle undefined output grads'):
gradcheck(fn, (x,), fast_mode=fast_mode)
self.assertFalse(gradcheck(fn, (x,), raise_exception=False, fast_mode=fast_mode))
check(fast_mode=True)
check(fast_mode=False)
def test_gradcheck_jacobian_mismatch(self):
def check(fast_mode):
def fn(x): # R -> R, C -> C
y = x.clone()
y.register_hook(lambda x: x + 1e-2)
return y
x = torch.ones(2, 2, requires_grad=True)
with self.assertRaisesRegex(RuntimeError, 'Jacobian mismatch for output 0 with respect to input 0'):
gradcheck(fn, (x,), fast_mode=fast_mode)
self.assertFalse(gradcheck(fn, (x,), raise_exception=False, fast_mode=fast_mode))
x_c = torch.ones(2, 2, requires_grad=True, dtype=torch.complex128)
with self.assertRaisesRegex(RuntimeError, 'While considering the imaginary part of complex outputs only'):
gradcheck(fn, (x_c,), fast_mode=False)
self.assertFalse(gradcheck(fn, (x_c,), raise_exception=False, fast_mode=False))
def fn2(x): # R -> C
y = torch.complex(x, x)
y.register_hook(lambda x: x + 1e-2)
return y
x = torch.ones(2, 2, requires_grad=True)
with self.assertRaisesRegex(RuntimeError, 'While considering the imaginary part of complex outputs only'):
gradcheck(fn2, (x,), fast_mode=False)
self.assertFalse(gradcheck(fn2, (x,), raise_exception=False, fast_mode=False))
def fn3(x): # C -> R
y = torch.real(x)
y.register_hook(lambda x: x + 1e-2)
return y
with self.assertRaisesRegex(RuntimeError, 'Jacobian mismatch for output 0 with respect to input 0'):
gradcheck(fn3, (x_c,), fast_mode=False)
self.assertFalse(gradcheck(fn3, (x_c,), raise_exception=False, fast_mode=False))
check(fast_mode=True)
check(fast_mode=False)
def test_gradcheck_dense_and_sparse_inputs(self):
def check(fast_mode):
def fn(x, y):
return x * y.coalesce().to_dense()
a = torch.rand(2, 2, dtype=torch.double, requires_grad=True)
b = torch.rand(2, 2, dtype=torch.double,).to_sparse().requires_grad_(True)
self.assertTrue(gradcheck(fn, (a, b), check_sparse_nnz=True, check_batched_grad=False, fast_mode=fast_mode))
check(fast_mode=True)
check(fast_mode=False)
@unittest.skipIf(not torch._C.has_mkldnn, "MKL-DNN build is disabled")
def test_gradcheck_multiple_mkldnn_inputs(self):
def check(fast_mode):
def fn(x, y):
return x + y.to_dense()
a = torch.rand(10, requires_grad=True)
b = torch.rand(10, dtype=torch.float32).to_mkldnn().requires_grad_(True)
self.assertTrue(gradcheck(fn, (a, b), atol=1e-1, check_batched_grad=False, fast_mode=fast_mode))
def fn2(x, y):
return x.to_dense() + y.to_dense()
c = torch.rand(10, dtype=torch.float32).to_mkldnn().requires_grad_(True)
self.assertTrue(gradcheck(fn, (a, c), atol=1e-1, check_batched_grad=False, fast_mode=fast_mode))
check(fast_mode=True)
check(fast_mode=False)
def test_gradcheck_output_shape_or_dtype_depend_on_values(self):
def check(fast_mode):
def fn(x):
if torch.all(x >= 1):
return torch.cat([x, x])
else:
return x
a = torch.ones(1, dtype=torch.double, requires_grad=True)
with self.assertRaisesRegex(AssertionError, 'return outputs with the same shape when inputs are perturbed'):
self.assertTrue(gradcheck(fn, (a,), fast_mode=fast_mode))
def fn2(x):
if torch.all(x >= 1):
return x.to(torch.float32)
else:
return x
with self.assertRaisesRegex(AssertionError, 'return outputs with the same dtype when inputs are perturbed'):
self.assertTrue(gradcheck(fn2, (a,), fast_mode=fast_mode))
check(fast_mode=True)
check(fast_mode=False)
def test_gradcheck_complex_non_complex_outputs(self):
def fn(x, y):
z = torch.complex(x, y)
return z, x + 1
a = torch.ones(2, 2, requires_grad=True, dtype=torch.float64)
b = torch.ones(2, 2, requires_grad=True, dtype=torch.float64)
self.assertTrue(gradcheck(fn, (a, b)))
def fn2(z):
return z, torch.real(z)
c = torch.ones(2, 2, requires_grad=True, dtype=torch.complex128)
self.assertTrue(gradcheck(fn2, (c)))
def test_gradcheck_get_numerical_jacobian(self):
# get_numerical_jacobian is deprecated and no longer used internally by gradcheck
from torch.autograd.gradcheck import get_numerical_jacobian
def fn(inputs):
# get_numerical_jacobian requires fn to take inputs as a tuple
# and returns the jacobian wrt the first output
x = inputs[0]
y = inputs[1]
return 2 * x + y, x + 2 * y
a = torch.rand(2, 2, requires_grad=True, dtype=torch.float64)
b = torch.rand(2, 2, requires_grad=True, dtype=torch.float64)
with self.assertWarnsRegex(UserWarning, "get_numerical_jacobian was part of PyTorch's private API"):
jacobian = get_numerical_jacobian(fn, (a, b), target=a, eps=1e-6)
self.assertEqual(jacobian[0], 2 * torch.eye(4, dtype=torch.double))
with self.assertWarnsRegex(UserWarning, "get_numerical_jacobian was part of PyTorch's private API"):
jacobian = get_numerical_jacobian(fn, (a, b), eps=1e-6)
self.assertEqual(jacobian[0], 2 * torch.eye(4, dtype=torch.double))
self.assertEqual(jacobian[1], 1 * torch.eye(4, dtype=torch.double))
with self.assertRaisesRegex(ValueError, "Expected grad_out to be 1.0"):
jacobian = get_numerical_jacobian(fn, (a, b), eps=1e-6, grad_out=2.0)
def test_gradcheck_get_analytical_jacobian(self):
from torch.autograd.gradcheck import get_analytical_jacobian
def fn(x, y):
return 2 * x + y, x + 2 * y
a = torch.rand(2, 2, requires_grad=True, dtype=torch.float64)
b = torch.rand(2, 2, requires_grad=True, dtype=torch.float64)
outputs = fn(a, b)
with self.assertWarnsRegex(UserWarning, "get_analytical_jacobian was part of PyTorch's private API"):
jacobians, reentrant, correct_grad_sizes, correct_grad_types = get_analytical_jacobian((a, b), outputs[0])
self.assertEqual(jacobians[0], 2 * torch.eye(4, dtype=torch.double))
self.assertEqual(jacobians[1], 1 * torch.eye(4, dtype=torch.double))
self.assertTrue(reentrant)
class NonDetFunc(Function):
@staticmethod
def forward(ctx, x, jitter=0.0):
ctx._jitter = jitter
return x
@staticmethod
def backward(ctx, grad_out):
return NonDetFunc.apply(grad_out, ctx._jitter) * (1 + torch.rand_like(grad_out) * ctx._jitter), None
outputs = NonDetFunc.apply(a, 1e-6)
with self.assertWarnsRegex(UserWarning, "get_analytical_jacobian was part of PyTorch's private API"):
jacobians, reentrant, correct_grad_sizes, correct_grad_types = get_analytical_jacobian((a,), outputs)
self.assertFalse(reentrant)
with self.assertRaisesRegex(ValueError, "Expected grad_out to be 1.0"):
jacobians, _, _, _ = get_analytical_jacobian((a,), outputs, grad_out=2.0)
def test_gradcheck_custom_error(self):
from torch.autograd.gradcheck import GradcheckError
def check(fast_mode):
def fn(x):
y = x.clone()
y.register_hook(lambda x: x + 1e-2)
return y
x = torch.ones(2, 2, requires_grad=True)
with self.assertRaisesRegex(GradcheckError, 'Jacobian mismatch for output 0 with respect to input 0'):
gradcheck(fn, (x,), fast_mode=fast_mode)
with self.assertRaisesRegex(RuntimeError, 'Jacobian mismatch for output 0 with respect to input 0'):
gradcheck(fn, (x,), fast_mode=fast_mode)
self.assertFalse(gradcheck(fn, (x,), raise_exception=False, fast_mode=fast_mode))
def fn2(x):
raise RuntimeError("Not a GradcheckError!")
# Checks that when raise_exception=False, non-GradcheckErrors are not caught by gradcheck
with self.assertRaisesRegex(RuntimeError, "Not a GradcheckError!"):
gradcheck(fn2, (x,), fast_mode=fast_mode, raise_exception=False)
check(fast_mode=True)
check(fast_mode=False)
def test_gradcheck_forward_ad(self):
def fn(x, y):
return x + y, y
def bad_fn(x, y):
# Hacky way to check if we're currently inside a forward ad level
is_running_forward_ad = fwAD._current_level >= 0
if is_running_forward_ad:
y_p, y_d = fwAD.unpack_dual(y)
y = fwAD.make_dual(y_p, y_d * 1.1)
return x + y, y
err_msg = "Jacobian computed with forward mode mismatch for output 0 with respect to input 1"
for fast_mode in [True, False]:
# Test for all inputs and outputs being real
x = torch.rand(2, dtype=torch.double, requires_grad=True)
y = torch.rand(2, dtype=torch.double, requires_grad=True)
gradcheck(fn, (x, y), check_forward_ad=True, fast_mode=fast_mode)
with self.assertRaisesRegex(RuntimeError, err_msg):
gradcheck(bad_fn, (x, y), check_forward_ad=True, fast_mode=fast_mode)
def basic_mul(x):
return torch.view_as_real(torch.resolve_conj(x * 1j))
gradcheck(basic_mul, x, check_forward_ad=True, fast_mode=fast_mode)
# Test for one input and one output being complex
x = torch.rand(2, dtype=torch.cdouble, requires_grad=True)
gradcheck(fn, (x, y), check_forward_ad=True, fast_mode=fast_mode)
with self.assertRaisesRegex(RuntimeError, err_msg):
gradcheck(bad_fn, (x, y), check_forward_ad=True, fast_mode=fast_mode)
# Test for all inputs and outputs being complex
y = torch.rand(2, dtype=torch.cdouble, requires_grad=True)
gradcheck(fn, (x, y), check_forward_ad=True, fast_mode=fast_mode)
with self.assertRaisesRegex(RuntimeError, err_msg):
gradcheck(bad_fn, (x, y), check_forward_ad=True, fast_mode=fast_mode)
def test_gradcheck_forward_ad_runs_with_no_requires_grad(self):
# Currently requires_grad is used as a easy way for gradcheck to know
# which inputs of the function are meant to be differentiable
# This test checks that when the inputs are passed to the function they should not have
# requires_grad=True even though they may have requires_grad=True when passed
# to gradcheck
class UserFn(Function):
@staticmethod
def forward(ctx, x, y):
if fwAD._current_level >= 0:
self.assertFalse(x.requires_grad)
self.assertFalse(y.requires_grad)
return x.clone(), y.clone()
@staticmethod
def jvp(ctx, x_t, y_t):
return x_t, y_t
x = torch.rand(2, dtype=torch.double, requires_grad=True)
y = torch.rand(2, dtype=torch.double, requires_grad=True)
gradcheck(UserFn.apply, (x, y), check_forward_ad=True, check_undefined_grad=False, check_backward_ad=False,
check_batched_grad=False, check_batched_forward_grad=False)
gradcheck(UserFn.apply, (x, y), check_forward_ad=True, check_undefined_grad=True, check_backward_ad=False,
check_batched_grad=False, check_batched_forward_grad=False)
gradcheck(UserFn.apply, (x, y), check_forward_ad=True, check_undefined_grad=True, check_backward_ad=False,
check_batched_grad=False, check_batched_forward_grad=True)
x = torch.rand(2, dtype=torch.double, requires_grad=True)
y = torch.rand(2, dtype=torch.double, requires_grad=False)
gradcheck(UserFn.apply, (x, y), check_forward_ad=True, check_undefined_grad=True, check_backward_ad=False,
check_batched_grad=False, check_batched_forward_grad=True)
def test_gradcheck_forward_ad_respects_requires_grad(self):
# Currently requires_grad is used as a easy way for gradcheck to know
# which inputs of the function are meant to be differentiable
jvp_count = [0]
class UserFn(Function):
@staticmethod
def forward(ctx, x, y):
return x.clone(), y.clone()
@staticmethod
def jvp(ctx, x_t, y_t):
jvp_count[0] += 1
return x_t, y_t
# NB: In slow gradcheck we need to loop through numel times so use numel = 1 to ensure
# that fast and slow have the same counts
x = torch.rand(1, dtype=torch.double, requires_grad=True)
y = torch.rand(1, dtype=torch.double, requires_grad=True)
gradcheck(UserFn.apply, (x, y), check_forward_ad=True, check_undefined_grad=False, check_backward_ad=False,
check_batched_grad=False, check_batched_forward_grad=False)
self.assertEqual(jvp_count[0], 2) # (2) once per input
jvp_count = [0]
gradcheck(UserFn.apply, (x, y), check_forward_ad=True, check_undefined_grad=True, check_backward_ad=False,
check_batched_grad=False, check_batched_forward_grad=False)
self.assertEqual(jvp_count[0], 6) # (+4): (once with normal ZT (+1), once with efficient ZT (+1)) for each input (x2)
jvp_count = [0]
gradcheck(UserFn.apply, (x, y), check_forward_ad=True, check_undefined_grad=True, check_backward_ad=False,
check_batched_grad=False, check_batched_forward_grad=True)
self.assertEqual(jvp_count[0], 12) # (+6): (compute batch of 2 with vmap (+1), with a loop (+2)) for each input (x2)
jvp_count = [0]
# Repeat the previous test except we mark one input with requires_grad=False
# NB: _test_undefined_forward_mode is only (+1), when function has single differentiable input, not (+2)!
# Otherwise, other counts are halved.
x = torch.rand(1, dtype=torch.double, requires_grad=True)
y = torch.rand(1, dtype=torch.double, requires_grad=False)
gradcheck(UserFn.apply, (x, y), check_forward_ad=True, check_undefined_grad=True, check_backward_ad=False,
check_batched_grad=False, check_batched_forward_grad=True)
self.assertEqual(jvp_count[0], 5) # 1 + 1 + 3
def test_gradcheck_check_forward_or_backward_only(self):
"""Depending on settings for check_forward_ad and check_backward_ad, the
correct codepaths should be reached (or not reached)
"""
fwd_fail_err_msg = "FAIL FWD"
bwd_fail_err_msg = "FAIL BWD"
class UserFn(Function):
@staticmethod
def forward(ctx, foo, fwd_bad, bwd_bad):
ctx.fwd_bad = fwd_bad
ctx.bwd_bad = bwd_bad
return foo * 2
@staticmethod
def vjp(ctx, gO):
if ctx.bwd_bad:
raise RuntimeError(bwd_fail_err_msg)
else:
return 2 * gO, None, None
@staticmethod
def jvp(ctx, gI, _1, _2):
if ctx.fwd_bad:
raise RuntimeError(fwd_fail_err_msg)
else:
return 2 * gI
for fast_mode in (True, False):
for check_forward_ad in (True, False):
for check_backward_ad in (True, False):
for fwd_bad in (True, False):
for bwd_bad in (True, False):
fwd_should_fail = fwd_bad and check_forward_ad
bwd_should_fail = bwd_bad and check_backward_ad
def run():
gradcheck(UserFn.apply, (x, fwd_bad, bwd_bad), check_forward_ad=check_forward_ad,
check_backward_ad=check_backward_ad, check_undefined_grad=check_backward_ad,
check_batched_grad=check_backward_ad, fast_mode=fast_mode)
x = torch.rand(2, dtype=torch.double, requires_grad=True)
if not check_forward_ad and not check_backward_ad:
with self.assertRaisesRegex(AssertionError, "Expected at least one of"):
run()
continue
if not fwd_should_fail and not bwd_should_fail:
run()
else:
# If both fail, backward AD failure "hides" forward AD failure
if fwd_should_fail:
fail_msg = fwd_fail_err_msg
if bwd_should_fail:
fail_msg = bwd_fail_err_msg
with self.assertRaisesRegex(RuntimeError, fail_msg):
run()
def test_gradcheck_forward_ad_batched_grad(self):
x = torch.rand(2, dtype=torch.double, requires_grad=True)
# multiple inputs and outputs with non-tensors inputs
def fn1(a: torch.Tensor, b: int):
return a.clone(), a + 1
gradcheck(fn1, (x, 1), check_forward_ad=True, check_backward_ad=False, check_batched_grad=False,
check_undefined_grad=False, check_batched_forward_grad=True)
# unrelated inputs: tangent for c is None
def fn2(a: torch.Tensor, c: torch.Tensor):
return a.clone()
gradcheck(fn2, (x, x.clone()), check_forward_ad=True, check_backward_ad=False, check_batched_grad=False,
check_undefined_grad=False, check_batched_forward_grad=True)
class Fn(Function):
@staticmethod
def forward(ctx, foo):
return foo * 2
@staticmethod
def vjp(ctx, gO):
return gO * 2
@staticmethod
def jvp(ctx, gI):
torch.randn_like(gI)
return gI * 2
msg = "vmap: We do not yet support calling random operations inside of vmap"
with self.assertRaisesRegex(RuntimeError, msg):
gradcheck(Fn.apply, (x,), check_forward_ad=True, check_batched_forward_grad=True)
def test_version_counter(self):
x = torch.randn(1, 2)
# In-place op bumps version
x_saved_version = x._version
x.add_(1).add_(1)
self.assertTrue(x._version > x_saved_version)
# Differentiable view shares version counter
xz = x[:]
self.assertTrue(x._version == xz._version)
xz.add_(1)
self.assertTrue(x._version == xz._version)
# `x.data = y` preserves version counter of `x`
x_saved_version = x._version
x.data = torch.randn(2, 3)
self.assertTrue(x._version == x_saved_version)
x.add_(1)
self.assertTrue(x._version > x_saved_version)
# Make sure `x` is still using the same version counter it shares with `xz`
self.assertTrue(x._version == xz._version)
# In-place op on `xz` also updates version of `x`,
# because they share the version counter
xz.add_(1)
self.assertTrue(x._version == xz._version)
def test_set_data_tensorimpl_type(self):
# Dense tensor has impl of type `TensorImpl`, while sparse tensor has impl
# of type `SparseTensorImpl`.
x = torch.randn(1, 2)
x_s = torch.sparse_coo_tensor(torch.zeros([1, 1]), torch.ones([1]))
with self.assertRaisesRegex(RuntimeError, 'incompatible tensor type'):
x.data = x_s
def test_set_data_preserve_pyobj(self):
a = torch.randn(1, 2)
b = torch.randn(1, 2)
b_id_saved = id(b)
b.data = a
self.assertTrue(b_id_saved == id(b))
def test_set_data_self_requires_grad(self):
a = torch.tensor(1.0, requires_grad=True)
b = torch.tensor(2.0)
c = torch.tensor(3, dtype=torch.int64)
a.data = b
with self.assertRaisesRegex(RuntimeError, 'must be floating point or complex dtype'):
a.data = c
@unittest.skipIf(IS_WINDOWS, "Skipping because doesn't work for windows")
def test_thread_shutdown(self):
code = """import torch
from torch.autograd import Function
class MyFunction(Function):
@staticmethod
def forward(ctx, x):
return x
@staticmethod
def backward(ctx, grad):
return grad
for shape in [(1,), ()]:
v = torch.ones(shape, requires_grad=True)
MyFunction.apply(v).backward()
"""
s = TestCase.runWithPytorchAPIUsageStderr(code)
# The autograd engine creates worker threads only when GPU devices are present.
# So make sure that we do shutdown threads when we're testing cuda and make sure
# that there is no thread to shutdown when we're not using cuda.
if TEST_CUDA or torch.backends.mps.is_available():
self.assertRegex(s, "PYTORCH_API_USAGE torch.autograd.thread_shutdown")
else:
self.assertNotRegex(s, "PYTORCH_API_USAGE torch.autograd.thread_shutdown")
@unittest.skipIf(IS_MACOS, "Fails with SIGBUS on macOS; https://github.com/pytorch/pytorch/issues/25941")
def test_deep_reentrant(self):
class DeepReentrant(Function):
@staticmethod
def forward(ctx, x):
with torch.enable_grad():
ctx.x = Variable(x.detach(), requires_grad=True)
ctx.x = ctx.x - 1
return ctx.x.detach()
@staticmethod
def backward(ctx, x):
if ctx.x < 0:
return x
with torch.enable_grad():
DeepReentrant.apply(ctx.x).sum().backward()
return x
# Test stack overflow escape mechanism
v = torch.tensor(2000.0, requires_grad=True)
# This will cause stack overflow if reentrant calls are handled
# in the same thread recursively
DeepReentrant.apply(v).sum().backward()
# Test stack overflow escape mechanism multiple times
# to ensure reusing workers in the pool works fine
v2 = torch.tensor(200.0, requires_grad=True)
DeepReentrant.apply(v2).sum().backward()
def test_reentrant_priority(self):
order = []
class MyFunction(Function):
@staticmethod
def forward(ctx, x):
return x
@staticmethod
def backward(ctx, x):
order.append("MyFunction")
return x
class Reentrant(Function):
@staticmethod
def forward(ctx, x):
with torch.enable_grad():
ctx.x = Variable(x.detach(), requires_grad=True)
ctx.x = ctx.x - 1
return ctx.x.detach()
@staticmethod
def backward(ctx, x):
order.append("Reentrant")
if ctx.x < 0:
return x
with torch.enable_grad():
Reentrant.apply(ctx.x).backward()
return x
a = MyFunction.apply(torch.tensor(6.0, requires_grad=True))
b = Reentrant.apply(torch.tensor(9.0, requires_grad=True))
v = a * b
v.backward()
# The tasks for the Reentrant and MyFunction backward() will be added
# to the queue in the autograd engine at the same time. The backward
# for Reentrant will be executed first, which will then add other
# backward tasks to the queue. We want to ensure all the reentrant tasks
# are prioritized over the MyFunction backward task regardless of their
# sequence numbers
self.assertEqual(len(order), 11)
self.assertEqual(order.count("Reentrant"), 10)
self.assertEqual(order[-1], "MyFunction")
@slowTest
def test_checkpointing(self):
num_inp = 2000
nz_inp = 10
nz_out = 10
nz_bottleneck = 1000
# small proxy network for some complex reasoning we want to do per input
module = nn.Sequential(
nn.Linear(nz_inp, nz_bottleneck),
nn.ReLU(),
nn.Linear(nz_bottleneck, nz_inp)
)
feat_combined = []
for r in range(num_inp):
data_r = torch.empty(1, nz_inp)
data_r.uniform_()
data_r.requires_grad = True
feat_r = checkpoint(module, data_r)
feat_combined.append(feat_r)
# compute mean as a proxy for some joint reasoning
mean_combined = torch.stack(feat_combined).mean()
mean_combined.backward()
@unittest.skipIf(not torch.cuda.is_available(), "Test requires CUDA")
@slowTest
def test_checkpointing_without_reentrant_memory_savings(self):
class MyModel(nn.Module):
def __init__(self, n, use_checkpoint, use_reentrant):
super().__init__()
self.n = n
self.use_checkpoint = use_checkpoint
self.use_reentrant = use_reentrant
self.layers = nn.ModuleList()
for i in range(self.n):
layer = nn.Sequential(
nn.Linear(256, 256), nn.Linear(256, 256), nn.Linear(256, 256)
)
self.layers.append(layer)
# pre-allocate the grad so that increased memory usage is mainly
# due to activations.
for layer in self.layers:
for lin in layer:
lin.weight.grad = torch.ones_like(lin.weight)
lin.bias.grad = torch.ones_like(lin.bias)
def forward(self, x):
for i in range(self.n):
if not self.use_checkpoint:
x = self.layers[i](x)
else:
x = checkpoint(self.layers[i], x, use_reentrant=self.use_reentrant)
return x
model_no_checkpoint = MyModel(8, use_checkpoint=False, use_reentrant=False).cuda()
model_reentrant_checkpoint = MyModel(8, use_checkpoint=True, use_reentrant=True).cuda()
model_no_reentrant_checkpoint = MyModel(8, use_checkpoint=True, use_reentrant=False).cuda()
x = torch.randn(100, 256, requires_grad=True, device='cuda')
torch.cuda.reset_peak_memory_stats()
loss = model_no_checkpoint(x.clone()).sum()
loss.backward()
mem_no_checkpoint = torch.cuda.max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
loss = model_reentrant_checkpoint(x.clone()).sum()
loss.backward()
mem_reentrant_checkpoint = torch.cuda.max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
loss = model_no_reentrant_checkpoint(x.clone()).sum()
loss.backward()
mem_no_reentrant_checkpoint = torch.cuda.max_memory_allocated()
self.assertTrue(mem_reentrant_checkpoint < mem_no_checkpoint)
self.assertTrue(mem_no_reentrant_checkpoint < mem_no_checkpoint)
def test_checkpointing_without_reentrant_custom_function_raises(self):
"""
Accessing ctx.saved_tensors multiple times in a custom function
backward pass with non-reentrant checkpoint currently throws due to
saved tensors not being recomputed in between the accesses.
"""
# For verifying first access to ctx.saved_tensors succeeded.
_first_saved_tensor_access_succeeded = False
class MyFunc(torch.autograd.Function):
@staticmethod
def forward(ctx, x, y, z):
w = x * y * z
out = w + w
ctx.save_for_backward(x, y, z, w, out)
return out
@staticmethod
def backward(ctx, grad_out):
x, y, z, w, out = ctx.saved_tensors
nonlocal _first_saved_tensor_access_succeeded
_first_saved_tensor_access_succeeded = True
# Raises issue in non-reentrant checkpointing where
# second access to saved tensors raises because they were
# not recomputed.
x_2, y_2, z_2, w_2, out_2 = ctx.saved_tensors
x = torch.tensor(1., requires_grad=True)
y = torch.tensor(2., requires_grad=True)
z = torch.tensor(3., requires_grad=True)
def foo(x, y, z):
x = x * y * z
y = y * y * z
z = z * z
out = MyFunc.apply(x, y, z)
return out
out = checkpoint(foo, x, y, z, use_reentrant=False)
with self.assertRaisesRegex(
RuntimeError,
"Attempt to retrieve a tensor saved by autograd multiple times"
):
out.sum().backward()
self.assertTrue(_first_saved_tensor_access_succeeded)
def test_access_saved_tensor_twice_without_recomputation_raises(self):
"""
If using saved tensor hooks based checkpointing and a saved tensor
is accessed multiple times without triggering recomputation in the
middle, error is raised indicating so.
"""
def foo(a):
b = a * a
c = a * b
d = torch.exp(a)
return d
a = torch.randn(5, requires_grad=True)
d = checkpoint(foo, a, use_reentrant=False)
# First access
d.grad_fn._saved_result
# Second access raises error
with self.assertRaisesRegex(
RuntimeError,
"Attempt to retrieve a tensor saved by autograd multiple times"
):
d.grad_fn._saved_result
@slowTest
@parametrize("input_requires_grad", [True, False])
def test_checkpointing_without_reentrant(self, input_requires_grad):
"""
Basic test for checkpoint without reentrant autograd.
"""
num_inp = 2000
nz_inp = 10
nz_out = 10
nz_bottleneck = 1000
# small proxy network for some complex reasoning we want to do per input
module = nn.Sequential(
nn.Linear(nz_inp, nz_bottleneck),
nn.ReLU(),
nn.Linear(nz_bottleneck, nz_inp)
)
# Module holder for testing activation checkpointing with no_reentrant
# supports kwargs.
class MyModule(nn.Module):
def __init__(self, mod):
super().__init__()
self.module = mod
def forward(self, data):
return self.module(data)
module = MyModule(mod=module)
# Run model with and without checkpointing and verify gradients are
# equivalent, regardless of if inputs require grads or not.
module_copy = deepcopy(module)
feat_combined = []
feat_combined_no_checkpoint = []
for r in range(num_inp):
data_r = torch.empty(1, nz_inp)
data_r.uniform_()
data_r.requires_grad = input_requires_grad
data_r_copy = data_r.clone()
feat_r = checkpoint(module, data=data_r, use_reentrant=False)
feat_combined.append(feat_r)
feat_r_no_checkpoint = module_copy(data_r)
feat_combined_no_checkpoint.append(feat_r_no_checkpoint)
# compute mean as a proxy for some joint reasoning
mean_combined = torch.stack(feat_combined).mean()
mean_combined.backward()
mean_combined_no_checkpoint = torch.stack(feat_combined_no_checkpoint).mean()
mean_combined_no_checkpoint.backward()
for checkpoint_param, param in zip(module.parameters(), module_copy.parameters()):
self.assertEqual(checkpoint_param.grad, param.grad)
def test_checkpoint_valid_reset_on_error(self):
a = torch.randn(2, 2, requires_grad=True)
with self.assertRaisesRegex(Exception, "Checkpointing is not compatible with .grad()"):
b = checkpoint(torch.exp, a).sum()
torch.autograd.grad(b, (a,))
c = checkpoint(torch.exp, a).sum()
c.backward()
@parametrize("use_reentrant", [True, False])
def test_checkpointing_without_reentrant_detached_tensor(self, use_reentrant):
class NoGradModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear = nn.Linear(2, 2, bias=False)
self.lin2 = nn.Linear(2, 2, bias=False)
def forward(self, x):
with torch.no_grad():
return self.lin2(self.linear(x))
module = NoGradModule()
err_ctx = (
self.assertRaisesRegex(
RuntimeError,
"none of output has requires_grad=True"
)
if use_reentrant
else contextlib.suppress()
)
a = torch.randn(2, 2, requires_grad=True)
for _ in range(3):
with err_ctx:
# out does not require grad
out = checkpoint(module, a, use_reentrant=use_reentrant)
# Make loss require grad, otherwise we would run into
# "element 0 of tensors does not require grad and does not have a grad_fn"
out += a
out.sum().backward()
def test_checkpointing_without_reentrant_correct_grad(self):
"""
Verifies that correct gradients are calculated for checkpoint
without reentrant autograd, for both backward() and autograd.grad().
"""
a = torch.randn(2, 2, requires_grad=True)
b = torch.exp(a).sum()
b.backward()
b_grad = a.grad
a.grad = None
c = checkpoint(torch.exp, a, use_reentrant=False).sum()
c.backward()
c_grad = a.grad
a.grad = None
d = checkpoint(torch.exp, a, use_reentrant=False).sum()
d_grad, = torch.autograd.grad(d, (a,))
self.assertEqual(b_grad, c_grad)
self.assertEqual(b_grad, d_grad)
def test_checkpointing_without_reentrant_dataparallel(self):
"""
Verifies gradient correctness when checkpoint without reentrant autograd
is used in conjunction with DataParallel.
"""
class LinearModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear = nn.Linear(2, 2, bias=False)
def forward(self, inp):
return self.linear(inp)
a = torch.randn(2, 2, requires_grad=True)
if torch.cuda.is_available():
a = a.cuda()
model = LinearModule()
if torch.cuda.is_available():
model = model.cuda()
b = deepcopy(model)(a).sum()
b.backward()
b_grad = a.grad
a.grad = None
module = torch.nn.DataParallel(deepcopy(model))
c = checkpoint(module, a, use_reentrant=False).sum()
c.backward()
c_grad = a.grad
self.assertEqual(b_grad, c_grad)
def test_checkpointing_without_reentrant_parameter_used_in_an_out(self):
"""
Ensures that gradient hooks are only called once per tensor.
"""
w = torch.randn(10, 10, requires_grad=True)
count = 0
def hook(grad):
nonlocal count
count += 1
w.register_hook(hook)
x = torch.rand(10, 10, requires_grad=True)
h = w * x # Using w outside the checkpoint
out = checkpoint(lambda x: w * x, h, use_reentrant=False) # Using w inside the checkpoint
out.sum().backward()
# should only call hook once
self.assertEqual(count, 1)
def test_checkpointing_without_reentrant_arbitrary_input_output(self):
"""
Ensures checkpointing without reentrant autograd works with functions
with arbitrary input/output structures.
"""
class MyModel(torch.nn.Module):
def __init__(self):
super().__init__()
self.layer = torch.nn.Linear(5, 5, bias=False)
def forward(self, dict_input):
tensor = dict_input["tensor"]
return {
"result": self.layer(tensor)
}
model_no_checkpoint = MyModel()
model_checkpoint_without_reentrant = deepcopy(model_no_checkpoint)
inp = {
"tensor": torch.randn(5, 5)
}
out_no_checkpoint = model_no_checkpoint(inp)["result"].sum()
out_checkpoint = checkpoint(
model_checkpoint_without_reentrant,
inp,
use_reentrant=False
)["result"].sum()
self.assertEqual(out_checkpoint, out_no_checkpoint)
out_no_checkpoint.backward()
out_checkpoint.backward()
for param, checkpoint_param in zip(model_no_checkpoint.parameters(), model_checkpoint_without_reentrant.parameters()):
self.assertEqual(param.grad, checkpoint_param.grad)
def test_callback_adds_callback(self):
called = [0]
def callback_final():
called[0] += 1
def callback_adds_callback():
called[0] += 1
Variable._execution_engine.queue_callback(callback_final)
class MyFunc(Function):
@staticmethod
def forward(ctx, input):
return input
@staticmethod
@once_differentiable
def backward(ctx, grad):
Variable._execution_engine.queue_callback(callback_adds_callback)
return grad
a = torch.rand((3, 3), requires_grad=True)
b = MyFunc.apply(a)
b.sum().backward()
self.assertEqual(called[0], 2)
def _test_reentrant_with_callbacks(self, install_callbacks_in_depths):
counter = {}
counter["inner"] = 0
counter["outer"] = 0
def inc_inner_counter():
counter["inner"] += 1
def inc_outer_counter():
counter["outer"] += 1
class MyFunc(Function):
@staticmethod
def forward(ctx, input):
return input
@staticmethod
@once_differentiable
def backward(ctx, input):
if 1 in install_callbacks_in_depths:
# Add a callback to execute.
Variable._execution_engine.queue_callback(inc_inner_counter)
return input
class MyReentrantFunc(Function):
@staticmethod
def forward(ctx, input):
return input
@staticmethod
@once_differentiable
def backward(ctx, input):
if 0 in install_callbacks_in_depths:
# Add a callback to execute.
Variable._execution_engine.queue_callback(inc_outer_counter)
# Reentrant backward call.
tmp_inp = input.detach().requires_grad_()
with torch.enable_grad():
tmp_out = (MyFunc.apply(tmp_inp)).sum()
tmp_out.backward()
return input
t1 = torch.rand((3, 3), requires_grad=True)
t2 = MyReentrantFunc.apply(t1)
t3 = t2.sum()
torch.autograd.backward([t3])
return counter
def test_reentrant_with_callbacks_depth_0(self):
# Verify callback is called only once.
ret = self._test_reentrant_with_callbacks([0])
self.assertEqual(1, ret["outer"])
self.assertEqual(0, ret["inner"])
def test_reentrant_with_callbacks_depth_1(self):
# Verify callback is called only once.
ret = self._test_reentrant_with_callbacks([1])
self.assertEqual(0, ret["outer"])
self.assertEqual(1, ret["inner"])
def test_reentrant_with_callbacks_both_depths(self):
# Verify callback is called twice.
ret = self._test_reentrant_with_callbacks([0, 1])
self.assertEqual(1, ret["outer"])
self.assertEqual(1, ret["inner"])
def test_reentrant_with_leaf_variable_hook(self):
handle = None
param = torch.rand(10, requires_grad=True)
def add_gradient_penalty_to_grad(grad):
handle.remove()
old_param_grad = grad
param.grad = None
# Add some sort of gradient penalty by directly updating the gradients
with torch.enable_grad():
g = grad.detach().requires_grad_()
new_param = param.detach().requires_grad_()
out = ((g * 2) + new_param).sum()
out.backward()
res = g.grad + grad
param.grad = old_param_grad
return res
handle = param.register_hook(add_gradient_penalty_to_grad)
# Forward pass
tmp = (param * param)
loss = tmp.sum()
# Compute the gradients
loss.backward()
def test_reentrant_with_non_leaf_variable_hook(self):
handle = None
param = torch.rand(10, requires_grad=True)
def manual_increase_gradient(grad):
handle.remove()
# Add some sort of gradient penalty by directly updating the gradients
with torch.enable_grad():
g = grad.detach().requires_grad_()
out = ((g * 2) + 5).sum()
out.backward()
res = g.grad + grad
return res
# Forward pass
tmp = (param * param)
handle = tmp.register_hook(manual_increase_gradient)
loss = tmp.sum()
# Compute the gradients
loss.backward()
self.assertEqual(param.grad, 6 * param)
def test_grad_fn_attr_bindings(self):
# Check that the getter of each type returns what we want
# See `gen_autograd_functions.py` for how the getters are generated
#
# This test is only meant to check if the codegen'd bindings work
# Please help update this test if you update the names of any the fields we check!
#
a = torch.ones(1, requires_grad=True)
b = torch.ones(1, requires_grad=True)
out = torch.stack([a, b], dim=0)
self.assertEqual(out.grad_fn._saved_tensors, (a, b)) # TensorList -> Tuple[Tensor]
self.assertIsInstance(out.grad_fn._saved_tensors[0], torch.Tensor)
self.assertIsInstance(out.grad_fn._raw_saved_tensors[0], torch._C._autograd.SavedTensor)
self.assertEqual(out.grad_fn._saved_dim, 0) # int64_t -> int
self.assertIsInstance(out.grad_fn._saved_dim, int)
out.grad_fn._raw_saved_tensors[0].register_hooks(lambda x: x, lambda x: x)
out.sum().backward()
with self.assertRaisesRegex(RuntimeError, "after they have already been freed"):
out.grad_fn._saved_tensors
with self.assertRaisesRegex(RuntimeError, "after they have already been freed"):
out.grad_fn._raw_saved_tensors
self.assertEqual(out.grad_fn._saved_dim, 0)
a = torch.ones(2, 2, requires_grad=True)
indices = torch.tensor([0, 1])
out = a[:, indices]
self.assertEqual(out.grad_fn._saved_indices, (None, indices)) # c10::List<c10::optional<Tensor>> -> Tuple[Tensor?]
self.assertIsInstance(out.grad_fn._saved_indices[1], torch.Tensor)
self.assertIsInstance(out.grad_fn._raw_saved_indices[1], torch._C._autograd.SavedTensor)
self.assertEqual(out.grad_fn._saved_self_sizes, a.shape) # IntArrayRef -> Tuple[int]
self.assertIsInstance(out.grad_fn._saved_self_sizes[0], int)
out.grad_fn._raw_saved_indices[1].register_hooks(lambda x: x, lambda x: x)
with self.assertRaisesRegex(RuntimeError, "None is forbidden"):
out.grad_fn._raw_saved_indices[0].register_hooks(lambda x: x, lambda x: x)
a = torch.ones(2, 2, requires_grad=True)
out = a * a
out.grad_fn._raw_saved_self.register_hooks(lambda x: x, lambda x: x)
out.sum().backward()
with self.assertRaisesRegex(RuntimeError, "after it has been freed"):
out.grad_fn._raw_saved_self.register_hooks(lambda x: x, lambda x: x)
a = torch.ones(1, 1, 2, requires_grad=True)
out = torch.nn.functional.interpolate(a, 4, mode="linear")
self.assertEqual(out.grad_fn._saved_output_size, (4,)) # c10::optional<IntArrayRef> -> int[]?
self.assertIsInstance(out.grad_fn._saved_output_size[0], int)
self.assertEqual(out.grad_fn._saved_align_corners, False) # bool -> bool
self.assertIsInstance(out.grad_fn._saved_align_corners, bool)
if hasattr(out.grad_fn, '_saved_scale_factors'):
self.assertIsNone(out.grad_fn._saved_scale_factors) # c10::optional<ArrayRef<double>> -> float[]?
else:
self.assertIsNone(out.grad_fn._saved_scales) # c10::optional<ArrayRef<double>> -> float[]?
out = torch.nn.functional.interpolate(a, scale_factor=0.5, mode="linear")
self.assertIsNone(out.grad_fn._saved_output_size)
self.assertEqual(out.grad_fn._saved_scale_factors, (0.5,))
self.assertIsInstance(out.grad_fn._saved_scale_factors[0], float)
a = torch.ones(2, 2, requires_grad=True)
out = torch.pdist(a, p=1)
self.assertEqual(out.grad_fn._saved_p, 1.) # double -> float
self.assertIsInstance(out.grad_fn._saved_p, float)
a = torch.ones(1, 1, 2, requires_grad=True)
out = torch.logit(a, 1.)
self.assertEqual(out.grad_fn._saved_eps, 1.) # c10:optional<double> -> float?
self.assertIsInstance(out.grad_fn._saved_eps, float)
out = torch.logit(a)
self.assertIsNone(out.grad_fn._saved_eps)
if torch._C.has_lapack:
a = torch.ones(1, 1, requires_grad=True)
q, r = torch.linalg.qr(a, mode="reduced")
self.assertEqual(q.grad_fn._saved_mode, "reduced") # std::string -> str
a = torch.tensor([1.], requires_grad=True)
out = torch.div(a, 2., rounding_mode="trunc")
self.assertEqual(out.grad_fn._saved_rounding_mode, "trunc") # c10::optional<std::string> -> str?
out = torch.div(a, 2., rounding_mode=None)
self.assertIsNone(out.grad_fn._saved_rounding_mode) # c10::optional<std::string> -> str?
x = torch.zeros(5, requires_grad=True)
out = torch.threshold(x, threshold=(1 + 0j), value=(1 + 0j))
self.assertIsInstance(out.grad_fn._saved_threshold, complex) # Scalar(complex double) -> complex
cfloat = torch.tensor(1 + 0j, dtype=torch.complex64)
out = torch.threshold(x, threshold=cfloat, value=(1 + 0j))
self.assertIsInstance(out.grad_fn._saved_threshold, complex) # Scalar(complex float) -> complex
out = torch.threshold(x, threshold=1., value=1.)
self.assertIsInstance(out.grad_fn._saved_threshold, float) # Scalar(floating point) -> float
out = torch.threshold(x, threshold=1, value=1)
self.assertIsInstance(out.grad_fn._saved_threshold, int) # Scalar(integral) -> int
out = torch.threshold(x, threshold=False, value=False)
self.assertIsInstance(out.grad_fn._saved_threshold, bool) # Scalar(bool) -> bool
a = torch.ones(2, 2, requires_grad=True)
out = a.as_strided((3,), (1,), 1)
self.assertEqual(out.grad_fn._saved_storage_offset, 1) # c10:optional<int64_t> -> int?
self.assertIsInstance(out.grad_fn._saved_storage_offset, int)
out = a.as_strided((3,), (1,))
self.assertIsNone(out.grad_fn._saved_storage_offset)
a = torch.ones(2, requires_grad=True)
out = torch.tanh(a)
self.assertEqual(out, out.grad_fn._saved_result) # saved variable when output
a = torch.randn(3, 5, requires_grad=True)
b = torch.tensor([1, 0, 4])
loss = nn.NLLLoss()
out = loss(a, b)
self.assertIsNone(out.grad_fn._saved_weight)
loss = nn.NLLLoss(weight=torch.ones((5,)))
out = loss(a, b)
self.assertEqual(out.grad_fn._saved_weight, torch.ones((5,))) # c10:optional<Tensor> -> Tensor?
out.sum().backward()
with self.assertRaisesRegex(RuntimeError, "after they have already been freed"):
out.grad_fn._saved_weight
def test_cant_create_saved_tensors(self):
with self.assertRaisesRegex(RuntimeError, "Trying to create a SavedTensor object from Python is forbidden"):
torch.autograd.SavedTensor()
def test_custom_function_saved_tensors(self):
def getFn(save=True):
class MyFn(Function):
@staticmethod
def forward(ctx, x):
if save:
ctx.save_for_backward(x, None)
return x
@staticmethod
def backward(ctx, g):
return g
return MyFn
a = torch.randn(5, requires_grad=True)
y = getFn(True).apply(a)
self.assertEqual((a, None), y.grad_fn.saved_tensors)
saved = y.grad_fn._raw_saved_tensors
self.assertIsInstance(saved[0], torch._C._autograd.SavedTensor)
# We can't tell the underlying tensor is None without unpacking it
self.assertIsInstance(saved[1], torch._C._autograd.SavedTensor)
# We catch that error when the user calls register_hooks on it
with self.assertRaisesRegex(RuntimeError, "None is forbidden"):
saved[1].register_hooks(lambda x: x, lambda x: x)
with self.assertRaisesRegex(TypeError, "incompatible function arguments"):
saved[0].register_hooks(lambda x: x)
with self.assertRaisesRegex(TypeError, "incompatible function arguments"):
saved[0].register_hooks(1, 1)
saved[0].register_hooks(lambda x: x, lambda x: x)
with self.assertRaisesRegex(RuntimeError, "already been set"):
saved[0].register_hooks(lambda x: x, lambda x: x)
y.sum().backward()
# Using a reference to the SavedTensor object after the
# saved variables have been released can lead to undefined behavior
del saved
with self.assertRaisesRegex(RuntimeError, "after they have already been freed"):
y.grad_fn._raw_saved_tensors
with self.assertRaisesRegex(RuntimeError, "after they have already been freed"):
y.grad_fn.saved_tensors
y = getFn(False).apply(a)
self.assertEqual(y.grad_fn.saved_tensors, ())
self.assertEqual(y.grad_fn._raw_saved_tensors, ())
def test_autograd_views_codegen(self):
# This is not necessarily the absolute correct behavior, but this is the current
# one. This test is here to make sure that any change to this behavior is detected
# and not silent. The TODOs below mark the places with unexpected behavior.
# Note that any change in these test will be BC-breaking and should be done carefully.
# This test checks the behavior of two codegen functions (view_as and unbind)
# with respect to view tracking and inplace operation on the output.
def run_test(grad_mode, requires_grad, is_view, should_raise_tuple):
def maybe_check_raise(fn, should_raise):
self.assertTrue(should_raise is None or isinstance(should_raise, str))
if should_raise is not None:
with self.assertRaisesRegex(RuntimeError, should_raise):
fn()
else:
fn()
inp = torch.rand(2, requires_grad=requires_grad).clone()
with torch.set_grad_enabled(grad_mode):
out = inp.view_as(inp)
# Are they differentiable views?
self.assertTrue(out._is_view() == is_view)
# Are inplace allowed?
maybe_check_raise(lambda: out.add_(1), should_raise_tuple[0])
inp = torch.rand(2, requires_grad=requires_grad).clone()
with torch.set_grad_enabled(grad_mode):
out = inp.unbind()
# Are they differentiable views?
self.assertTrue(out[0]._is_view() == is_view)
self.assertTrue(out[1]._is_view() == is_view)
# Are inplace allowed?
maybe_check_raise(lambda: out[0].add_(1), should_raise_tuple[1])
maybe_check_raise(lambda: out[1].add_(1), should_raise_tuple[2])
# should_raise contains None if it should not raise
# should_raise contains a string of the error if it should raise
# The 3 elements are for view_as, first output of unbind and second output of unbind
run_test(grad_mode=True, requires_grad=False, is_view=True,
should_raise_tuple=(None, None, None))
inp_change_err = "Output {} of UnbindBackward0 is a view and is being modified inplace."
run_test(grad_mode=True, requires_grad=True, is_view=True,
should_raise_tuple=(None, inp_change_err.format("0"), inp_change_err.format("1")))
leaf_grad_err = "A view was created in no_grad mode and is being modified inplace"
run_test(grad_mode=False, requires_grad=True, is_view=True,
should_raise_tuple=(leaf_grad_err, leaf_grad_err, leaf_grad_err))
run_test(grad_mode=False, requires_grad=False, is_view=True,
should_raise_tuple=(None, None, None))
def test_inplace_not_requires_grad(self):
class MyFn(torch.autograd.Function):
@staticmethod
def forward(ctx, inp):
return inp.view_as(inp)
@staticmethod
def backward(ctx, grad):
return grad
# Original Tensor does not require grad
a = torch.rand(1, 2)
# Tensor being written does require grad
b = torch.rand(1, requires_grad=True)
# Take an invalid view on 'a' that should raise an error (warns during deprecation)
view_a = MyFn.apply(a)
with self.assertRaisesRegex(RuntimeError, "This view was created inside a custom Function"):
view_a += b
# Extra test for copy_ that is a manual implementation and could be easily
# forgotten when the codegen is updated (warns during deprecation)
a = torch.rand(1, 2)
b = torch.rand(1, requires_grad=True)
view_a = MyFn.apply(a)
with self.assertRaisesRegex(RuntimeError, "This view was created inside a custom Function"):
view_a.copy_(b)
# Functions that should throw must properly throw
a = torch.rand(1, 2)
b = torch.rand(1, requires_grad=True)
view_a = a.unbind()[0]
with self.assertRaisesRegex(RuntimeError, "This view is the output of a function that returns "
"multiple views."):
view_a.copy_(b)
# Sanity check that views that should work still work
a = torch.rand(1, 2)
b = torch.rand(1, requires_grad=True)
a.select(1, 0).copy_(b)
def _do_test_autograd_simple_views_python(self, dtype):
# This is not necessarily the absolute correct behavior, but this is the current
# one. This test is here to make sure that any change to this behavior is detected
# and not silent. The TODOs below mark the places with unexpected behavior.
# Note that any change in these test will be BC-breaking and should be done carefully.
# This checks the autograd.Function behavior when we return one or multiple outputs
# while one of these is an input, a view of an input or of a temporary tensor.
# This indicator is used to track how many times the backward function was called
bw_called = [0]
# This indicator is used to check if the argument `ga` contains non-zero values
ga_nz = [False]
class IdOneOutput(Function):
@staticmethod
def forward(ctx, a, b, make_view):
if make_view:
a = a.narrow(0, 0, 2)
else:
a = a.clone()
return a
@staticmethod
def backward(ctx, ga):
bw_called[0] += 1
return ga, None, None
class IdTwoOutput(Function):
@staticmethod
def forward(ctx, a, b, make_view):
if make_view:
a = a.narrow(0, 0, 2)
else:
a = a.clone()
return a, a + b
@staticmethod
def backward(ctx, ga, gab):
bw_called[0] += 1
if ga.eq(0).all():
ga_nz[0] = False
else:
ga_nz[0] = True
return ga + gab, gab, None
class ViewOfTemp(Function):
@staticmethod
def forward(ctx, a, make_view):
ctx.save_for_backward(a)
if make_view:
a = a.narrow(0, 0, 2)
else:
a = a.clone()
b = a.clone()
return b.select(0, 0)
@staticmethod
def backward(ctx, grad):
bw_called[0] += 1
a, = ctx.saved_tensors
res = torch.zeros_like(a)
res.select(0, 0).copy_(grad)
return res, None
fn_id_to_inplace_on_view_err_msg = {
"one_output": ("Output 0 of IdOneOutputBackward is a view and is being "
"modified inplace. This view was created inside a custom Function"),
"two_output": ("Output 0 of IdTwoOutputBackward is a view and is being modified inplace."
" This view is the output of a function that returns multiple views."),
"view_of_temp": ("Output 0 of ViewOfTempBackward is a view and is being "
"modified inplace. This view was created inside a custom Function")
}
for fn_id in ["one_output", "two_output", "view_of_temp"]:
for inplace in [True, False]:
for make_view in [True, False]:
# Used for special casing the tests below
output_is_a_view = (make_view or fn_id == "view_of_temp")
def fn(a, b):
# never modify a, b inplace for gracheck
a = a.clone()
b = b.clone()
if fn_id == "two_output":
tmp1, tmp2 = IdTwoOutput.apply(a, b, make_view)
if inplace:
tmp1 += 3
tmp2 += 3
else:
tmp1 = tmp1 + 3
tmp2 = tmp2 + 3
tmp = tmp1 * tmp2
else:
if fn_id == "one_output":
tmp = IdOneOutput.apply(a, b, make_view)
else:
tmp = ViewOfTemp.apply(a + b, make_view)
if inplace:
tmp += 3
else:
tmp = tmp + 3
return tmp.sum()
a = torch.ones(2, dtype=dtype, requires_grad=True)
b = torch.ones(2, dtype=dtype, requires_grad=True)
err_msg = fn_id_to_inplace_on_view_err_msg[fn_id]
if not inplace or not output_is_a_view:
gradcheck(fn, (a, b), check_batched_grad=False)
# Was the custom backward called properly
bw_called[0] = 0
ga_nz[0] = True # For the case where the backward is called
if inplace and output_is_a_view:
with self.assertRaisesRegex(RuntimeError, err_msg):
fn(a, b)
else:
fn(a, b).backward()
expected_called = 1
expected_ga_nz = True
if output_is_a_view and inplace:
expected_called = 0
self.assertTrue(bw_called[0] == expected_called)
self.assertTrue(ga_nz[0] == expected_ga_nz)
def test_autograd_simple_views_python(self):
self._do_test_autograd_simple_views_python(torch.double)
self._do_test_autograd_simple_views_python(torch.cdouble)
def test_autograd_inplace_views_creation_meta(self):
# Tests creation_meta properly handled for inplace views
class Func(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
return x.view_as(x)
@staticmethod
def backward(ctx, x):
return x
view_custom = Func.apply
def run_test(fn, fn_type, grad_mode_view, grad_mode_iview, requires_grad, error1, error2):
# This test checks the behavior of inplace-view functions when
# the views are created in grad mode or not
base = torch.rand(2, 3, requires_grad=requires_grad).clone()
# 1. Create a view with `grad_mode=grad_mode_view`
with torch.set_grad_enabled(grad_mode_view):
if fn_type == "multi_view":
inp = base.unbind()[0]
elif fn_type == "custom" :
inp = view_custom(base)
else:
inp = base.view_as(base)
# 2. Perform inplace view with `grad_mode=grad_mode_iview`
with torch.set_grad_enabled(grad_mode_iview):
if error1 is not None:
with self.assertRaisesRegex(RuntimeError, error1):
fn(inp)
return
else:
# If error is None, check that runs without error
fn(inp)
# 3. Do inplace on the (new) view
if error2 is not None:
with self.assertRaisesRegex(RuntimeError, error2):
inp.add_(1)
else:
# If error is None, check that runs without error
inp.add_(1)
no_grad_err = "A view was created in no_grad mode"
multi_view_err = "function that returns multiple views"
custom_err = "view was created inside a custom Function"
def run_tests(fn):
for fn_type in ("normal", "multi_view", "custom"):
for grad_mode_view in (True, False):
for grad_mode_iview in (True, False):
for requires_grad in (True, False):
error1 = None # expected error when we do inplace_view on original view
error2 = None # expected error when we do inplace on the resulting view
if requires_grad:
if not grad_mode_view and grad_mode_iview:
error1 = no_grad_err
if not grad_mode_view and not grad_mode_iview:
error2 = no_grad_err
if fn_type == "multi_view":
if grad_mode_view and grad_mode_iview:
error1 = multi_view_err
if grad_mode_view and not grad_mode_iview:
error2 = multi_view_err
if fn_type == "custom":
if grad_mode_view and grad_mode_iview:
error1 = custom_err
if grad_mode_view and not grad_mode_iview:
error2 = custom_err
run_test(fn, fn_type, grad_mode_view, grad_mode_iview, requires_grad, error1, error2)
# This list was created by logging gen_inplace_or_view_type.py
# detach_ is excluded for this test because it cannot be applied to
# views and thus does not return a view
run_tests(lambda v: v.as_strided_((1, 0), (2, 2)))
run_tests(lambda v: v.transpose_(0, 0))
run_tests(lambda v: v.t_())
run_tests(lambda v: v.squeeze_(0))
run_tests(lambda v: v.unsqueeze_(0))
run_tests(lambda v: v.swapdims_(0, 0))
run_tests(lambda v: v.swapaxes_(0, 0))
# TODO This is not the correct behavior -
# See https://github.com/pytorch/pytorch/issues/49825#issuecomment-794466627
def test_autograd_inplace_views_cross_dtype(self):
# This test is here to make sure that any change to this behavior is detected
# and not silent. The TODOs below mark the places with unexpected behavior.
a_orig = torch.rand(3, 3, requires_grad=True, dtype=torch.complex64)
a = a_orig.clone()
b = torch.view_as_real(a)
b = b.transpose(0, 1)
b += 1
b.backward(torch.arange(0, 18, dtype=torch.float).view(3, 3, 2))
non_inplace_grad = a_orig.grad
a_orig = torch.rand(3, 3, requires_grad=True, dtype=torch.complex64)
a = a_orig.clone()
b = torch.view_as_real(a)
b.transpose_(0, 1)
b += 1
b.backward(torch.arange(0, 18, dtype=torch.float).view(3, 3, 2))
inplace_grad = a_orig.grad
# TODO: this is a bug!
# once this is fixed, it should have the transpose removed:
# self.assertEqual(non_inplace_grad, inplace_grad)
self.assertEqual(non_inplace_grad.T, inplace_grad)
def test_autograd_multiple_views_python(self):
# This is not necessarily the absolute correct behavior, but this is the current
# one. This test is here to make sure that any change to this behavior is detected
# and not silent. The TODOs below mark the places with unexpected behavior.
# Note that any change in these test will be BC-breaking and should be done carefully.
# This checks that multiples views in the forward are properly traced and how they
# behave with respect to inplace operations.
# This indicator is used to track how many times the backward function was called
bw_called = [0]
class ComplexView(Function):
@staticmethod
def forward(ctx, a, idx):
res = a.narrow(0, idx, 1)
res = a.select(0, idx)
ctx.save_for_backward(a)
ctx.idx = idx
return res
@staticmethod
def backward(ctx, grad):
bw_called[0] += 1
a, = ctx.saved_tensors
res = torch.zeros_like(a)
res.select(0, ctx.idx).copy_(grad)
return res, None
a = torch.ones(2, requires_grad=True)
idx = 1
bw_called[0] = 0
out = ComplexView.apply(a.clone(), idx)
out.sum().backward()
self.assertTrue(bw_called[0] == 1)
out = ComplexView.apply(a.clone(), idx)
with self.assertRaisesRegex(RuntimeError,
"Output 0 of ComplexViewBackward is a view and is being modified inplace"):
out += 1
def test_autograd_python_custom_function_inplace(self):
# This is not necessarily the absolute correct behavior, but this is the current
# one. This test is here to make sure that any change to this behavior is detected
# and not silent. The TODOs below mark the places with unexpected behavior.
# Note that any change in these test will be BC-breaking and should be done carefully.
# This test checks custom autograd.Function that perform inplace operations
bw_called = [0]
# I) Single output
class MyAdder(Function):
@staticmethod
def forward(ctx, a, b):
a.add_(b)
ctx.mark_dirty(a)
return a
@staticmethod
def backward(ctx, grad):
bw_called[0] += 1
return grad, grad
a = torch.ones(2, requires_grad=True)
b = torch.ones(2, requires_grad=True)
# No extra inplace
c = MyAdder.apply(a.clone(), b)
c.sum().backward()
self.assertTrue(bw_called[0] == 1)
# With extra inplace on the output
bw_called[0] = 0
c = MyAdder.apply(a.clone(), b)
c += 2
c.sum().backward()
self.assertTrue(bw_called[0] == 1)
# The input is a view
bw_called[0] = 0
c = MyAdder.apply(a.clone().view_as(a), b)
c.sum().backward()
self.assertTrue(bw_called[0] == 1)
# Should not give non-inputs to mark_dirty
class MyAdderBad(Function):
@staticmethod
def forward(ctx, a, b):
c = 3 * a
c.add_(b)
ctx.mark_dirty(c)
return c
@staticmethod
def backward(ctx, grad):
bw_called[0] += 1
grad = 3 * grad
return grad, grad
a = torch.ones(2, requires_grad=True)
b = torch.ones(2, requires_grad=True)
with warnings.catch_warnings(record=True) as w:
MyAdderBad.apply(a.clone(), b)
self.assertEqual(len(w), 1)
# II) Multiple outputs
class MyBadAdder(Function):
@staticmethod
def forward(ctx, a, b):
a.add_(b)
ctx.mark_dirty(a)
return a, a + b
@staticmethod
def backward(ctx, ga, gab):
bw_called[0] += 1
return ga + gab, ga + gab
# No extra inplace
bw_called[0] = 0
c, d = MyBadAdder.apply(a.clone(), b)
(c * d).sum().backward()
self.assertTrue(bw_called[0] == 1)
# With extra inplace on the output
bw_called[0] = 0
c, d = MyBadAdder.apply(a.clone(), b)
c += 2
(c * d).sum().backward()
self.assertTrue(bw_called[0] == 1)
# The input is a view
inplace_on_view_err = "your Function modifies inplace an input that is a view of another Tensor"
with self.assertRaisesRegex(RuntimeError, inplace_on_view_err):
c, d = MyBadAdder.apply(a.clone().view_as(a), b)
# III) Inplace + other op
class MyOutPlaceAdder(Function):
@staticmethod
def forward(ctx, a, b):
a.add_(b)
ctx.mark_dirty(a)
return a.clone(), a + b
@staticmethod
def backward(ctx, ga, gab):
bw_called[0] += 1
return ga + gab, ga + 2 * gab
# We don't reuse the input
def fn(a, b):
orig_a = a.clone().view_as(a)
c, d = MyOutPlaceAdder.apply(orig_a, b)
return (c * d).sum()
bad_mark_dirty_err = "Some elements marked as dirty during the forward method were not returned as output."
with self.assertRaisesRegex(RuntimeError, bad_mark_dirty_err):
fn(a, b)
def test_named_tensor_for_complex_views(self):
names = ["batch", "height", "width", "complex"]
z = torch.ones((5, 12, 14, 2), requires_grad=True)
z_named = z.refine_names(*names)
z_complex = torch.view_as_complex(z_named.rename(None)).refine_names(*names[:-1])
z_complex.sum().backward()
self.assertEqual(z.grad, torch.view_as_real(torch.ones_like(z_complex).rename(None)))
def test_custom_function_return_view_in_nograd(self):
class Alias(Function):
@staticmethod
def forward(ctx, x):
return x[:]
@staticmethod
def backward(ctx, gx):
return gx
inp = torch.rand(2, requires_grad=True)
with torch.no_grad():
output = Alias.apply(inp)
with torch.no_grad():
expected_output = inp[:]
# Calling the custom function should operate as if we called an equivalent op
self.assertEqual(output.requires_grad, expected_output.requires_grad)
# Check that in-place modification on view throws
leaf_grad_err = "A view was created in no_grad mode and is being modified inplace"
with self.assertRaisesRegex(RuntimeError, leaf_grad_err):
output.zero_()
def test_grad_mode_restored_reentrant(self):
class MyFunction(Function):
@staticmethod
def forward(ctx, inp):
return inp.clone()
@staticmethod
def backward(ctx, go):
original = torch._C.is_grad_enabled()
with torch.enable_grad():
self.assertTrue(torch._C.is_grad_enabled())
foo = torch.rand(go.size(), requires_grad=True)
grad, = torch.autograd.grad(
foo ** 3, foo, grad_outputs=go
)
self.assertTrue(torch._C.is_grad_enabled())
self.assertTrue(torch._C.is_grad_enabled() == original)
return grad
inp = torch.rand(3, requires_grad=True)
# Case where original==False
MyFunction.apply(inp).sum().backward()
# Case where original==True
MyFunction.apply(inp).sum().backward(create_graph=True)
def test_power_function(self):
a = torch.tensor([0., 0., 0.])
b = torch.tensor([-1., 0., 1.], requires_grad=True)
c = torch.sum(a**b)
c.backward()
self.assertEqual(b.grad, torch.tensor([-inf, 0., 0.]))
s = 0
b = torch.tensor([-1., 0., 1.], requires_grad=True)
c = torch.sum(s**b)
c.backward()
self.assertEqual(b.grad, torch.tensor([-inf, 0., 0.]))
def test_custom_function_error(self):
class BadFw(Function):
@staticmethod
def backward(ctx, foo):
return foo
class BadBw(Function):
@staticmethod
def forward(ctx, foo):
return foo.clone()
class BadBw2(Function):
@staticmethod
def forward(ctx, foo):
return foo.clone()
@staticmethod
def backward(ctx, foo):
return foo
@staticmethod
def vjp(ctx, foo):
return foo
class BadJvp(Function):
@staticmethod
def forward(ctx, foo):
return foo.clone()
inp = torch.rand(1, requires_grad=True)
with self.assertRaisesRegex(NotImplementedError, "must implement the forward"):
BadFw.apply(inp)
with self.assertRaisesRegex(RuntimeError, "must implement either the backward"):
BadBw.apply(inp).sum().backward()
with self.assertRaisesRegex(RuntimeError, "Implementing both 'backward' and 'vjp'"):
BadBw2.apply(inp).sum().backward()
with self.assertRaisesRegex(RuntimeError, "must implement the jvp function"):
with fwAD.dual_level():
d = fwAD.make_dual(inp, torch.rand_like(inp))
res = BadJvp.apply(d)
def test_custom_function_forward_mode_view_checks(self):
flag_to_error = {
"ok": None,
"not_a_view": "jvp is not returning a view",
"not_a_view_of_inp": "jvp is not returning a view of the given",
"not_a_view_of_inp_base": "jvp is not returning a view of the same base",
}
class ViewFn(Function):
@staticmethod
def forward(ctx, foo, flag):
ctx.flag = flag
ctx.size = foo.size()
return foo.narrow(0, 0, 2)
@staticmethod
def vjp(ctx, gO):
gI = gO.new_zeros(ctx.size)
gI.narrow(0, 0, 2).copy_(gO)
return gI, None
@staticmethod
def jvp(ctx, gI, _):
res = gI.narrow(0, 0, 2)
if ctx.flag != "ok":
# Break the view in the gradients!
res = res.clone()
if ctx.flag in ["not_a_view_of_inp", "not_a_view_of_inp_base"]:
# Result should be a view, just of the wrong thing
res = res.view_as(res)
return res
inp = torch.rand(4, 4, dtype=torch.double, requires_grad=True)
for flag, msg in flag_to_error.items():
def test_fn(inp):
if flag == "not_a_view_of_inp_base":
inp = inp.view_as(inp)
return ViewFn.apply(inp, flag)
if msg is None:
gradcheck(test_fn, inp, check_forward_ad=True)
else:
with self.assertRaisesRegex(RuntimeError, msg):
gradcheck(test_fn, inp, check_forward_ad=True)
def test_custom_function_forward_mode_inplace_checks(self):
class InplaceFn(Function):
@staticmethod
def forward(ctx, foo, flag):
ctx.mark_dirty(foo)
ctx.flag = flag
foo.mul_(2)
return foo
@staticmethod
def vjp(ctx, gO):
return 2 * gO, None
@staticmethod
def jvp(ctx, gI, _):
if ctx.flag:
# Don't do the change inplace
return 2 * gI
else:
gI.mul_(2)
return gI
inp = torch.rand(4, 4, dtype=torch.double, requires_grad=True)
def test_fn(inp, flag):
inp = inp.clone()
return InplaceFn.apply(inp, flag)
gradcheck(test_fn, (inp, False), check_forward_ad=True)
with self.assertRaisesRegex(RuntimeError, "inplace custom Function is not modifying the forward mode gradients inplace"):
gradcheck(test_fn, (inp, True), check_forward_ad=True)
def test_custom_function_forward_mode_wrong_formula(self):
class UserFn(Function):
@staticmethod
def forward(ctx, foo, should_fail):
ctx.should_fail = should_fail
return foo * 2
@staticmethod
def vjp(ctx, gO):
return 2 * gO, None
@staticmethod
def jvp(ctx, gI, _):
if ctx.should_fail:
# Wrong gradient formula
return 3 * gI
else:
return 2 * gI
inp = torch.rand(10, dtype=torch.double, requires_grad=True)
gradcheck(UserFn.apply, (inp, False), check_forward_ad=True)
with self.assertRaisesRegex(RuntimeError, "Jacobian computed with forward mode mismatch for output 0"):
gradcheck(UserFn.apply, (inp, True), check_forward_ad=True)
def test_custom_function_forward_mode_non_tensor_before_tensor_args(self):
class MyFn(torch.autograd.Function):
@staticmethod
def forward(ctx, nt, x, nt2, y):
return x * 2 + y * 3
@staticmethod
def jvp(ctx, nt, x_t, nt2, y_t):
self.assertIsNone(nt)
self.assertIsNone(nt2)
return x_t * 2 + y_t * 3
x = torch.tensor(1., dtype=torch.double)
t = torch.tensor(1., dtype=torch.double)
y = torch.tensor(1., dtype=torch.double)
with fwAD.dual_level():
dual_x = fwAD.make_dual(x, t)
MyFn.apply(1, dual_x, 1, y)
gradcheck(MyFn.apply, (1, x.requires_grad_(True), 1, y.requires_grad_(True)), check_forward_ad=True,
check_backward_ad=False, check_batched_grad=False)
def test_custom_function_forward_mode_forward_is_no_op(self):
error_regex = "A custom Function's forward is returning a view \\(or an input as-is\\)"
return_lambdas = {
# If we return an input as-is in forward, that is treated
# as if self.view_as(self) is performed. If jvp returns x.view_as(x),
# this is OK.
"view_as": lambda x: x.view_as(x),
# Expect this to raise an error
"self": lambda x: x,
# Expect this to raise the same error
"mul_by_2": lambda x: x * 2,
}
for k, fn in return_lambdas.items():
class MyFn(torch.autograd.Function):
@staticmethod
def forward(ctx, x, y):
return x + y, x
@staticmethod
def vjp(ctx, gO1, gO2):
return gO1 + gO2, gO1
@staticmethod
def jvp(ctx, x_t, y_t):
return x_t + y_t, fn(x_t)
a = torch.tensor(1., dtype=torch.double, requires_grad=True)
t = torch.tensor(1., dtype=torch.double)
b = torch.tensor(1., dtype=torch.double, requires_grad=True)
c = torch.tensor(1., dtype=torch.double)
t2 = torch.tensor(1., dtype=torch.double)
d = torch.tensor(1., dtype=torch.double)
with fwAD.dual_level():
a_dual = fwAD.make_dual(a, t)
c_dual = fwAD.make_dual(c, t2)
if k == "view_as":
_, out2 = MyFn.apply(a_dual, b)
self.assertTrue(fwAD.unpack_dual(out2).tangent._base is t)
_, out2 = MyFn.apply(c_dual, d)
self.assertTrue(fwAD.unpack_dual(out2).tangent._base is t2)
else:
with self.assertRaisesRegex(RuntimeError, error_regex):
MyFn.apply(a_dual, b)
with self.assertRaisesRegex(RuntimeError, error_regex):
MyFn.apply(c_dual, d)
if k == "view_as":
gradcheck(MyFn.apply, (a, c), check_forward_ad=True)
else:
with self.assertRaisesRegex(RuntimeError, error_regex):
gradcheck(MyFn.apply, (a, c), check_forward_ad=True)
def test_custom_function_save_for_forward(self):
class Func(torch.autograd.Function):
@staticmethod
def forward(ctx, x: torch.Tensor, y: torch.Tensor, z: int):
ctx.save_for_backward(x, y)
ctx.save_for_forward(x, y)
ctx.z = z
ctx.prod = x * y
return z * ctx.prod
@staticmethod
def jvp(ctx, x_t, y_t, _):
x_p, y_p = ctx.saved_tensors
z = ctx.z
return z * (y_p * x_t + x_p * y_t)
@staticmethod
def vjp(ctx, grad_out):
x, y = ctx.saved_tensors
z = ctx.z
return z * grad_out * y, z * grad_out * x, None
a = torch.tensor(1., requires_grad=True, dtype=torch.double)
t = torch.tensor(1., dtype=torch.double)
b = torch.tensor(2., requires_grad=True, dtype=torch.double)
c = 4
with fwAD.dual_level():
a_dual = fwAD.make_dual(a, t)
out = Func.apply(a_dual, b, c)
out.backward()
gradcheck(Func.apply, (a, b, c), check_forward_ad=True)
# When saved for backward, but not saved for forward
class Func(torch.autograd.Function):
@staticmethod
def forward(ctx, x: torch.Tensor):
ctx.save_for_backward(x)
return x.clone()
@staticmethod
def jvp(ctx, x_t):
self.assertEqual(len(ctx.saved_tensors), 0)
return x_t
@staticmethod
def vjp(ctx, grad_out):
x, = ctx.saved_tensors
self.assertEqual(len(ctx.saved_tensors), 1)
return grad_out
with fwAD.dual_level():
a_dual = fwAD.make_dual(a, t)
out = Func.apply(a_dual)
out.backward()
gradcheck(Func.apply, (a,), check_forward_ad=True)
def test_custom_function_local_inplace(self):
class MyFn(torch.autograd.Function):
@staticmethod
def forward(ctx, inp, inplace):
view = inp.clone()[:3]
if inplace:
view += 2
return view
@staticmethod
def backward(ctx, grad):
return grad, None
base = torch.rand(10, requires_grad=True)
foo = MyFn.apply(base, False)
self.assertEqual(foo.grad_fn.__class__.__name__, "MyFnBackward")
foo = MyFn.apply(base, True)
self.assertEqual(foo.grad_fn.__class__.__name__, "MyFnBackward")
def test_integer_outputs(self):
inp = torch.rand(4, requires_grad=True)
out = inp.argmax()
self.assertFalse(out.dtype.is_floating_point)
self.assertFalse(out.requires_grad)
out = inp.argmin()
self.assertFalse(out.dtype.is_floating_point)
self.assertFalse(out.requires_grad)
out = inp.argsort()
self.assertFalse(out.dtype.is_floating_point)
self.assertFalse(out.requires_grad)
val = torch.rand((), requires_grad=True)
out = torch.searchsorted(inp, val)
self.assertFalse(out.dtype.is_floating_point)
self.assertFalse(out.requires_grad)
bins = torch.linspace(0, 1.0, steps=100, requires_grad=True)
vals = torch.rand(5, 5, requires_grad=True)
out = torch.bucketize(vals, bins)
self.assertFalse(out.dtype.is_floating_point)
self.assertFalse(out.requires_grad)
val = torch.empty(5).requires_grad_()
out = val.count_nonzero()
self.assertFalse(out.requires_grad)
def assert_only_first_requires_grad(res):
if not isinstance(res, tuple):
res = (res,)
self.assertTrue(res[0].requires_grad)
for out in res[1:]:
if out is not None:
self.assertFalse(out.requires_grad)
for sort in [True, False]:
for return_inverse in [True, False]:
for return_counts in [True, False]:
res = torch.unique(inp, sorted=sort, return_inverse=return_inverse,
return_counts=return_counts)
assert_only_first_requires_grad(res)
res = torch.unique(inp, sorted=sort, return_inverse=return_inverse,
return_counts=return_counts, dim=0)
assert_only_first_requires_grad(res)
res = torch.unique_consecutive(inp, return_inverse=return_inverse,
return_counts=return_counts)
assert_only_first_requires_grad(res)
res = torch.unique_consecutive(inp, return_inverse=return_inverse,
return_counts=return_counts, dim=0)
assert_only_first_requires_grad(res)
# Here we test the internal functions to make sure all of them are
# covered on top of the public API
res = torch._unique(inp, sorted=sort, return_inverse=return_inverse)
assert_only_first_requires_grad(res)
# This looks public but is actually manually deleted from the
# torch namespace in torch/functional.py
res = torch._VF.unique_dim(inp, dim=0, sorted=sort, return_inverse=return_inverse,
return_counts=return_counts)
assert_only_first_requires_grad(res)
# We don't test `unique_dim_consecutive` here.
# It looks public but the python binding is actually manually disabled in
# tools/autograd/gen_python_functions.py
res = torch._unique2(inp, sorted=sort, return_inverse=return_inverse,
return_counts=return_counts)
assert_only_first_requires_grad(res)
def test_custom_function_cycle(self):
class MyFn(Function):
@staticmethod
def forward(ctx, x, metadata):
x = x.clone()
ctx.meta = metadata
ctx.save_for_backward(x)
return x
@staticmethod
def backward(ctx, gO):
x, = ctx.saved_tensors
self.assertEqual(x, 3.14)
self.assertEqual(ctx.meta["foo"], 3.14)
return gO * x, None
def get_refs(with_backward):
a = torch.tensor(3.14, requires_grad=True)
metadata = {}
out = MyFn.apply(a, metadata)
metadata["foo"] = out
if with_backward:
out.sum().backward()
self.assertEqual(a.grad, a)
return torch._C._WeakTensorRef(out)
with disable_gc():
ref = get_refs(False)
self.assertFalse(ref.expired())
gc.collect()
self.assertTrue(ref.expired())
# The backward clears the saved_variables but not the __dict__
with disable_gc():
ref = get_refs(True)
self.assertFalse(ref.expired())
gc.collect()
self.assertTrue(ref.expired())
def test_input_buffer_accum(self):
leaf = torch.rand(2, 2, requires_grad=True)
# An op that returns sparse gradients
ind = torch.tensor([[0, 0]], dtype=torch.long)
out2 = leaf.gather(0, ind, sparse_grad=True)
# An op that returns the gradients as-is
out1 = leaf.clone()
grad_out1_original = torch.rand_like(out1)
grad_out1 = grad_out1_original.clone()
grad_out2 = torch.rand_like(out2)
torch.autograd.backward((out1, out2), (grad_out1, grad_out2))
# Given gradients should not be modified inplace
self.assertEqual(grad_out1, grad_out1_original)
def test_no_unnecessary_unwrapping(self):
a = torch.randn(5, requires_grad=True)
a_orig = a.detach().clone()
b = a * a
c = a * b
d = torch.exp(a)
# a is leaf
self.assertIs(b.grad_fn._saved_self, a)
self.assertIs(b.grad_fn._saved_other, a)
self.assertIs(c.grad_fn._saved_self, a)
# b is not an output
self.assertIs(c.grad_fn._saved_other, b)
# d is an output
self.assertEqual(d.grad_fn._saved_result, d)
self.assertIsNot(d.grad_fn._saved_result, d)
c.sum().backward()
with self.assertRaisesRegex(RuntimeError, "after they have already been freed"):
c.grad_fn._saved_self
# a is left untouched
self.assertEqual(a, a_orig)
def test_saved_variable_version_counter(self):
a = torch.rand(2, requires_grad=True)
b = torch.exp(a)
b_unpacked = b.grad_fn._saved_result
self.assertEqual(b, b_unpacked)
self.assertEqual(b._version, b_unpacked._version)
with torch.no_grad():
b += 1
self.assertEqual(b, b_unpacked)
self.assertEqual(b._version, b_unpacked._version)
def test_saved_variable_packing_unpacking_saved_original_with_hooks(self):
# Tests that packing/unpacking a SavedVariable works correctly with user-defined hooks
# The saved_original / did_not_save_original distinction corresponds to the `save_original`
# attribute of `SavedVariable`.
def test(get_input, is_leaf):
a = get_input()
grad_fn = a.grad_fn
y = a * a
y.grad_fn._raw_saved_self.register_hooks(lambda x: 2 * x, lambda x: x / 2)
self.assertEqual(a, y.grad_fn._saved_self)
if not is_leaf:
self.assertIs(grad_fn, y.grad_fn._saved_self.grad_fn)
y.sum().backward()
else:
y.sum().backward()
self.assertEqual(2 * a, a.grad)
a = get_input()
grad_fn = a.grad_fn
y = a * a
y.grad_fn._raw_saved_self.register_hooks(lambda x: 2 * x, lambda x: x)
self.assertEqual(2 * a, y.grad_fn._saved_self)
if not is_leaf:
self.assertIs(grad_fn, y.grad_fn._saved_self.grad_fn)
y.sum().backward()
else:
y.sum().backward()
self.assertEqual(3 * a, a.grad)
# double backward
a = get_input()
grad_fn = a.grad_fn
y = a ** 3
y.grad_fn._raw_saved_self.register_hooks(lambda x: x, lambda x: x)
s = torch.sum(y)
g, = torch.autograd.grad(s, (a, ), create_graph=True)
if not is_leaf:
self.assertIs(grad_fn, y.grad_fn._saved_self.grad_fn)
g.sum().backward()
else:
g.sum().backward()
self.assertEqual(6 * a, a.grad)
a = get_input()
y = a * a
y.grad_fn._raw_saved_self.register_hooks(lambda x: x, lambda x: 1)
with self.assertRaisesRegex(TypeError, "Output of saved tensor unpack_hook expected to be a Tensor"):
print(y.grad_fn._saved_self)
a = get_input()
y = a * a
with self.assertRaisesRegex(TypeError, "missing 1 required positional argument"):
y.grad_fn._raw_saved_self.register_hooks(lambda x, b: x, lambda x: x)
a = get_input()
y = a * a
with self.assertRaisesRegex(TypeError, "missing 1 required positional argument"):
y.grad_fn._raw_saved_self.register_hooks(lambda x, b: (x, b), lambda x: x)
def inplace_double(x):
x *= 2
return x
a = get_input()
t = a * a
with self.assertRaisesRegex(RuntimeError, "A saved tensor pack hook is modifying its input in place."):
t.grad_fn._raw_saved_self.register_hooks(inplace_double, lambda x: x / 2)
# leaf
test(lambda: torch.randn(5, requires_grad=True), True)
# not leaf, not output
test(lambda: (1 + torch.randn(5, requires_grad=True)), False)
def test_saved_variable_packing_unpacking_did_not_save_original_with_hooks(self):
# Tests that packing/unpacking a SavedVariable works correctly with user-defined hooks
# The saved_original / did_not_save_original distinction corresponds to the `save_original`
# attribute of `SavedVariable`.
a = torch.randn(5, requires_grad=True)
y = torch.exp(a)
y.grad_fn._raw_saved_result.register_hooks(lambda x: x, lambda x: x)
self.assertEqual(y, y.grad_fn._saved_result)
self.assertIs(y.grad_fn, y.grad_fn._saved_result.grad_fn)
y.sum().backward()
self.assertEqual(a.grad, y)
def test_saved_variable_packing_unpacking_saved_original_with_default_hooks(self):
# Tests that default hooks are properly registered, used and reset
# The saved_original / did_not_save_original distinction corresponds to the `save_original`
# attribute of `SavedVariable`.
# See also:
# - test_saved_variable_packing_unpacking_saved_original_with_hooks
def pack(x):
warnings.warn("pack")
return x
with torch.autograd.graph.saved_tensors_hooks(pack, lambda x: x):
a = torch.ones(5, requires_grad=True)
warnings.simplefilter('always')
with warnings.catch_warnings(record=True) as w:
y = a * a
# should raise two warnings from a being saved twice
self.assertEqual(len(w), 2)
with torch.autograd.graph.saved_tensors_hooks(lambda x: x, lambda x: x):
a = torch.randn(5, requires_grad=True)
y = a * a
self.assertEqual(a, y.grad_fn._saved_self)
self.assertEqual(a, y.grad_fn._saved_other)
y.sum().backward()
self.assertEqual(2 * a, a.grad)
with torch.autograd.graph.saved_tensors_hooks(lambda x: 2 * x, lambda x: x / 2):
a = torch.randn(5, requires_grad=True)
y = a * a
self.assertEqual(a, y.grad_fn._saved_self)
self.assertEqual(a, y.grad_fn._saved_other)
y.sum().backward()
self.assertEqual(2 * a, a.grad)
with torch.autograd.graph.saved_tensors_hooks(lambda x: 2 * x, lambda x: x):
a = torch.randn(5, requires_grad=True)
y = a * a
self.assertEqual(2 * a, y.grad_fn._saved_self)
self.assertEqual(2 * a, y.grad_fn._saved_other)
y.sum().backward()
self.assertEqual(4 * a, a.grad)
# Exited hooks correctly
a = torch.randn(5, requires_grad=True)
y = a * a
self.assertEqual(a, y.grad_fn._saved_self)
self.assertEqual(a, y.grad_fn._saved_other)
y.sum().backward()
self.assertEqual(2 * a, a.grad)
def test_saved_variable_packing_unpacking_did_not_save_original_with_default_hooks(self):
# See also test_saved_variable_packing_unpacking_did_not_save_original_with_hooks
with torch.autograd.graph.saved_tensors_hooks(lambda x: x, lambda x: x):
a = torch.randn(5, requires_grad=True)
y = torch.exp(a)
self.assertEqual(y, y.grad_fn._saved_result)
y.sum().backward()
self.assertEqual(a.grad, y)
def test_setting_default_saved_variable_hooks_twice_should_not_fail(self):
with torch.autograd.graph.saved_tensors_hooks(lambda x: x, lambda x: x):
with torch.autograd.graph.saved_tensors_hooks(lambda x: x, lambda x: x):
pass
def test_setting_default_saved_variable_hooks_twice_should_use_inner(self):
with torch.autograd.graph.saved_tensors_hooks(lambda x: 3 * x, lambda x: 3 * x):
b = torch.randn(5, requires_grad=True)
with torch.autograd.graph.saved_tensors_hooks(lambda x: 5 * x, lambda x: 5 * x):
a = torch.randn(5, requires_grad=True)
y = a * a
z = b * b
y.sum().backward()
z.sum().backward()
self.assertEqual(2 * 5 * 5 * a, a.grad)
self.assertEqual(2 * 3 * 3 * b, b.grad)
def test_save_on_cpu_and_checkpoint(self):
a = torch.randn(2, 2, requires_grad=True)
b = a.pow(2).pow(2).pow(2).pow(2)
b.sum().backward()
b_grad = a.grad.clone()
a.grad.zero_()
with torch.autograd.graph.save_on_cpu():
h = a.pow(2)
h = checkpoint(lambda x: x.pow(2).pow(2), h, use_reentrant=False)
c = h.pow(2)
c.sum().backward()
c_grad = a.grad.clone()
a.grad.zero_()
def f(a):
h = a.pow(2)
with torch.autograd.graph.save_on_cpu():
h = h.pow(2).pow(2)
return h.pow(2)
d = checkpoint(f, a, use_reentrant=False)
d.sum().backward()
d_grad = a.grad.clone()
self.assertEqual(b_grad, c_grad)
self.assertEqual(b_grad, d_grad)
def test_pack_hook_with_inplace_modification_should_fail(self):
a = torch.randn(5, requires_grad=True)
def inc(x):
x += 1
return x
with torch.autograd.graph.saved_tensors_hooks(inc, lambda x: x):
with self.assertRaisesRegex(RuntimeError, "A saved tensor pack hook is modifying its input in place."):
y = torch.exp(a)
y = torch.exp(a)
with self.assertRaisesRegex(RuntimeError, "A saved tensor pack hook is modifying its input in place."):
y.grad_fn._raw_saved_result.register_hooks(inc, lambda x: x)
def test_saving_variable_to_disk(self):
with tempfile.TemporaryDirectory() as tmp_dir:
def pack(x):
name = os.path.join(tmp_dir, str(uuid.uuid4()))
torch.save(x, name)
return name
def unpack(name):
return torch.load(name)
with torch.autograd.graph.saved_tensors_hooks(pack, unpack):
a = torch.ones(5, requires_grad=True)
y = a * a
self.assertEqual(a, y.grad_fn._saved_self)
y.sum().backward()
self.assertEqual(2 * a, a.grad)
def test_default_saved_variable_hooks_double_backward(self):
with torch.autograd.graph.saved_tensors_hooks(lambda x: x, lambda x: x):
a = torch.randn(5, requires_grad=True)
y = a ** 3
s = torch.sum(y)
g, = torch.autograd.grad(s, (a, ), create_graph=True)
g.sum().backward()
self.assertEqual(6 * a, a.grad)
with torch.autograd.graph.saved_tensors_hooks(lambda x: 2 * x, lambda x: x):
a = torch.randn(5, requires_grad=True)
y = a ** 3
s = torch.sum(y)
g, = torch.autograd.grad(s, (a, ), create_graph=True)
g.sum().backward()
# factor 2 because only a is saved once
self.assertEqual(6 * 2 * a, a.grad)
a = torch.randn(5, requires_grad=True)
y = a ** 3
s = torch.sum(y)
with torch.autograd.graph.saved_tensors_hooks(lambda x: 2 * x, lambda x: x):
g, = torch.autograd.grad(s, (a, ), create_graph=True)
g.sum().backward()
# factor 4 because pow_backward is grad * (exp * self.pow(exp - 1))
# so grad is saved and self (i.e. a) is saved
self.assertEqual(6 * 4 * a, a.grad)
with torch.autograd.graph.saved_tensors_hooks(lambda x: 2 * x, lambda x: x):
a = torch.randn(5, requires_grad=True)
y = a ** 3
s = torch.sum(y)
g, = torch.autograd.grad(s, (a, ), create_graph=True)
g.sum().backward()
# combining the two above blocks: 2 * 4 = 8
# note that in that sense, a is saved twice
self.assertEqual(6 * 8 * a, a.grad)
def test_graph_save_on_cpu(self):
def test(get_input, cuda, pin_memory):
with torch.autograd.graph.save_on_cpu(pin_memory):
a = get_input()
if cuda:
a.cuda()
y = a * a
self.assertEqual(a, y.grad_fn._saved_self)
self.assertEqual(a, y.grad_fn._saved_other)
self.assertEqual(a.dtype, y.grad_fn._saved_self.dtype)
self.assertEqual(a.layout, y.grad_fn._saved_self.layout)
if y.is_sparse:
y = y.to_dense()
y.sum().backward()
actual = 2 * a
expected = a.grad
if a.is_sparse:
actual = actual.coalesce()
expected = expected.coalesce()
self.assertEqual(actual, expected)
for cuda in [False] + ([True] if torch.cuda.is_available() else []):
for pin_memory in [True, False]:
# FloatTensor
test(lambda: torch.randn(5, requires_grad=True), cuda, pin_memory)
# DoubleTensor
test(lambda: torch.randn(5, requires_grad=True, dtype=torch.double), cuda, pin_memory)
# Sparse tensor
x = torch.sparse_coo_tensor(torch.tensor([[1, 1]]).long(), torch.tensor([1., 1.]), requires_grad=True)
test(lambda: x, cuda, pin_memory)
@unittest.skipIf(not TEST_CUDA, "test requires CUDA")
def test_graph_save_on_cpu_cuda(self):
def f(x):
a = x + 1
return a * a
# with grad
a = torch.ones(1, requires_grad=True, device="cuda")
y = f(a)
memory_with_grad = torch.cuda.memory_allocated()
del a
del y
# without grad
a = torch.ones(1, requires_grad=True, device="cuda")
with torch.no_grad():
y = f(a)
memory_without_grad = torch.cuda.memory_allocated()
self.assertGreater(memory_with_grad, memory_without_grad)
del a
del y
# with hooks
with torch.autograd.graph.save_on_cpu():
a = torch.ones(1, requires_grad=True, device="cuda")
y = f(a)
memory_with_hooks = torch.cuda.memory_allocated()
self.assertEqual(memory_with_hooks, memory_without_grad)
def test_pynode_destruction_deadlock(self):
script = """
import torch
class Foo(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
return x.clone()
@staticmethod
def forward(ctx, gO):
return gO.clone()
def get_out():
inp = torch.rand(2, requires_grad=True)
# The python function is first so that it runs
# last in the backward pass
right = Foo.apply(inp)
# An op that creates new memory
left1 = inp.clone()
# An op that saves its input
left2 = left1 ** 2
# Inplace modify so that the backward for
# left2 always raises an error
left1 += 1
# An op that takes both side as input.
# After running, both side's last op will be in
# the ready queue
# And the op for left will run first as it was
# executed last during the forward
out = left2 + right
return out
# Nothing should be global variables here as, from what
# I can see, python leaks all the global objects
get_out().sum().backward()
# This used to deadlock when the PyNode is being destroyed after
# the error is raised.
"""
try:
subprocess.check_output(
[sys.executable, '-c', script],
stderr=subprocess.STDOUT,
# On Windows, opening the subprocess with the default CWD makes `import torch`
# fail, so just set CWD to this script's directory
cwd=os.path.dirname(os.path.realpath(__file__)),
# It is ok to have an extra long timeout here as a timeout means the test failed
timeout=20)
except subprocess.TimeoutExpired as e:
self.fail(msg="Example code timed out! See the code sample in the test for details.")
except subprocess.CalledProcessError as e:
err_msg = "RuntimeError: one of the variables needed for gradient computation"
self.assertTrue(err_msg in e.output.decode("utf-8"))
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 bernoulli_scalar():
return torch.tensor(0, dtype=torch.uint8).bernoulli_()
class TestAutogradForwardModeBatchedGrad(TestCase):
def test_out_of_place_basic(self):
a = torch.rand(4, 4, dtype=torch.double, requires_grad=True)
b = torch.rand(4, 4, dtype=torch.double, requires_grad=True)
self.assertTrue(gradcheck(torch.sin, a, check_forward_ad=True, check_batched_grad=True,
check_batched_forward_grad=True))
self.assertTrue(gradcheck(torch.add, (a, b), check_forward_ad=True, check_batched_grad=True,
check_batched_forward_grad=True))
def test_out_of_place_not_same_layout(self):
input = torch.zeros([2, 2]).transpose(0, 1)
tangent = torch.zeros([2, 2, 2])
def jvp(tangent):
with fwAD.dual_level():
x = fwAD.make_dual(input, tangent)
return fwAD.unpack_dual(x)[1]
x_tangent = torch._vmap_internals._vmap(jvp, 0, 0)(tangent)
self.assertIsNot(x_tangent, tangent)
def test_inplace_on_view_same_layout(self):
input = torch.zeros([2, 2])
tangent = torch.zeros([2, 2, 2])
base = torch.zeros([2, 2])
view = base.view_as(base)
def jvp(tangent):
with fwAD.dual_level():
x = fwAD.make_dual(input, tangent)
view.copy_(x)
return fwAD.unpack_dual(x)[1], fwAD.unpack_dual(view)[1], fwAD.unpack_dual(view._base)[1]
x_tangent, view_tangent, base_tangent = torch._vmap_internals._vmap(jvp, 0, 0)(tangent)
self.assertFalse(view_tangent._is_view()) # Optimization to share the same tensor!
self.assertIs(view_tangent, base_tangent)
self.assertIs(x_tangent, tangent)
def test_inplace_on_view_not_same_layout(self):
input = torch.zeros([2, 2])
tangent = torch.zeros([2, 2, 2])
view = torch.zeros([2, 2]).transpose(0, 1)
def jvp(tangent):
with fwAD.dual_level():
x = fwAD.make_dual(input, tangent)
view.copy_(x)
return fwAD.unpack_dual(x)[1], fwAD.unpack_dual(view)[1], fwAD.unpack_dual(view._base)[1]
x_tangent, view_tangent, base_tangent = torch._vmap_internals._vmap(jvp, 0, 0)(tangent)
self.assertIs(view_tangent._base, base_tangent)
self.assertIs(x_tangent, tangent)
self.assertIsNot(view_tangent, tangent)
def test_metadata_check_for_storage_numel_skipped(self):
# See: test_metadata_check_checks_storage_numel for the reverse of this test
primal = torch.randn(5)[:4].detach()
self.assertEqual(len(primal.storage()), 5)
tangent = torch.randn(10, 4)
def jvp(tangent):
with fwAD.dual_level():
dual = fwAD.make_dual(primal, tangent)
_, unpacked_tangent = fwAD.unpack_dual(dual)
# No copy is made
self.assertIs(tangent, unpacked_tangent)
# as_strided raises
with self.assertRaisesRegex(RuntimeError, "can access memory outside of `tensor`"):
dual.as_strided((5,), (1,), 0)
return unpacked_tangent
torch._vmap_internals._vmap(jvp, 0, 0)(tangent)
class TestAutogradForwardMode(TestCase):
def tearDown(self):
# Ensure that a failing test won't make others fail
while fwAD._current_level >= 0:
fwAD.exit_dual_level()
super().tearDown()
def test_forward_level_cleanup(self):
def get_tensor_and_weak_ref():
# Create a new Tensor and weak reference
t = torch.rand(2, requires_grad=True)
return t, torch._C._WeakTensorRef(t)
# Sanity check that the helper function works as expected
t, t_ref = get_tensor_and_weak_ref()
self.assertFalse(t_ref.expired())
del t
self.assertTrue(t_ref.expired())
# Main test code
foo = torch.rand(2)
with fwAD.dual_level():
tangent, tangent_ref = get_tensor_and_weak_ref()
self.assertFalse(tangent_ref.expired())
dual = fwAD.make_dual(foo, tangent)
self.assertFalse(tangent_ref.expired())
# Make sure that the tangent we provided has been re-used as is
self.assertTrue(fwAD.unpack_dual(dual)[1] is tangent)
# Make sure that dual is keeping the tangent alive
del tangent
self.assertFalse(tangent_ref.expired())
# Make sure that the dual level does not keep the c++
# version of the tangent alive
del dual
self.assertTrue(tangent_ref.expired())
def test_size_check(self):
foo = torch.rand(2)
tangent = torch.rand(3)
with fwAD.dual_level():
with self.assertRaisesRegex(RuntimeError, "Trying to set a forward gradient that has a different size"):
dual = fwAD.make_dual(foo, tangent)
dual = fwAD.make_dual(foo, tangent[1:])
def test_metadata_check_checks_storage_numel(self):
primal = torch.randn(5)[:4].detach()
self.assertEqual(len(primal.storage()), 5)
tangent = torch.randn(4)
with fwAD.dual_level():
dual = fwAD.make_dual(primal, tangent)
_, unpacked_tangent = fwAD.unpack_dual(dual)
# # Verify that mutating unpacked tangent does not affect the original tangent
tangent_clone = tangent.clone()
unpacked_tangent *= 2
self.assertTrue(torch.allclose(tangent_clone, tangent))
# as_strided runs without error
dual.as_strided((5,), (1,), 0)
def test_metadata_check_checks_ignores_size_zero(self):
a = torch.ones(0).as_strided((0, 1,), (1, 1,), 0)
b = torch.ones(0).as_strided((0, 1,), (1, 0,), 0)
with fwAD.dual_level():
dual = fwAD.make_dual(a, b)
torch.diagonal(dual, offset=0)
input = torch.rand([0, 1], dtype=torch.complex128, requires_grad=True)
func = partial(torch.diagonal, offset=0)
torch.autograd.gradcheck(func, (input,), check_forward_ad=True)
def test_metadata_check_when_primal_has_conj_bit(self):
# Make sure the _has_same_storage_numel is a fallthrough, so that
# conj bit does not materialize. If it materializes it would
# cause the layout check to fail for views that do not index the
# the entire storage.
a = torch.randn(2, 2, dtype=torch.cdouble).conj()
b = torch.rand_like(a)
self.assertTrue(torch.is_conj(a))
self.assertEqual(len(a.storage()), len(b.storage()))
with fwAD.dual_level():
dual = fwAD.make_dual(a, b)
dual[1:]
def test_metadata_check_when_primal_has_neg_bit(self):
# Make sure the _has_same_storage_numel is a fallthrough, so that
# conj bit does not materialize. If it materializes it would
# cause the layout check to fail for views that do not index the
# the entire storage.
a = torch.randn(2, 2, dtype=torch.cdouble).conj().imag
b = torch.randn(2, 2, dtype=torch.cdouble).imag
self.assertTrue(torch.is_neg(a))
self.assertEqual(len(a.storage()), len(b.storage()))
with fwAD.dual_level():
dual = fwAD.make_dual(a, b)
dual[1:]
def test_metadata_check_check_conj(self):
keys = {
"NEITHER": lambda x: x,
"CONJ": lambda x: x.conj(),
"NEG": lambda x: x._neg_view()
}
for primal_key, tangent_key in product(keys, keys):
x = keys[primal_key](torch.randn(2, 3, 4, dtype=torch.cdouble))
t = keys[tangent_key](torch.randn(2, 3, 4, dtype=torch.cdouble))
if primal_key == tangent_key:
with fwAD.dual_level():
dual = fwAD.make_dual(x, t)
self.assertTrue(fwAD.unpack_dual(dual).tangent is t)
torch.real(dual)
torch.imag(dual)
else:
with fwAD.dual_level():
dual = fwAD.make_dual(x, t)
self.assertTrue(fwAD.unpack_dual(dual).tangent is not t)
torch.real(dual)
torch.imag(dual)
def test_metadata_check_ignore_storage_offset_for_zero_numel_tensor(self):
# See https://github.com/pytorch/pytorch/issues/80507
a = torch.tensor([1.]).as_strided((0,), (1,), 1)
b = torch.tensor([1.]).as_strided((0,), (1,), 2)
with fwAD.dual_level():
dual_input = fwAD.make_dual(a, b)
# Check that no copy is made
self.assertIs(fwAD.unpack_dual(dual_input).tangent, b)
a = torch.tensor([1.]).as_strided((1,), (2,), 0)
b = torch.tensor([1.]).as_strided((1,), (1,), 0)
with fwAD.dual_level():
dual_input = fwAD.make_dual(a, b)
dual_input[1:]
# The following test functions want to ensure all the following behaviors:
# - Ensure that default level system in the python binding works
# - Ensure that only level 0 exists and nesting is properly disabled
# - Ensure that printing works fine
# - Ensure that basic packing/unpacking works
# - Ensure that advanced packing/unpacking works
# - For memory / version counter share
# - For backward AD (regular ops)
# - Ensure that view + inplace for both modes work fine
# - Ensure we do proper cleanup on exit of a level
def test_default_level(self):
foo = torch.rand(2)
bar = torch.rand(2)
with fwAD.dual_level():
baz = fwAD.make_dual(foo, bar)
baz_primal, baz_tangent = fwAD.unpack_dual(baz)
self.assertEqual(baz_primal, foo)
# We don't actually need to enforce that these two are the exact same python
# object, feel free to relax in the future
self.assertIs(baz_tangent, bar)
baz_primal, baz_tangent = fwAD.unpack_dual(baz)
self.assertEqual(baz_primal, foo)
self.assertEqual(baz_tangent, None)
def test_nested_level(self):
with fwAD.dual_level() as level:
# For now only level 0 exists
self.assertEqual(level, 0)
with fwAD.dual_level():
with self.assertRaisesRegex(RuntimeError, "Nested forward mode AD is not supported at the moment"):
nest_level = fwAD.enter_dual_level()
def test_set_fw_grad_having_own_fw_grad_at_same_level(self):
foo = torch.rand(2)
bar = torch.rand(2)
baz = torch.rand(2)
with fwAD.dual_level():
dual = fwAD.make_dual(foo, bar)
with self.assertRaisesRegex(RuntimeError, "has a forward gradient at the same level"):
fwAD.make_dual(baz, dual)
def test_codegen_ignores_undefined_outputs(self):
# This test checks that codegen silently ignores undefined outputs
# Below, grad_input is specified as False in grad_output_mask, so
# convolution backward will return a undefined tensor in that position.
# Note that for this test to work we need to make sure either grad_output
# or weight to be a dual tensor, so grad_input requires forward grad
weight = torch.randn(6, 1, 30, 30)
inp = torch.rand((1, 1, 32, 32))
out = torch.nn.functional.conv2d(inp, weight)
grad_out = torch.ones_like(out)
with fwAD.dual_level():
dual_weight = fwAD.make_dual(weight, torch.ones_like(weight))
grad_input, _, _ = torch.ops.aten.convolution_backward(
grad_out, inp, dual_weight, (0,),
(1, 1), (0, 0), (1, 1), False, (0, 0), 1, (False, True, False))
self.assertIsNone(grad_input)
def test_make_dual_inference_tensor_in_inference_mode(self):
with torch.inference_mode():
foo = torch.rand(2)
bar = torch.rand(2)
foo_copy = foo.clone()
with fwAD.dual_level():
dual = fwAD.make_dual(foo, bar)
self.assertFalse(dual._is_view())
dual += 1
self.assertFalse(torch.allclose(foo, foo_copy))
def test_make_dual_torch_dispatch(self):
counter = [0]
class MySubclass(torch.Tensor):
def __new__(cls, data=None):
return torch.Tensor._make_subclass(cls, data)
__torch_function__ = torch._C._disabled_torch_function_impl
@classmethod
def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
if func.overloadpacket == torch.ops.aten.alias:
counter[0] += 1
# Make sure we can re-enable autograd here
with torch.overrides.enable_reentrant_dispatch():
foo = torch.rand(1, requires_grad=True)
self.assertIsNotNone(foo.exp().grad_fn)
with no_dispatch():
return func(*args, **kwargs)
a = torch.tensor(1.)
s = MySubclass(a)
with fwAD.dual_level():
# Only the primal has "alias" called on it
fwAD.make_dual(s, torch.rand_like(s))
self.assertEqual(counter[0], 1)
fwAD.make_dual(torch.rand_like(s), s)
self.assertEqual(counter[0], 1)
def test_make_dual_forbid_integral_dtype(self):
primal_f = torch.ones(2, 2, dtype=torch.float)
primal_l = torch.ones(2, 2, dtype=torch.long)
tangent_f = torch.ones(2, 2, dtype=torch.float)
tangent_l = torch.ones(2, 2, dtype=torch.long)
with fwAD.dual_level():
# Float Primal and Long Tangent
with self.assertRaisesRegex(ValueError, "Expected tangent to be floating point or complex"):
fwAD.make_dual(primal_f, tangent_l)
# Long Primal and Long Tangent
with self.assertRaisesRegex(ValueError, "Expected primal to be floating point or complex"):
fwAD.make_dual(primal_l, tangent_l)
# Long Primal and Float Tangent
with self.assertRaisesRegex(ValueError, "Expected primal to be floating point or complex"):
fwAD.make_dual(primal_l, tangent_f)
def test_print(self):
with fwAD.dual_level() as level:
a = torch.rand(3)
self.assertFalse("tangent=" in str(a))
b = fwAD.make_dual(a, torch.rand(3))
self.assertFalse("tangent=" in str(a))
self.assertTrue("tangent=" in str(b))
b_primal, b_tangent = fwAD.unpack_dual(b)
self.assertFalse("tangent=" in str(b_primal))
self.assertFalse("tangent=" in str(b_tangent))
def test_basic_packing_unpacking(self):
foo = torch.rand(2)
bar = torch.rand(2)
with fwAD.dual_level():
baz = fwAD.make_dual(foo, bar)
baz_primal, baz_tangent = fwAD.unpack_dual(baz)
self.assertEqual(baz_primal, foo)
self.assertIs(baz_tangent, bar)
# Check unpacked dual is returned as a named tuple
# NB: Every invocation of unpack_dual returns a new tensor view
self.assertIsNot(baz_primal, fwAD.unpack_dual(baz).primal)
self.assertEqual(baz_primal, fwAD.unpack_dual(baz).primal)
self.assertIs(baz_tangent, fwAD.unpack_dual(baz).tangent)
# Check that packing/unpacking did not change the input
foo_primal, foo_tangent = fwAD.unpack_dual(foo)
self.assertEqual(foo_primal, foo)
self.assertIsNone(foo_tangent)
def test_advanced_packing_unpacking(self):
foo = torch.rand(2)
bar = torch.ones(2)
# Memory and version counter check
with fwAD.dual_level():
dual = fwAD.make_dual(foo, bar)
# Ensure that they are sharing memory and version counter
self.assertEqual(dual.storage().data_ptr(), foo.storage().data_ptr())
# Ensure we properly share the version counter
self.assertEqual(foo._version, dual._version)
foo.add_(1)
self.assertEqual(foo._version, dual._version)
# Unpacking should only create aliases as well
dual_primal, dual_tangent = fwAD.unpack_dual(dual)
self.assertEqual(dual_primal.storage().data_ptr(), foo.storage().data_ptr())
self.assertEqual(dual_tangent.storage().data_ptr(), bar.storage().data_ptr())
# And the tangent is actually re-used as-is so it is still the same Tensor
self.assertIs(dual_tangent, bar)
# Ensure we properly share the version counter
self.assertEqual(foo._version, dual_primal._version)
foo.add_(1)
self.assertEqual(foo._version, dual_primal._version)
self.assertEqual(bar._version, dual_tangent._version)
bar.add_(1)
self.assertEqual(bar._version, dual_tangent._version)
# backward mode check
with fwAD.dual_level():
foo.requires_grad_()
bar.requires_grad_()
# Check that backward gradients properly propagates through packing/unpacking
dual = fwAD.make_dual(foo, bar)
p, t = fwAD.unpack_dual(dual)
gfoo, gbar = torch.autograd.grad(p.sum(), (foo, bar), retain_graph=True, allow_unused=True)
self.assertEqual(gfoo, torch.ones_like(foo))
self.assertIsNone(gbar)
gfoo, gbar = torch.autograd.grad(t.sum(), (foo, bar), retain_graph=True, allow_unused=True)
self.assertIsNone(gfoo)
self.assertEqual(gbar, torch.ones_like(bar))
# Check that forward gradients are impacted by detach()
detached_dual = dual.detach()
out = detached_dual * 2
p, t = fwAD.unpack_dual(out)
self.assertFalse(p.requires_grad)
self.assertEqual(p, foo * 2)
self.assertIsNone(t)
# Check that forward gradients are not impacted by no_grad
with torch.no_grad():
out = dual * 3
p, t = fwAD.unpack_dual(out)
self.assertFalse(p.requires_grad)
self.assertFalse(t.requires_grad)
self.assertEqual(p, foo * 3)
self.assertEqual(t, bar * 3)
# Check that forward gradients are not impacted by inplace detach
dual = dual.clone()
dual.detach_()
out = dual * 2
p, t = fwAD.unpack_dual(out)
self.assertFalse(p.requires_grad)
self.assertEqual(p, foo * 2)
self.assertIsNone(t)
def test_view_inplace_non_differentiable_views(self):
original_foo = torch.rand(2, dtype=torch.double)
original_bar = torch.ones(2, dtype=torch.double)
# Do clones to be able to compare the values updated inplace
# with the original content of these Tensors
foo = original_foo.clone()
bar = original_bar.clone()
with fwAD.dual_level():
# Note that in this test, we use "update" to mean computing the right tangent for the dual
# All the inplace operations here are expected to update the primal value of the Tensors but
# not always their tangents.
# Also all mentions of "non differentiable view" here means non forward differentiable view
# unless specified otherwise.
# See note [Forward Grad View/inplace] for more details on how these views work.
# Check that inplace ops do not update non-differentiable views
# Non differentiable view
dual = fwAD.make_dual(foo, bar)
dual *= 2
# Check that non differentiable view's tangent was not updated
self.assertIsNone(fwAD.unpack_dual(foo)[1])
# Check that the computed result is correct
self.assertEqual(bar, original_bar * 2)
self.assertEqual(fwAD.unpack_dual(dual)[1], original_bar * 2)
self.assertEqual(foo, original_foo * 2)
self.assertEqual(fwAD.unpack_dual(dual)[0], original_foo * 2)
# Other non differentiable view
dual_primal, dual_tangent = fwAD.unpack_dual(dual)
self.assertIsNone(fwAD.unpack_dual(dual_primal)[1])
self.assertIsNone(fwAD.unpack_dual(dual_tangent)[1])
dual_primal *= 2
# Ensure dual's tangent did not change
self.assertEqual(fwAD.unpack_dual(dual)[0], original_foo * 4)
self.assertEqual(fwAD.unpack_dual(dual)[1], original_bar * 2)
dual_tangent *= 2
# Ensure dual's primal did not change
self.assertEqual(fwAD.unpack_dual(dual)[0], original_foo * 4)
self.assertEqual(fwAD.unpack_dual(dual)[1], original_bar * 4)
def test_view_inplace_differentiable_views(self):
original_foo = torch.rand(2)
original_bar = torch.ones(2)
# Do clones to be able to compare the values updated inplace
# with the original content of these Tensors
foo = original_foo.clone()
bar = original_bar.clone()
with fwAD.dual_level():
# Check that inplace ops do update differentiable view but stop at non differentiable ones
# A non differentiable view
dual = fwAD.make_dual(foo, bar)
# A differentiable view
view = dual.narrow(0, 0, 1)
view *= 2
# Check that non differentiable view was not updated
self.assertIsNone(fwAD.unpack_dual(foo)[1])
# Check that differentiable view was updated
self.assertEqual(fwAD.unpack_dual(dual)[1], torch.tensor([2., 1.]))
self.assertEqual(fwAD.unpack_dual(view)[1], torch.tensor([2.]))
# Check that we track differentiable view even for Tensors that are not dual
baz = torch.rand(2)
baz += dual
self.assertEqual(fwAD.unpack_dual(baz)[1], fwAD.unpack_dual(dual)[1])
# Updates on view should as well
baz = torch.rand(2)
baz[0] = dual[0]
self.assertEqual(fwAD.unpack_dual(baz)[1][0], fwAD.unpack_dual(dual)[1][0])
# Unused values get a gradient of 0
self.assertEqual(fwAD.unpack_dual(baz)[1][1], 0.)
# Check that forward non-differentiable views do prevent gradient update
baz = torch.rand(2)
view = baz.detach()
view += dual
self.assertIsNone(fwAD.unpack_dual(baz)[1])
def test_view_inplace_always_creates_a_view(self):
# See https://github.com/pytorch/pytorch/issues/67800
# The codepath may depend on the op. At the time writing, when self is not a dual tensor
# the resulting forward grad for self for...
# - add_ has the same layout as self
# - mul_ has the same layout as other
# This is kind of fragile because the above depends on how the forward grad expression
# is written. For add and mul at least, the output inherits the layout of LHS.
# We want to handle at least these two cases.
inplace_binary_ops = ( # Add more to this list?
lambda x, y: x.add_(y),
lambda x, y: x.mul_(y),
lambda x, y: x.copy_(y),
)
for inplace_binary_op in inplace_binary_ops:
base = torch.randn(2, 2)
view = base.transpose(0, 1)
primal = torch.randn(2, 2)
tangent = torch.randn(2, 2)
with fwAD.dual_level():
dual = fwAD.make_dual(primal, tangent)
inplace_binary_op(view, dual)
# Verify that a view relationship is created for both the primal and tangent
p, t = fwAD.unpack_dual(base)
p_clone = p.clone()
t_clone = t.clone()
view *= 2
p, t = fwAD.unpack_dual(base)
self.assertTrue(torch.allclose(p_clone * 2, p))
self.assertTrue(torch.allclose(t_clone * 2, t))
def test_grad_cleanup(self):
foo = torch.rand(2)
bar = torch.rand(2)
baz = torch.rand(2)
with fwAD.dual_level():
dual = fwAD.make_dual(foo, bar)
self.assertIsNone(fwAD.unpack_dual(foo)[1])
self.assertIs(fwAD.unpack_dual(dual)[1], bar)
self.assertIsNone(fwAD.unpack_dual(dual)[1])
with fwAD.dual_level():
self.assertIsNone(fwAD.unpack_dual(foo)[1])
new_dual = fwAD.make_dual(foo, baz)
dual_primal, dual_tangent = fwAD.unpack_dual(dual)
new_dual_primal, new_dual_tangent = fwAD.unpack_dual(new_dual)
self.assertEqual(dual_primal, new_dual_primal)
self.assertIsNone(dual_tangent)
self.assertEqual(new_dual_tangent, baz)
def test_detach_view_tracking(self):
# Default detach is both forward and backward non-differentiable
foo = torch.rand(2)
foo_weak = torch._C._WeakTensorRef(foo)
out = foo.detach()
del foo
self.assertTrue(foo_weak.expired())
def test_out_variant(self):
with fwAD.dual_level():
foo = fwAD.make_dual(torch.rand(2), torch.rand(2))
bar = torch.rand(2)
with self.assertRaisesRegex(RuntimeError, "out= function"):
torch.add(bar, bar, out=foo)
with self.assertRaisesRegex(RuntimeError, "out= function"):
torch.add(foo, bar, out=bar)
def test_non_differentiable(self):
with fwAD.dual_level():
foo = fwAD.make_dual(torch.rand(2), torch.rand(2))
bar = torch.rand(2)
# No differentiable outputs, shouldn't error
eq = foo == bar
# Inplace
foo.eq_(bar)
def test_create_new_zeros_with_same_meta(self):
new_zeroes_fn = torch.ops.aten._new_zeros_with_same_feature_meta
def check(a, b):
def assert_same_meta(t, target):
for num_bdim in range(t.dim()):
result = new_zeroes_fn(t, target, self_num_batch_dims=num_bdim)
self.assertEqual(result.dim(), target.dim() + num_bdim)
# Check size/strides match for feature dims only
for i in range(num_bdim, result.dim()):
self.assertEqual(result.size()[i], target.size()[i - num_bdim])
self.assertEqual(result.stride()[i], target.stride()[i - num_bdim])
# Check that we generate strides reasonably
if target.is_contiguous():
self.assertTrue(result.is_contiguous())
self.assertEqual(result.storage_offset(), target.storage_offset())
prod_of_t_bdims = reduce(operator.mul, t.size()[:num_bdim], 1)
self.assertEqual(len(result.storage()), len(target.storage()) * prod_of_t_bdims)
# TensorOptions is same
self.assertEqual(result.dtype, target.dtype)
assert_same_meta(a, b)
assert_same_meta(b, a)
a = torch.randn(5, dtype=torch.float)
b = torch.randn(2, 3, 4, dtype=torch.double)
check(a, b)
# non-contiguous case
a = torch.randn(2, 3, 4).transpose(0, 1).contiguous().transpose(0, 1)
b = torch.randn(2, 3, 4)
check(a, b)
a = torch.randn(5).narrow(0, 1, 2)
b = torch.randn(2)
check(a, b)
# tensor is not a view, but still does not index entirety of storage
a = torch.randn(5).resize_(4)
b = torch.randn(4)
check(a, b)
# Zero-numel tensors
a = torch.randn(1, 0, 2)
b = torch.randn(1, 2)
check(a, b)
# Scalar tensor
a = torch.tensor(1.)
b = torch.randn(1, 2)
check(a, b)
def test_backward_graph_destruction(self):
def fn():
a = torch.rand(10, requires_grad=True)
da = fwAD.make_dual(torch.rand_like(a), a)
# Create an object with a c++ cycle as:
# db -> AutogradMeta -> ForwardGrad -> db's grad
# db's grad -> AutogradMeta -> MulBackward
# MulBackward -> SavedVariable -> db
db = da.exp()
with fwAD.dual_level():
fn()
# This test make sure that we don't deadlock on exit of this
# context manager. If you do, there is something wrong with the
# locking of the forward ad level most likely
# Generic device type autograd tests.
class TestAutogradDeviceType(TestCase):
def test_min_max_median_backprops_to_all_values(self, device):
for f in [torch.min, torch.max, torch.median, torch.nanmedian]:
x1 = torch.tensor([1., 0., 1., 0., 1., 0.], device=device, requires_grad=True)
x2 = torch.tensor([float('nan'), float('nan'), float('nan')], requires_grad=True)
for x in [x1, x2]:
y = f(x)
y.backward()
self.assertEqual(x.grad.sum(), 1.)
self.assertEqual((x.grad == 1 / 3).sum(), 3)
def test_scatter_index_reduce_amin_amax_backprops_to_all_values(self, device):
# tests that gradients are evenly distributed when there are multiple max/min values
# tested here instead of adding a SampleInput as the backward for this case is non-differentiable for gradgrad
# as is the case for test_min_max_median_backprops_to_all_values above
fns = (torch.scatter_reduce, torch.index_reduce)
reduces = ('amin', 'amax')
for fn, reduction in product(fns, reduces):
input = torch.randn((2, 3), device=device, dtype=torch.float64, requires_grad=True)
src = input.clone().detach_().requires_grad_(True)
idx = torch.arange(2).to(dtype=torch.long, device=device)
if fn == torch.scatter_reduce:
idx = idx.unsqueeze(-1).expand((2, 3))
gradcheck(fn, (input, 0, idx, src, reduction), check_batched_grad=False)
def test_scatter_index_reduce_prod_gradgrad_error(self, device):
# test that double backward raises an error for the case where 2 zeros in src
# are scattered to the same position in self
input = torch.tensor([1.], device=device, dtype=torch.float64, requires_grad=True)
src = torch.tensor([0., 0.], device=device, dtype=torch.float64, requires_grad=True)
idx = torch.tensor([0, 0], device=device, dtype=torch.long)
for fn in (torch.scatter_reduce, torch.index_reduce):
# check that this case passes on gradcheck
gradcheck(fn, (input, 0, idx, src, 'prod'), check_batched_grad=False)
with self.assertRaisesRegex(RuntimeError, "Double backward is unsupported for"):
gradgradcheck(fn, (input, 0, idx, src, 'prod'))
@skipIfMps # the test doesn't work on MPS as double types are not supported
def test_parameter_resize(self, device):
asd = torch.nn.Parameter(torch.ones(16, dtype=torch.double, device=device))
for i in range(2):
with torch.no_grad():
asd.set_(asd[1:])
asd.grad = None
m = torch.cat((asd, asd))
m.sum().backward()
@skipIfMps # the test doesn't work on MPS as double types are not supported
@dtypes(torch.double, torch.cdouble)
def test_sparse_ctor_getter_backward(self, device, dtype):
# 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, dtype=torch.double, device=device, requires_grad=True)
other = self.genSparseTensor(size, sparse_dim, nnz, is_uncoalesced=True, device=device,
dtype=dtype)[0]
def fn(v):
x = torch.sparse_coo_tensor(i, v, size, dtype=dtype, 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,), check_batched_grad=False)
# FIXME: make gradgradcheck work.
# gradgradcheck(fn, (inp,), check_batched_grad=False)
# 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()))
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
_test(sparse_size + dense_size, len(sparse_size), nnz, device)
@skipMeta
@skipIfMps
@dtypes(torch.double, torch.cdouble)
def test_sparse_backward(self, device, dtype):
class FixedGradientFunction(Function):
@staticmethod
def forward(ctx, x, grad_x):
ctx.save_for_backward(grad_x)
return x
@staticmethod
def backward(ctx, grad_x):
saved_grad_x, = ctx.saved_tensors
return saved_grad_x, None
size = torch.Size([6, 3, 2])
i1 = torch.tensor([
[0, 3, 4],
[0, 2, 2],
], dtype=torch.long)
v1 = make_tensor([3, 2], dtype=dtype, device=device)
sparse_grad1 = torch.sparse_coo_tensor(i1, v1, size, dtype=dtype, device=device)
i2 = torch.tensor([
[0, 1, 3, 4],
[0, 1, 2, 2],
], dtype=torch.long)
v2 = make_tensor([4, 2], dtype=dtype, device=device)
sparse_grad2 = torch.sparse_coo_tensor(i2, v2, size, dtype=dtype, device=device)
dense_grad = torch.rand(size, device=device, dtype=dtype)
fn = FixedGradientFunction
# sparse first
x = torch.randn(size, dtype=dtype, device=device, requires_grad=True)
(fn.apply(x, sparse_grad1) + fn.apply(x, dense_grad) + fn.apply(x, sparse_grad2)).sum().backward()
self.assertEqual(x.grad, dense_grad + sparse_grad1 + sparse_grad2)
# dense first
x = torch.randn(size, dtype=dtype, device=device, requires_grad=True)
(fn.apply(x, dense_grad) + fn.apply(x, sparse_grad1) + fn.apply(x, sparse_grad2)).sum().backward()
self.assertEqual(x.grad, dense_grad + sparse_grad1 + sparse_grad2)
# sparse only
x = torch.randn(size, dtype=dtype, device=device, requires_grad=True)
(fn.apply(x, sparse_grad1) + fn.apply(x, sparse_grad2)).sum().backward()
self.assertEqual(x.grad, sparse_grad1 + sparse_grad2)
# autograd tests via common_method_invocations don't allow input tensors to
# be sparse (RuntimeError: gradcheck expects all tensor inputs are dense when
# check_sparse_nnz is set to False.)
@skipIfMps
def test_sparse_mask_autograd(self, device):
tensor = torch.randn(3, requires_grad=True, device=device)
mask = torch.ones(3, device=device)
mask[1] = 0
mask = mask.to_sparse()
converted = tensor.sparse_mask(mask).to_dense()
converted.sum().backward()
self.assertEqual(tensor.grad, mask.to_dense())
@skipIfMps # the test doesn't work on MPS as double types are not supported
def test_pyscalar_conversions(self, device):
def _test_pyscalar_conversions(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, dtype=torch.double)))
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)
f[0] = -inf
self.assertEqual(float(f), -inf)
# 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))
_test_pyscalar_conversions(lambda x: x.to(device), lambda x: int(x))
@dtypesIfMPS(torch.float32)
@dtypesIfCUDA(torch.half, torch.float, torch.double, torch.int8, torch.int16, torch.int32, torch.int64)
@dtypes(torch.float, torch.double, torch.int8, torch.int16, torch.int32, torch.int64)
def test_set_requires_grad_only_for_floats(self, device, dtype):
def f1():
a = torch.ones(1, dtype=dtype, device=device)
a.requires_grad_()
def f2():
a = torch.ones(1, dtype=dtype, device=device)
a.requires_grad = True
def f3():
torch.ones(1, dtype=dtype, device=device, requires_grad=True)
a = torch.ones(1, dtype=dtype, device=device)
a.requires_grad = False # should always work
a.requires_grad_(False)
for f in [f1, f2, f3]:
if dtype.is_floating_point:
f()
else:
with self.assertRaisesRegex(RuntimeError, 'floating point', msg="dt: {} device: {}".format(a.dtype, a.device)):
f()
@onlyCUDA
def test_advanced_indexing_backwards_large(self, device):
# See https://github.com/pytorch/pytorch/issues/22843
n = (1 << 16)
x = torch.rand(n, 1, device=device, requires_grad=True)
a = x[:, [0]]
a.sum().backward()
self.assertEqual(x.grad, torch.ones(n, 1, device=device))
def test_advanced_indexing_backwards_memory_format(self, device):
# See https://github.com/pytorch/pytorch/issues/36956
shape = (2, 8, 1, 2)
i = torch.randint(1, shape, device=device).contiguous(memory_format=torch.channels_last)
x = torch.randn(shape, requires_grad=True, device=device)
x[i].sum().backward()
def _test_reentrant_parent_error_on_cpu(self, device):
t1 = torch.rand([3, 3], requires_grad=True)
t2 = torch.rand([3, 3], device=device, requires_grad=True)
t3 = torch.rand([3, 3], device=device, requires_grad=True)
# Parent graph cpu graph.
t4 = t1 * t1
t5 = TestAutograd.SimulateBackwardError.apply(t4)
# Child gpu graph (much longer than parent graph).
prev = t2 * t2
for i in range(10):
prev = prev * t2
reentrant_root = prev
class ReentrantFunc(Function):
@staticmethod
def forward(ctx, inp):
return inp.clone()
@staticmethod
def backward(ctx, grad):
# Reentrant backward in child will take much longer.
reentrant_root.backward()
return grad
# Parent gpu graph.
t6 = ReentrantFunc.apply(t3)
t7 = t6 * t6
# Parent graph will error out first, while child graph will continue executing.
with self.assertRaisesRegex(Exception, "Simulate error"):
torch.autograd.backward([t5.sum(), t7.sum()])
# No grads should be accumulated since child graph will stop execution
# after parent receives error.
self.assertIsNone(t2.grad)
self.assertIsNone(t1.grad)
self.assertIsNone(t3.grad)
@onlyCUDA
def test_reentrant_parent_error_on_cpu(self, device):
def _get_cuda_memory_usage():
# we don't need CUDA synchronize because the statistics are not tracked at
# actual freeing, but at when marking the block as free.
num_devices = torch.cuda.device_count()
gc.collect()
return tuple(torch.cuda.memory_allocated(i) for i in range(num_devices))
before = _get_cuda_memory_usage()
# Run as separate function so that gc can clean up everything when we
# check for memory usage.
self._test_reentrant_parent_error_on_cpu(device)
# Wait for autograd thread to cleanup failed tasks.
after = _get_cuda_memory_usage()
start = time.time()
while before != after and time.time() - start < 30:
time.sleep(0.1)
after = _get_cuda_memory_usage()
self.assertEqual(before, after)
@skipIfMps # the test doesn't work on MPS
# TODO: see if these tests can be ported to OpInfos or moved to where's test suite
def test_where_functional(self, device):
x = torch.randn(5, 5, dtype=torch.double, device=device, requires_grad=True)
y = torch.randn(5, 5, dtype=torch.double, device=device, requires_grad=True)
cond = mask_not_all_zeros((5, 5)).to(device=device)
def where(cond, x, y):
return torch.where(cond, x, y)
gradcheck(where, [cond, x, y], raise_exception=True)
gradgradcheck(where, [cond, x, y], [torch.randn(5, 5, device=device)])
x = torch.randn(5, 1, 5, dtype=torch.double, device=device, requires_grad=True)
y = torch.randn(5, 5, 1, dtype=torch.double, device=device, requires_grad=True)
gradcheck(where, [cond, x, y], raise_exception=True)
gradgradcheck(where, [cond, x, y], [torch.randn(5, 5, 5, device=device)])
@skipIfMps # the test doesn't work on MPS
def test_where_scalar(self, device):
x = torch.randn(5, 5, dtype=torch.double, device=device, requires_grad=True)
scalar = 4.
cond = mask_not_all_zeros((5, 5)).to(device=device)
def where_scalar_first(cond, x):
return torch.where(cond, scalar, x)
def where_scalar_second(cond, x):
return torch.where(cond, x, scalar)
gradcheck(where_scalar_first, (cond, x))
gradgradcheck(where_scalar_first, (cond, x))
gradcheck(where_scalar_second, (cond, x))
gradgradcheck(where_scalar_second, (cond, x))
@onlyCUDA
def test_free_unneeded_tensor(self, device):
x = torch.randn(2, 3, 10, 10, device=device, requires_grad=True)
m = torch.randn(1, 3, 1, 1, device=device)
z = x.sum()
base_mem = torch.cuda.memory_allocated()
z = ((x + 2) * m).sum()
end_mem = torch.cuda.memory_allocated()
# In the end the memory usage should remain equal, because neither of
# (x + 2) and ((x + 2) * m) should be kept alive for backward, while the
# previous allocation of z had the same size as the current one.
self.assertEqual(base_mem, end_mem)
@onlyCUDA
def test_pin_memory(self, device):
x = torch.randn(2, 2, dtype=torch.double, 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])
@skipCUDAIfRocm
@onlyCUDA
def test_profiler_emit_nvtx(self, device):
# This test is not intended to ensure correctness of nvtx ranges.
# That would require something a great deal more complex (you'd have to create a
# profile in a subprocess, open it, and parse the sql somehow).
# This test is merely intended to catch if emit_nvtx breaks on construction.
a = torch.tensor([1, 2, 3], dtype=torch.float32, device=device)
with torch.cuda.profiler.profile():
with emit_nvtx():
a.add(1.0)
@onlyCUDA
def test_rnn_backward_to_input_but_not_parameters(self, device):
# this checks whether it is possible to not require
# weight parameters, but require inputs, see #7722
l = torch.nn.LSTM(2, 3).to(device)
for p in l.parameters():
p.requires_grad = False
s = torch.randn(1, 1, 2, requires_grad=True, device=device)
out, _ = l(s)
out.sum().backward()
self.assertFalse(s.grad is None or s.grad.abs().sum().item() == 0)
@skipIfMps # the test doesn't work as randn is not supported with type long
@deviceCountAtLeast(1)
def test_grad_assignment(self, devices):
x = torch.randn(5, 5, device=devices[0])
# Tests that the wrong type raises
with self.assertRaisesRegex(TypeError, "expected to be a Tensor or None"):
x.grad = 0
# Tests that the wrong shape raises
with self.assertRaises(RuntimeError):
x.grad = torch.randn(2, 2, device=devices[0])
# Tests that the wrong dtype raises
with self.assertRaises(RuntimeError):
x.grad = torch.randn(5, 5, dtype=torch.long, device=devices[0])
# Tests that self-assignment raises
with self.assertRaises(RuntimeError):
x.grad = x
# Tests device -> cpu grad assignment raises
if self.device_type != 'cpu':
with self.assertRaises(RuntimeError):
t_cpu = torch.rand(5, 5)
t_cpu.grad = torch.randn(5, 5, device=devices[0])
# Tests half type on CUDA
if self.device_type == 'cuda':
x = x.to(dtype=torch.half, device=devices[0])
x.grad = torch.zeros_like(x)
# Tests cross-device assignment raises
if len(devices) > 1:
x = torch.randn(5, 5, device=devices[0])
with self.assertRaises(RuntimeError):
x.grad = torch.randn(5, 5, device=devices[1])
@dtypesIfMPS(torch.float32)
@deviceCountAtLeast(1)
@dtypes(torch.float, torch.double)
def test_requires_grad_factory(self, devices, dtype):
fns = [torch.ones_like, torch.randn_like]
x = torch.randn(2, 3, dtype=dtype, device=devices[0])
for fn in fns:
for requires_grad in [True, False]:
output = fn(x, dtype=dtype, device=devices[0], requires_grad=requires_grad)
self.assertEqual(requires_grad, output.requires_grad)
self.assertIs(dtype, output.dtype)
self.assertEqual(devices[0], str(x.device))
@deviceCountAtLeast(2)
def test_unused_output_device(self, devices):
from torch.nn.parallel._functions import Broadcast
x = torch.randn(5, 5, dtype=torch.float, device=devices[0], requires_grad=True)
outputs = Broadcast.apply(list(range(len(devices))), x)
y = outputs[-1] * 2
y.sum().backward()
# TODO(#38095): Replace assertEqualIgnoreType. See issue #38095
self.assertEqualIgnoreType(x.grad, torch.ones(5, 5) * 2)
@deviceCountAtLeast(2)
def test_backward_device(self, devices):
# 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] = grad_output.device
return grad_output.clone()
v = torch.randn(1, device=devices[1], requires_grad=True)
Identity.apply(v).backward()
self.assertEqual(str(device[0]), devices[1])
@deviceCountAtLeast(2)
def test_inputbuffer_add_multidevice(self, devices):
input = torch.randn(1, device=devices[0], requires_grad=True)
output = input.to(device=devices[1]) + input.to(device=devices[1])
output.backward()
@onlyCPU
def test_copy_(self, device):
# At the time of writing this test, copy_ is not generated from native_functions.yaml
# there was a bug that bfloat16 was not recognized as floating.
x = torch.randn(10, device=device, requires_grad=True)
floating_dt = floating_types_and(torch.half, torch.bfloat16)
for dt in floating_dt:
y = torch.empty(10, device=device, dtype=dt)
y.copy_(x)
self.assertTrue(y.requires_grad)
z = x.to(torch.bfloat16)
self.assertTrue(z.requires_grad)
def test_copy_forward_ad_broadcasting(self, device):
# copy_ allows the src to have a different shape from self as long as src is
# broadcastable to self. Make sure forward AD handles this case.
primal = torch.rand(3, 3, device=device)
tangent = torch.rand(3, 3, device=device)
non_dual = torch.rand(1, 3, 3, device=device)
with fwAD.dual_level():
dual = fwAD.make_dual(primal, tangent)
non_dual.copy_(dual)
def test_copy_forward_ad_same_layout_copies_grad(self, device):
primal = torch.tensor([[3.], [4.]], device=device)
tangent = torch.tensor([[5.], [6.]], device=device)
with fwAD.dual_level():
x_dual = fwAD.make_dual(primal, tangent)
non_dual = torch.tensor([[1.], [2.]])
non_dual.copy_(x_dual)
self.assertTrue(fwAD.unpack_dual(non_dual).tangent is not tangent)
@onlyCUDA
def test_simple_reentrant_cross_device(self, device):
class ReentrantFunc(Function):
_cpu_mode = True
@staticmethod
def forward(ctx, x):
return x * (x + 2)
@staticmethod
def backward(ctx, grad_output):
with torch.enable_grad():
if ReentrantFunc._cpu_mode:
new_param = torch.randn(2, 2, requires_grad=True)
(new_param ** 2).sum().backward()
else:
new_param = torch.randn(2, 2, device=device, requires_grad=True)
(new_param ** 2).sum().backward()
return grad_output
# Reentrant starts on GPU thread, finishs on GPU thread
x = torch.randn(2, 2, device=device, requires_grad=True)
out = ReentrantFunc.apply(x)
out.sum().backward()
# Reentrant starts on CPU thread, finishs on GPU thread
x = torch.randn(2, 2, requires_grad=True)
# set ReentrantFunc node to GPU to emit tasks to GPU queue
ReentrantFunc._cpu_mode = False
out = ReentrantFunc.apply(x)
out.sum().backward()
# Reentrant starts on GPU thread, finishs on CPU thread
x = torch.randn(2, 2, device=device, requires_grad=True)
# set ReentrantFunc node to CPU to emit tasks to CPU queue
ReentrantFunc._cpu_mode = True
out = ReentrantFunc.apply(x)
out.sum().backward()
@onlyCUDA
def test_cross_device_reentrant_autograd(self, device):
# Output on gpu so that this task will be associated with the gpu thread
def fn_on_gpu(inp):
# Artificially increase the priority of the next op to make sure it runs
# as soon as we reach it before the ops of branch1.
dummy = inp * 2 * 2 * 2 * 2
return inp.to(device=device)
def parent_on_cpu(inp):
# Slow branch of ops on gpu so that the work queue for the gpu thread
# won't empty too quickly. They also have smaller priorities than the
# ones created by fn_on_gpu
branch1 = inp.to(device=device)
branch1 = branch1 / branch1
branch1 = branch1 / branch1
branch1 = branch1 / branch1
# Perform checkpoint on cpu tensors. So the last op performed in the reentrant
# autograd is an AccumulateGrad that runs on the cpu thread for the gpu thread.
# So the cpu thread will notify the gpu thread with an empty NodeTask.
branch2 = checkpoint(fn_on_gpu, inp)
out = branch2 + branch1
return out
inp = torch.rand(2, requires_grad=True)
out = parent_on_cpu(inp)
# This will segfault if the empty NodeTask is not handled properly in the
# gpu thread ReadyQueue
out.sum().backward()
def test_inplace_on_view_backprop_base(self, device):
# modify view and back-prop through base
root = torch.randn(2, 2, device=device, requires_grad=True)
x = root.clone()
v1 = x.narrow(0, 0, 1)
v1.mul_(2)
x.sum().backward()
self.assertEqual(root.grad.tolist(), [[2, 2], [1, 1]])
def test_inplace_on_view_backprop_view_of_view(self, device):
# modify view and backprop through view-of-view
root = torch.randn(2, 2, device=device, 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.tolist(), [[2, 2], [0, 0]])
def test_inplace_on_view_of_view(self, device):
# modify view-of-view and backprop through base
root = torch.randn(2, 2, device=device, 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.tolist(), [[1, 2], [1, 1]])
@skipIfMps # the test doesn't work on MPS as double types are not supported
def test_inplace_on_view_then_no_grad(self, device):
# Perform an in-place operation on a view of a non-leaf variable.
a = torch.ones(3, 1, dtype=torch.double, device=device, requires_grad=True)
b = a * 2
c = b.view_as(b)
c[0][0] = 3
# Force a graph update with grad disabled.
with torch.no_grad():
c.grad_fn
c.sum().backward()
@skipIfMps # the test doesn't work on MPS as double types are not supported
def test_inplace_on_view_gradcheck(self, device):
# gradcheck modifications to views
a = torch.randn(4, 4, dtype=torch.double, device=device, requires_grad=True)
b = torch.randn(2, 2, dtype=torch.double, device=device, 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(), dtype=torch.double, device=device, requires_grad=True)
gradgradcheck(func, (a, b), (go,))
def test_inplace_on_view_multiple_outputs(self, device):
root = torch.arange(9., dtype=torch.double).reshape(3, 3).requires_grad_()
x = root.clone()
v1 = x.unbind()
with self.assertRaises(RuntimeError):
v1[0].mul_(2)
@skipIfMps # the test doesn't work on MPS as double types are not supported
def test_inplace_on_view_of_multiple_output_view(self, device):
a = torch.rand(10, dtype=torch.double, device=device, requires_grad=True).clone()
b = a.unbind(0)
c = b[0].view_as(b[0])
with self.assertRaises(RuntimeError):
c.mul_(2)
@skipIfMps # MPS backend doesn't support double types
def test_inplace_multiple_output_view_of_view(self, device):
a = torch.rand(10, dtype=torch.double, device=device, requires_grad=True).clone()
b = a.view_as(a)
c = b.unbind(0)
with self.assertRaises(RuntimeError):
c[0].mul_(2)
@skipIfMps # MPS backend doesn't support double types
def test_inplace_on_view_makes_base_require_grad(self, device):
# in-place modification to view makes base require grad
a = torch.randn(4, 4, dtype=torch.double, device=device, requires_grad=False)
b = torch.randn(4, 2, dtype=torch.double, device=device, 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(), dtype=torch.double, device=device, requires_grad=True)
gradgradcheck(func, (a, b), (go,))
def test_inplace_on_view_backprop_view(self, device):
# modify view and backprop through view
a = torch.tensor([2., 5.], device=device, requires_grad=False)
b = torch.tensor([3.], device=device, requires_grad=True)
res = a.narrow(0, 1, 1).mul_(b)
res.sum().backward()
self.assertEqual(b.grad.tolist(), [5])
self.assertIsNone(a.grad)
@skipIfMps # the test doesn't work on MPS as double types are not supported
def test_inplace_on_view_modify_base(self, device):
# 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, dtype=torch.double, device=device, requires_grad=True)
def fn(r):
x = torch.ones(5, dtype=torch.double, device=device)
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])
@skipIfMps # the test doesn't work on MPS as double types are not supported
def test_inplace_on_view_python(self, device):
# in-place modifications of Python-autograd created view
a = torch.randn(4, 4, dtype=torch.double, device=device, requires_grad=True)
b = torch.randn(2, 2, dtype=torch.double, device=device, 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(), dtype=torch.double, device=device, requires_grad=True)
gradgradcheck(func, (a, b), (go,))
def test_inplace_on_view_non_contig(self, device):
root = torch.ones(2, 3, 2, device=device).select(2, 1).t().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.tolist(), [[1, 2], [1, 1], [1, 1]])
def test_inplace_on_view_multi_output_unsafe(self, device):
for f in [lambda t: t.unsafe_split(1),
lambda t: t.unsafe_split_with_sizes((1, 1, 1)),
lambda t: t.unsafe_chunk(3)]:
a = torch.randn(3, 3, device=device, requires_grad=True)
b = a + a
s1, s2, s3 = f(b)
s1.mul_(s2)
s1.sum().backward()
def test_inplace_on_view_multi_output_safe(self, device):
for f in [lambda t: t.split(1),
lambda t: t.split_with_sizes((1, 1, 1)),
lambda t: t.chunk(3)]:
a = torch.randn(3, 3, device=device, requires_grad=True)
b = a + a
s1, s2, s3 = f(b)
error_msg = 'This view is the output of a function that returns multiple views.'
with self.assertRaisesRegex(RuntimeError, error_msg):
s1.mul_(s2)
@skipIfMps # the test doesn't work on MPS as double types are not supported
def test_mv_grad_stride_0(self, device):
# Reference: https://github.com/pytorch/pytorch/issues/38315
mat = torch.randn(2, 2, dtype=torch.double, device=device)
vec = torch.randn(1, dtype=torch.double, device=device).requires_grad_(True)
def fn(vec):
# Expand inside the function to make sure the input to
# gradcheck does not have overlapping memory
vec = vec.expand(2)
return (mat @ vec).sum()
gradcheck(fn, (vec))
gradgradcheck(fn, (vec))
@onlyCUDA
def test_gradcheck_input_output_different_device(self, device):
x = torch.ones((1,), dtype=torch.double, device="cuda", requires_grad=True)
gradcheck(lambda x: x.to("cpu"), (x,))
x = torch.ones((1,), dtype=torch.double, device="cpu", requires_grad=True)
gradcheck(lambda x: x.to("cuda"), (x,))
def test_strided_leaf_grad_layout(self, device):
# (1) If leaf is non-overlapping and dense, grad's layout should match its leaf.
for fmt_a in (torch.contiguous_format, torch.channels_last):
for fmt_b in (torch.contiguous_format, torch.channels_last):
a = torch.rand((2, 3, 4, 5), device=device).to(memory_format=fmt_a)
b = torch.rand((2, 3, 4, 5), device=device).to(memory_format=fmt_b)
a.requires_grad_()
b.requires_grad_()
# checks (1) for broadcasted gradients
a.sum().backward()
self.assertEqual(a.grad.stride(), a.stride())
b.sum().backward()
self.assertEqual(b.grad.stride(), b.stride())
# checks (1) for non-broadcasted gradients
a.grad = None
b.grad = None
(a * b).sum().backward()
self.assertEqual(a.grad.stride(), a.stride())
self.assertEqual(b.grad.stride(), b.stride())
# (2) If leaf isn't dense, checks that grads are rowmajor contiguous.
c = torch.empty_strided((2, 2), (4, 2), device=device).copy_(torch.rand((2, 2), device=device))
c.requires_grad_()
d = torch.rand((2, 2), device=device)
# checks (2) for broadcasted gradients
c.sum().backward()
self.assertEqual(c.grad.stride(), (2, 1))
# checks (2) for non-broadcasted gradients
c.grad = None
(c * d).sum().backward()
self.assertEqual(c.grad.stride(), (2, 1))
@skipIfMps
def test_copy_r_to_c(self, device):
out_c = torch.empty(3, 2, dtype=torch.cdouble, device=device)
inp_r = torch.randn(3, 2, dtype=torch.double, device=device,
requires_grad=True)
def do_test():
out_c.copy_(inp_r)
out_c.sum().backward()
self.assertEqual(inp_r.grad, torch.ones_like(inp_r))
self.assertNotWarn(do_test)
def test_to_r_to_c(self, device):
def do_test():
inp_r = torch.randn(3, 2, dtype=torch.double, device=device,
requires_grad=True)
out = inp_r.to(torch.complex128)
out.sum().backward()
self.assertEqual(inp_r.grad, torch.ones_like(inp_r))
self.assertNotWarn(do_test)
def test_non_differentiable_ops(self, device):
# Just make sure the op doesn't raise an error
# and resulting tensor has requires_grad=False.
x = torch.tensor([[1, 2], [3, 4.]], requires_grad=True, device=device)
out = torch.isin(x, torch.tensor([2, 3], device=device))
self.assertFalse(out.requires_grad)
x = torch.randn(3, 3, requires_grad=True)
out = torch.signbit(x)
self.assertFalse(out.requires_grad)
def test_warning_in_backward(self, device):
# Test warning during backward are always propagated as python warnings (gh-50209)
# NOTE: For device=cuda, warning gets propagated from a worker thread
a = torch.zeros((), device=device, requires_grad=True)
b = torch._C._nn._test_warn_in_autograd(a)
with self.assertWarnsRegex(UserWarning, "Warn from backward"):
b.backward()
class TestAutogradInferenceMode(TestCase):
def _is_inference_tensor(self, tensor):
try:
err_msg = "Inference tensors do not track version counter"
with self.assertRaisesRegex(RuntimeError, err_msg):
tensor._version
return True
except AssertionError as e:
return False
def test_inference_mode_context_manager(self):
self.assertFalse(torch.is_inference_mode_enabled())
with torch.inference_mode():
self.assertTrue(torch.is_inference_mode_enabled())
with torch.inference_mode(False):
self.assertFalse(torch.is_inference_mode_enabled())
self.assertTrue(torch.is_inference_mode_enabled())
self.assertFalse(torch.is_inference_mode_enabled())
def test_inference_mode_decorator(self):
for mode in (True, False):
@torch.inference_mode(mode)
def func(x):
self.assertEqual(torch.is_inference_mode_enabled(), mode)
return x * x
for requires_grad in (True, False):
c = torch.ones(1, 2, 3, requires_grad=requires_grad)
d = func(c)
self.assertTrue(not mode or torch.is_inference(d))
self.assertEqual(d.requires_grad, requires_grad and not mode)
def test_inference_mode_tensor_creation(self):
with torch.inference_mode():
# new tensors created through constructors are inference tensors
c = torch.ones(1, 2, 3)
self.assertFalse(c.requires_grad)
self.assertTrue(torch.is_inference(c))
# requires_grad doesn't change inference tensor behavior in InferenceMode
tmp = torch.ones(1, 2, 3, requires_grad=True)
self.assertTrue(tmp.requires_grad)
self.assertTrue(torch.is_inference(tmp))
tmp = torch.ones(1, 2, 3).requires_grad_(False)
self.assertFalse(tmp.requires_grad)
self.assertTrue(torch.is_inference(tmp))
def test_inference_mode_existing_autograd_session(self):
s = torch.ones(1, 2, 3, requires_grad=True)
a = s.clone()
# `a` gets saved outside of inference mode
out = a * a
with torch.inference_mode():
a.add_(2)
self.assertFalse(torch.is_inference(a))
# tensors created outside of inference mode aren't
# inference tensors, so they will still have their
# version counters tracked
err_msg = ("one of the variables needed for gradient computation has been "
"modified by an inplace operation")
with self.assertRaisesRegex(RuntimeError, err_msg):
out.backward(torch.ones_like(out))
def test_inference_mode_inf_tensor_in_inf_mode_functional_op(self):
def functional_op(x):
return x * x
with torch.inference_mode():
for requires_grad in (True, False):
c = torch.ones(1, 2, 3, requires_grad=requires_grad)
# performing a non-view operation produces a inference tensor
# that does not require grad
func_out = functional_op(c)
self.assertTrue(torch.is_inference(func_out))
self.assertFalse(func_out.requires_grad)
def test_inference_mode_inf_tensor_in_inf_mode_inplace_op(self):
@torch.inference_mode()
def run_test(fn):
for requires_grad in (True, False):
c = torch.ones(1, 2, 3, requires_grad=requires_grad)
# after performing inplace operation, tensor is still
# an inference tensor
fn(c)
self.assertTrue(torch.is_inference(c))
self.assertEqual(c.requires_grad, requires_grad)
run_test(lambda x: x.add_(2))
run_test(lambda x: x.transpose_(0, 1))
def test_inference_mode_inf_tensor_in_inf_mode_view_op(self):
with torch.inference_mode():
for requires_grad in (True, False):
c = torch.ones(1, 2, 3, requires_grad=requires_grad)
# perform view operation produces inference tensor
# that does not require grad
view_out = c.view(-1)
self.assertTrue(torch.is_inference(view_out))
self.assertFalse(view_out.requires_grad)
def test_inference_mode_inf_tensor_in_normal_mode_functional_op(self):
def functional_op(x):
return x * x
for requires_grad in (True, False):
with torch.inference_mode():
c = torch.ones(1, 2, 3, requires_grad=requires_grad)
func_out = functional_op(c)
self.assertFalse(torch.is_inference(func_out))
self.assertFalse(func_out.requires_grad)
self.assertTrue(func_out.is_leaf)
def test_inference_mode_inf_tensor_in_normal_mode_inplace_op(self):
def run_test(fn):
for requires_grad in (False, True):
with torch.inference_mode():
c = torch.ones(1, 2, 3, requires_grad=requires_grad)
if requires_grad:
# leaf variable that requires grad is being used in an inplace
# operation when requires_grad=True
pass
else:
err_msg = "Inplace update to inference tensor outside InferenceMode"
with self.assertRaisesRegex(RuntimeError, err_msg):
fn(c)
run_test(lambda x: x.add_(2))
run_test(lambda x: x.transpose_(0, 1))
def test_inference_mode_inf_tensor_in_normal_mode_view_op(self):
for requires_grad in (True, False):
with torch.inference_mode():
c = torch.ones(1, 2, 3, requires_grad=requires_grad)
out = c.view(-1)
self.assertTrue(torch.is_inference(out))
self.assertFalse(out.requires_grad)
self.assertFalse(out._is_view())
self.assertTrue(out.is_leaf)
def test_normal_tensor_inplace_output_in_inference_mode(self):
def run_test(fn):
for requires_grad in (True, False):
s = torch.ones(1, 2, 3, requires_grad=requires_grad)
a = s.clone()
with torch.inference_mode():
fn(a)
self.assertFalse(torch.is_inference(a))
self.assertEqual(a.requires_grad, requires_grad)
# inplace -> inplace
fn(a)
self.assertFalse(torch.is_inference(a))
self.assertEqual(a.requires_grad, requires_grad)
# inplace -> inplace -> view
view_out = a.view(-1)
self.assertFalse(torch.is_inference(view_out))
self.assertEqual(view_out.requires_grad, requires_grad)
run_test(lambda x: x.add_(2))
run_test(lambda x: x.transpose_(0, 1))
def test_normal_tensor_inplace_output_in_normal_mode(self):
def run_test(fn):
for requires_grad in (True, False):
s = torch.ones(1, 2, 3, requires_grad=requires_grad)
a = s.clone()
with torch.inference_mode():
fn(a)
self.assertFalse(torch.is_inference(a))
self.assertEqual(a.requires_grad, requires_grad)
fn(a)
self.assertFalse(torch.is_inference(a))
self.assertEqual(a.requires_grad, requires_grad)
# inplace -> inplace
fn(a)
self.assertFalse(torch.is_inference(a))
self.assertEqual(a.requires_grad, requires_grad)
# inplace -> inplace -> view
view_out = a.view(-1)
self.assertFalse(torch.is_inference(view_out))
self.assertEqual(view_out.requires_grad, requires_grad)
run_test(lambda x: x.add_(2))
run_test(lambda x: x.transpose_(0, 1))
def test_normal_tensor_view_output_in_inference_mode(self):
for requires_grad in (True, False):
s = torch.ones(1, 2, 3, requires_grad=requires_grad)
a = s.clone()
with torch.inference_mode():
out = a.view(-1)
self.assertFalse(torch.is_inference(out))
self.assertEqual(out.requires_grad, requires_grad)
self.assertTrue(out._is_view())
# view -> view
tmp = out.view(-1)
self.assertFalse(torch.is_inference(tmp))
self.assertEqual(tmp.requires_grad, requires_grad)
self.assertTrue(tmp._is_view())
self.assertTrue(tmp.is_leaf)
# view -> view -> inplace
self.assertTrue(torch.is_inference_mode_enabled())
tmp.add_(2)
self.assertFalse(torch.is_inference(tmp))
self.assertEqual(tmp.requires_grad, requires_grad)
# Accessing is_leaf in python tries to update grad_fn and raises:
# A view was created in inference mode and its base or
# another view of its base has been modified inplace in normal mode
# tmp.is_leaf
self.assertEqual(a._version, tmp._version)
def test_normal_tensor_view_output_in_normal_mode(self):
def functional_op(x):
return x * x
for requires_grad in (True, False):
s = torch.ones(1, 2, 3, requires_grad=requires_grad)
a = s.clone()
with torch.inference_mode():
out = a.view(-1)
self.assertFalse(torch.is_inference(out))
self.assertEqual(out.requires_grad, requires_grad)
self.assertTrue(out._is_view())
self.assertTrue(out.is_leaf)
tmp = functional_op(out)
self.assertFalse(torch.is_inference(tmp))
self.assertEqual(tmp.requires_grad, requires_grad)
if requires_grad:
err_msg = "A view was created in inference mode and is being modified inplace"
with self.assertRaisesRegex(RuntimeError, err_msg):
out.add_(2)
pass
else:
out.add_(2)
tmp = out.view(2, 3)
self.assertFalse(torch.is_inference(tmp))
self.assertEqual(tmp.requires_grad, requires_grad)
def test_mix_inference_and_normal_tensor_functional_op(self):
for requires_grad in (True, False):
s = torch.ones(1, 2, 3, requires_grad=requires_grad)
with torch.inference_mode():
c = torch.ones(1, 2, 3, requires_grad=requires_grad)
# add is safe since it doesn't save any variable for backward
out = c.add(s)
self.assertFalse(torch.is_inference(out))
self.assertEqual(out.requires_grad, requires_grad)
if requires_grad:
# leaf inference tensor with requires_grad=True can still have gradient
out.backward(torch.ones_like(out))
self.assertEqual(c.grad, torch.ones_like(c))
if requires_grad:
err_msg = "Inference tensors cannot be saved for backward"
with self.assertRaisesRegex(RuntimeError, err_msg):
c * s
# inference tensor in TensorList input
inputs = [s, c]
with self.assertRaisesRegex(RuntimeError, err_msg):
torch.stack(inputs)
def test_mix_inference_and_normal_tensor_inplace_op(self):
for requires_grad in (True, False):
s = torch.ones(1, 2, 3, requires_grad=requires_grad)
a = s.clone()
with torch.inference_mode():
c = torch.ones(1, 2, 3)
self.assertTrue(torch.is_inference(c))
if requires_grad:
err_msg = "Inference tensors cannot be saved for backward"
with self.assertRaisesRegex(RuntimeError, err_msg):
a.mul_(c)
# inference tensor in TensorList input
err_msg = ("out=... arguments don't support automatic differentiation, "
"but one of the arguments requires grad")
with self.assertRaisesRegex(RuntimeError, err_msg):
torch.mul(s, s, out=c)
else:
a.mul_(c)
err_msg = "Inplace update to inference tensor outside InferenceMode is not allowed"
with self.assertRaisesRegex(RuntimeError, err_msg):
torch.mul(s, s, out=c)
def test_mix_inference_and_normal_tensor_view_op(self):
for requires_grad in (True, False):
s = torch.ones(1, 2, 3, requires_grad=requires_grad)
with torch.inference_mode():
c = torch.ones(1, 2, 3)
# view_as is a composite op which calls view with only one
# tensor argument. So there isn't a mixed inference and normal
# tensor inputs for view ops
tmp1 = c.view_as(s)
self.assertTrue(torch.is_inference(tmp1))
self.assertFalse(tmp1.requires_grad)
# this is fine since its equivalent as s.view(c.sizes()) which
# isn't a mixed input scenario
tmp2 = s.view_as(c)
self.assertFalse(torch.is_inference(tmp2))
self.assertEqual(tmp2.requires_grad, requires_grad)
def test_inference_mode_handle_direct_view_on_rebase(self):
def run_test(fn):
for requires_grad in (True, False):
s = torch.ones(1, 2, 3, requires_grad=requires_grad)
a = s.clone()
with torch.inference_mode():
view_out = a.view_as(a)
if requires_grad:
err_msg = "A view was created in inference mode and is being modified inplace"
with self.assertRaisesRegex(RuntimeError, err_msg):
fn(view_out)
pass
else:
fn(view_out)
run_test(lambda x: x.add_(2))
run_test(lambda x: x.transpose_(0, 1))
def test_inference_mode_handle_indirect_view_on_rebase(self):
def run_test(fn):
for requires_grad in (True, False):
s = torch.ones(1, 2, 3, requires_grad=requires_grad)
a = s.clone()
with torch.inference_mode():
view_out = a.view(-1)
fn(a)
if requires_grad:
err_msg = "A view was created in inference mode and its base or another view "
with self.assertRaisesRegex(RuntimeError, err_msg):
view_out.grad_fn
pass
else:
view_out.grad_fn
run_test(lambda x: x.add_(2))
run_test(lambda x: x.transpose_(0, 1))
class TestMultithreadAutograd(TestCase):
def _run_py_multithread_fn(self, fn, args=(), num_threads=10, kwargs=None):
class PropagatingThread(threading.Thread):
'''Helper class to propagate exception from child
thread to main thread on join.
Reference: https://stackoverflow.com/a/31614591/5602957
'''
def run(self):
self.exception = None
try:
self.ret = super(PropagatingThread, self).run()
except Exception as e:
self.exception = e
def join(self, timeout=None):
super(PropagatingThread, self).join(timeout)
if self.exception:
raise self.exception from self.exception
return self.ret
threads = []
for _ in range(num_threads):
p = PropagatingThread(target=fn, args=args)
p.start()
threads.append(p)
for p in threads:
p.join()
def test_multithreaded_exception_propagation(self):
# Test whether exception in child thread
# are propagated to main thread.
def fn():
self.assertTrue(False)
with self.assertRaises(AssertionError):
self._run_py_multithread_fn(fn)
def test_simple_backward(self):
# simple multithreaded backward that create threads in the beginning of training
# and everything else is training separately, i.e. inputs, operations, etc.
def train_fn():
x = torch.ones(5, 5, requires_grad=True)
y = (x + 3) * (x + 4) * 0.5
y.sum().backward()
self.assertEqual(x.grad, x + 3.5)
self._run_py_multithread_fn(train_fn)
def test_simple_backward_same_input(self):
# simple multithreaded backward with only shared inputs (i.e. This is common
# for things like Hogwild multithreaded training with multiple CPU threads)
def train_fn_backward(x):
y = (x + 3) * (x + 4) * 0.5
y.sum().backward()
x = torch.ones(5, 5, requires_grad=True)
self._run_py_multithread_fn(train_fn_backward, (x,))
# Since we are calling backward from multiple threads
# and all threads share the same input, when we do backward
# concurrently, different backwards will all accumulate to
# the same .grad for each input, and the gradients should
# be equal to num_threads * gradient
self.assertEqual(x.grad, 10 * (x + 3.5))
def train_fn_grad(x):
y = (x + 3) * (x + 4) * 0.5
grads = torch.autograd.grad(y.sum(), x)
self.assertEqual(len(grads), 1)
self.assertEqual(grads[0], x + 3.5)
# since we use functional grad() api, gradients will not
# be accumulate to the same place and should be the same
self._run_py_multithread_fn(train_fn_grad, (x,))
def test_multithread_saved_tensors_hooks(self):
def pack(x):
warnings.warn("pack")
return x
def registers_hooks_for_each_thread():
with torch.autograd.graph.saved_tensors_hooks(pack, lambda x: x):
x = torch.ones(5, 5, requires_grad=True)
with warnings.catch_warnings(record=True) as w:
y = x * x
# should raise two warnings from x being saved twice
self.assertEqual(len(w), 2)
y.sum().backward()
def test_dataparallel_saved_tensors_hooks(self):
def pack(x):
warnings.warn("pack")
return x
_self = self
class Model(torch.nn.Module):
def forward(self, x):
with warnings.catch_warnings(record=True) as w:
y = x * x
if torch.cuda.device_count() >= 2:
# DataParallel is calling the forward in different threads
# without progating TLS, so hooks should not be called here
_self.assertEqual(len(w), 0)
else:
# DataParallel only uses one thread
# so hooks should be called here
_self.assertGreater(len(w), 0)
x = torch.ones(5, 5, requires_grad=True)
model = torch.nn.DataParallel(Model())
with torch.autograd.graph.saved_tensors_hooks(pack, lambda x: x):
model(x)
with warnings.catch_warnings(record=True) as w:
y = x * x
# hooks should be called here
_self.assertGreater(len(w), 0)
def test_python_thread_in_middle(self):
# User might write a network that starts on one CPU thread, then runs its second half
# concurrently with other threads (either via python threading or fork/join calls),
# then calls backward()/grad() on BOTH threads, like a Y pattern from input at the
# bottom to output at the top. This way part of the GraphTask is being shared across
# different threads and we need to ensure user specify retain_graph=True, otherwise
# error out with the correct error message
# Case 1: multiple backward with python threads, retain_graph=False
# should throw error in some threads with no retain_graph.
success_vs_raises = [0, 0]
def train_fn_no_retain_graph(x):
y = x + x ** 2
try:
y.sum().backward()
success_vs_raises[0] += 1
except RuntimeError as error:
success_vs_raises[1] += 1
self.assertRegex(str(error), "Specify retain_graph=True")
x_no_retain = torch.ones(5, 5, requires_grad=True)
y_no_retain = x_no_retain + x_no_retain ** 2
self._run_py_multithread_fn(train_fn_no_retain_graph, (y_no_retain,), num_threads=5)
# at least one thread will be success in this case, all other threads should raise
# with the error that throw to user to recommend them specify retain_graph=True
self.assertTrue(success_vs_raises[0] >= 1)
# multiple backward with python threads, no error with retain_graph=True
def train_fn_retain_graph(x):
y = x + x ** 2
y.sum().backward(retain_graph=True)
x_retain = torch.ones(5, 5, requires_grad=True)
y_retain = x_retain + x_retain ** 2
self._run_py_multithread_fn(train_fn_retain_graph, (y_retain,), num_threads=5)
# result should equal to num_thread * gradients
self.assertEqual(x_retain.grad, 5 * (4 * x_retain ** 3 + 6 * (x_retain ** 2) + 4 * x_retain + 1))
def test_fork_join_in_middle(self):
# multiple backward with jit threads (fork/join primitive)
# similar to test_python_thread_in_middle, we test with retain_graph=False/True
# Case 1: multiple grad() calls with jit threads, retain_graph=False
# should throw error in some threads with no retain_graph.
@torch.jit.script
def train_fn_jit_no_retain(middle, orig_x):
y = middle + middle ** 2
return torch.autograd.grad([y.sum()], [orig_x])
@torch.jit.script
def train_fn_fork_join_calls_no_retain(x):
y_no_retain = (x + 3) * (x + 4) * 0.5
fut = torch.jit._fork(train_fn_jit_no_retain, y_no_retain, x)
grad_hat = train_fn_jit_no_retain(y_no_retain, x)
grad = torch.jit._wait(fut)
return grad, grad_hat
try:
train_fn_fork_join_calls_no_retain(torch.randn(5, 5, requires_grad=True))
except RuntimeError as error:
self.assertRegex(str(error), "Specify retain_graph=True")
# Case 2: no error with retain_graph=True
@torch.jit.script
def train_fn_jit_retain(middle, orig_x):
y = middle + middle ** 2
return torch.autograd.grad([y.sum()], [orig_x], retain_graph=True)
@torch.jit.script
def train_fn_fork_join_calls_retain(x):
y_retain = (x + 3) * (x + 4) * 0.5
fut1 = torch.jit._fork(train_fn_jit_retain, y_retain, x)
fut2 = torch.jit._fork(train_fn_jit_retain, y_retain, x)
grad = train_fn_jit_retain(y_retain, x)
grad1 = torch.jit._wait(fut1)
grad2 = torch.jit._wait(fut2)
return grad, grad1, grad2
grad, grad1, grad2 = train_fn_fork_join_calls_retain(torch.randn(5, 5, requires_grad=True))
self.assertEqual(grad, grad1)
self.assertEqual(grad, grad2)
def test_preserve_backtrace(self):
class Foo(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
return input
@staticmethod
def backward(ctx, *grad):
raise ValueError("something")
t = torch.rand(10, requires_grad=True)
try:
Foo.apply(t).sum().backward()
except Exception:
import traceback
tb = sys.exc_info()[2]
tb_str = "\n".join(traceback.format_tb(tb))
self.assertTrue('raise ValueError("something")' in tb_str)
# TODO(@anjali411): add an OpInfo based test for torch.cat
# Issue: https://github.com/pytorch/pytorch/issues/51627
def test_cat_r_to_c(self):
inp_c = torch.rand(3, 2, dtype=torch.cdouble, requires_grad=True)
inp_r = torch.randn(3, 2, dtype=torch.double, requires_grad=True)
def fn(x1, x2):
return torch.cat((x1, x2), dim=-1)
torch.autograd.gradcheck(fn, [inp_r, inp_c], check_forward_ad=True)
torch.autograd.gradcheck(fn, [inp_c, inp_r], check_forward_ad=True)
# Import test cases from below autograd/ here. These are found
# implicitly by the loader, so Flake8 thinks they are unused, hence
# the suppressions.
from autograd.test_complex import TestAutogradComplex # noqa: F401
from autograd.test_functional import TestAutogradFunctional # noqa: F401
# e.g., TestAutogradDeviceTypeCPU and TestAutogradDeviceTypeCUDA
instantiate_device_type_tests(
TestAutogradDeviceType,
globals(),
except_for=None
)
instantiate_parametrized_tests(TestAutograd)
if __name__ == '__main__':
run_tests()