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android / platform / external / pytorch / bc032be13e / . / test / test_autograd.py
blob: 5ce0c88a6a7e44322c3adad48ec969e728d5dd32 [file] [log] [blame]
import contextlib
import gc
import sys
import math
import torch
import unittest
import warnings
from copy import deepcopy
from collections import OrderedDict
from itertools import product
from operator import mul
from functools import reduce
import torch.nn.functional as F
from torch.autograd import gradcheck
from torch.autograd.gradcheck import gradgradcheck
from torch.autograd.function import once_differentiable
from common import TestCase, run_tests, skipIfNoLapack
from torch.autograd._functions import *
from torch.autograd import Variable, Function
if sys.version_info[0] == 2:
import cPickle as pickle
else:
import pickle
PRECISION = 1e-4
@contextlib.contextmanager
def backward_engine(engine):
_prev_engine = Variable._execution_engine
Variable._execution_engine = engine()
try:
yield
finally:
Variable._execution_engine = _prev_engine
def graph_desc(fn):
if fn is None:
return 'None'
result = type(fn).__name__ + '('
next_functions = fn.next_functions
for next_fn, _ in next_functions:
result += graph_desc(next_fn)
result += ', '
if next_functions:
result = result[:-2]
return result + ')'
class TestAutograd(TestCase):
def _function_test(self, cls):
x = Variable(torch.randn(5, 5), requires_grad=True)
y = Variable(torch.randn(5, 5), requires_grad=True)
result = cls.apply(x, 2, y)
go = Variable(torch.ones(1), requires_grad=True)
result.sum().backward(go)
self.assertEqual(x.grad.data, y.data + torch.ones(5, 5))
self.assertEqual(y.grad.data, x.data + torch.ones(5, 5) * 2)
self.assertFalse(x.grad.volatile)
self.assertFalse(y.grad.volatile)
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, scalar, tensor2):
ctx.scalar = scalar
ctx.save_for_backward(tensor1, tensor2)
return tensor1 + scalar * tensor2 + tensor1 * tensor2
@staticmethod
def backward(ctx, grad_output):
var1, var2 = ctx.saved_variables
# NOTE: self is the test case here
self.assertIsInstance(var1, Variable)
self.assertIsInstance(var2, Variable)
self.assertIsInstance(grad_output, Variable)
return (grad_output + grad_output * var2, None,
grad_output * ctx.scalar + 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.assertEqual(
x_grad_desc,
'Identity(AddBackward(ExpandBackward(AccumulateGrad()), '
'MulBackward(ExpandBackward(AccumulateGrad()), AccumulateGrad())))')
self.assertEqual(
y_grad_desc,
'Identity(AddBackward(MulConstantBackward(ExpandBackward(AccumulateGrad())), '
'MulBackward(ExpandBackward(AccumulateGrad()), AccumulateGrad())))')
def test_once_differentiable(self):
class MyFunction(Function):
@staticmethod
def forward(ctx, tensor1, scalar, tensor2):
ctx.scalar = scalar
ctx.save_for_backward(tensor1, tensor2)
return tensor1 + scalar * tensor2 + tensor1 * tensor2
@staticmethod
@once_differentiable
def backward(ctx, grad_output):
t1, t2 = ctx.saved_tensors
# NOTE: self is the test case here
self.assertTrue(torch.is_tensor(t1))
self.assertTrue(torch.is_tensor(t2))
self.assertTrue(torch.is_tensor(grad_output))
return (grad_output + grad_output * t2, None,
grad_output * ctx.scalar + grad_output * t1)
x, y = self._function_test(MyFunction)
self.assertEqual(graph_desc(x.grad.grad_fn),
'Identity(Error(AccumulateGrad(), None, AccumulateGrad()))')
self.assertEqual(graph_desc(y.grad.grad_fn),
'Identity(Error(AccumulateGrad(), None, AccumulateGrad()))')
def test_accumulate_grad(self):
grad_output = Variable(torch.ones(5, 5))
for start_volatile, end_volatile in product((True, False), repeat=2):
go1 = grad_output.data if start_volatile else grad_output
go2 = grad_output.data if end_volatile else grad_output
x = Variable(torch.randn(5, 5), requires_grad=True)
y = x + 2
y.backward(go1, retain_graph=True)
x_grad = x.grad
x_grad_clone = x.grad.data.clone()
del x
y.backward(go2)
# That's the only case when we can accumulate in-place
if start_volatile and end_volatile:
expected_grad = x_grad_clone * 2
else:
expected_grad = x_grad_clone
self.assertEqual(x_grad.data, expected_grad)
def test_hessian_vector(self):
x = Variable(torch.randn(2, 2), requires_grad=True)
y = Variable(torch.randn(2, 2), requires_grad=True)
z = x ** 2 + y * x + y ** 2
z.backward(Variable(torch.ones(2, 2), requires_grad=True), retain_graph=True)
x_grad = 2 * x.data + y.data
y_grad = x.data + 2 * y.data
self.assertEqual(x.grad.data, x_grad)
self.assertEqual(y.grad.data, y_grad)
grad_sum = 2 * x.grad + y.grad
grad_sum.backward(torch.ones(2, 2))
x_hv = torch.ones(2, 2) * 5
y_hv = torch.ones(2, 2) * 4
self.assertEqual(x.grad.data, x_grad + x_hv)
self.assertEqual(y.grad.data, y_grad + y_hv)
def test_grad(self):
x = Variable(torch.randn(2, 2), requires_grad=True)
y = Variable(torch.randn(2, 2), requires_grad=True)
z = x ** 2 + y * x + y ** 2
z.backward(Variable(torch.ones(2, 2)), retain_graph=True)
x_grad = 2 * x.data + y.data
y_grad = x.data + 2 * y.data
self.assertEqual(x.grad.data, x_grad)
self.assertEqual(y.grad.data, y_grad)
grad_sum = 2 * x.grad + y.grad
x_hv = torch.autograd.grad(
outputs=[grad_sum], grad_outputs=[torch.ones(2, 2)],
inputs=[x], create_graph=True, only_inputs=True)
expected_x_hv = torch.ones(2, 2) * 5
expected_y_hv = torch.ones(2, 2) * 4
self.assertEqual(x_hv[0].data, expected_x_hv)
self.assertEqual(x.grad.data, x_grad)
self.assertEqual(y.grad.data, y_grad)
grad_sum = 2 * x.grad + y.grad
x_hv = torch.autograd.grad(
outputs=grad_sum, inputs=x,
grad_outputs=torch.ones(2, 2),
only_inputs=False)
self.assertEqual(x_hv[0].data, expected_x_hv)
self.assertEqual(x.grad.data, x_grad)
self.assertEqual(y.grad.data, y_grad + expected_y_hv)
def test_grad_nonleaf(self):
x_init = Variable(torch.randn(2, 2), requires_grad=True)
x = x_init
y = Variable(torch.randn(2, 2), requires_grad=True)
grad_output = torch.ones(2, 2)
def fn(x):
return x ** 2 + y * x + y ** 2
for i in range(5):
grad_x, = torch.autograd.grad(
fn(x), x, grad_outputs=grad_output, create_graph=True)
grad_x_expected = 2 * x.data + y.data
self.assertIsNone(y.grad)
self.assertIsNone(x.grad)
self.assertEqual(grad_x.data, grad_x_expected)
x = x + 0.05 * grad_x
val_init = fn(x_init).data.sum()
val_final = fn(x).data.sum()
self.assertGreater(val_final, val_init)
x.backward(grad_output)
self.assertIsNotNone(y.grad)
self.assertIsNotNone(x_init.grad)
def test_grad_nonleaf_many_outputs(self):
# This checks an edge case for function callbacks
# We want to capture two grads of a function, but can only
# register a single callback.
x = Variable(torch.randn(4, 2), requires_grad=True)
a, b = x.chunk(2)
def hook(*grads):
hook_called[0] = True
hook_called = [False]
x.register_hook(hook)
go = torch.randn(2, 2)
grad_a, grad_b = torch.autograd.grad(
(a + 2 * b), [a, b], grad_outputs=go, create_graph=True)
self.assertEqual(grad_a.data, go)
self.assertEqual(grad_b.data, go * 2)
self.assertFalse(hook_called[0])
self.assertIsNone(x.grad)
def test_grad_badcalls(self):
x = Variable(torch.ones(1))
y = x ** 2
with self.assertRaisesRegex(RuntimeError, 'does not require grad'):
torch.autograd.grad(x, y)
with self.assertRaisesRegex(RuntimeError, 'not have been used in the graph'):
torch.autograd.grad(y, x)
x = Variable(torch.ones(1), requires_grad=True)
y = x ** 2
torch.autograd.grad(y, x) # this should succeed now
with self.assertRaisesRegex(RuntimeError, 'unreachable'):
torch.autograd.grad(x, y)
def test_hooks(self):
x = Variable(torch.ones(5, 5), requires_grad=True)
y = Variable(torch.ones(5, 5) * 4, requires_grad=True)
counter = [0]
def bw_hook(inc, grad):
self.assertIsInstance(grad, Variable)
counter[0] += inc
z = x ** 2 + x * 2 + x * y + y
x.register_hook(lambda *args: bw_hook(0, *args))
test = z.register_hook(lambda *args: bw_hook(1, *args))
z.backward(torch.ones(5, 5), retain_graph=True)
self.assertEqual(counter[0], 1)
test2 = z.register_hook(lambda *args: bw_hook(2, *args))
z.backward(torch.ones(5, 5), retain_graph=True)
self.assertEqual(counter[0], 4)
test2.remove()
z.backward(torch.ones(5, 5), retain_graph=True)
self.assertEqual(counter[0], 5)
def bw_hook_modify(grad):
return grad.mul(2)
test.remove()
z.register_hook(bw_hook_modify)
y.grad.data.zero_()
z.backward(torch.ones(5, 5), retain_graph=True)
self.assertEqual(y.grad.data, (x.data + 1) * 2)
y.register_hook(bw_hook_modify)
y.grad.data.zero_()
z.backward(torch.ones(5, 5))
self.assertEqual(y.grad.data, (x.data + 1) * 4)
def test_hooks_cpp(self):
# Tests hooks for autograd function implemented in C++
bn = torch.nn.BatchNorm1d(5, affine=False)
bn.eval()
counter = [0]
def bw_hook(grad):
counter[0] += 1
return grad * 2
x = Variable(torch.ones(5, 5), requires_grad=True)
z = bn(x)
z.register_hook(bw_hook)
z.sum().backward()
self.assertEqual(counter[0], 1, 'bw_hook not called')
self.assertEqual(x.grad.data, torch.ones(5, 5) * 2)
@unittest.skipIf(sys.version_info[0] == 2, "Python 2 doesn't collect cycles involving __del__")
def test_hooks_cycle(self):
import gc
counter = [0]
class GradHook(object):
def __init__(self, var):
self.var = var
def __del__(self):
counter[0] += 1
def __call__(self, *args):
pass
def run_test():
x = Variable(torch.ones(5, 5), requires_grad=True)
y = x * 2
x.register_hook(GradHook(x))
y.register_hook(GradHook(y))
y._backward_hooks[1] = GradHook(y)
run_test()
gc.collect()
self.assertEqual(counter[0], 3)
def test_hook_none(self):
# WARNING: this is a test for autograd internals.
# You should never have to use such things in your code.
class NoneGradientFunction(Function):
def forward(self, x, y):
assert self.needs_input_grad[0]
assert not self.needs_input_grad[1]
return x, y
def backward(self, grad_x, grad_y):
return grad_x, None
fn = NoneGradientFunction()
was_called = [False]
def hook(grad_input, grad_output):
self.assertIsInstance(grad_input, tuple)
self.assertIsInstance(grad_output, tuple)
self.assertIsNotNone(grad_input[0])
self.assertIsNone(grad_input[1])
self.assertIsNotNone(grad_output[0])
self.assertIsNotNone(grad_output[1])
was_called[0] = True
fn.register_hook(hook)
x = Variable(torch.randn(5, 5), requires_grad=True)
y = Variable(torch.randn(5, 5))
sum(fn(x, y)).sum().backward()
self.assertTrue(was_called[0])
def test_backward(self):
v_t = torch.randn(5, 5)
x_t = torch.randn(5, 5)
y_t = torch.rand(5, 5) + 0.1
z_t = torch.randn(5, 5)
grad_output = torch.randn(5, 5)
v = Variable(v_t, requires_grad=True)
x = Variable(x_t, requires_grad=True)
y = Variable(y_t, requires_grad=True)
z = Variable(z_t, requires_grad=True)
v.backward(grad_output)
self.assertEqual(v.grad.data, grad_output)
a = x + (y * z) + 4 * z ** 2 * x / y
a.backward(grad_output)
x_grad = 4 * z_t.pow(2) / y_t + 1
y_grad = z_t - 4 * x_t * z_t.pow(2) / y_t.pow(2)
z_grad = 8 * x_t * z_t / y_t + y_t
self.assertEqual(x.grad.data, x_grad * grad_output)
self.assertEqual(y.grad.data, y_grad * grad_output)
self.assertEqual(z.grad.data, z_grad * grad_output)
def test_sparse_backward(self):
class FixedGradientFunction(Function):
def __init__(self, grad):
self.grad = grad
def forward(self, x):
return x
def backward(self, grad_x):
return self.grad
size = torch.Size([6, 3, 2])
i1 = torch.LongTensor([
[0, 3, 4],
[0, 2, 2],
])
v1 = torch.DoubleTensor([[1, 2], [4, 5], [7, 8]])
sparse_grad1 = torch.sparse.DoubleTensor(i1, v1, size)
i2 = torch.LongTensor([
[0, 1, 3, 4],
[0, 1, 2, 2],
])
v2 = torch.DoubleTensor([[1, 2], [4, 3], [4, 5], [7, 8]])
sparse_grad2 = torch.sparse.DoubleTensor(i2, v2, size)
dense_grad = torch.rand(size).double()
sparse_fn1 = FixedGradientFunction(sparse_grad1)
sparse_fn2 = FixedGradientFunction(sparse_grad2)
dense_fn = FixedGradientFunction(dense_grad)
# sparse first
x = Variable(torch.randn(5, 5), requires_grad=True)
(sparse_fn1(x) + dense_fn(x) + sparse_fn2(x)).sum().backward()
self.assertEqual(x.grad.data, dense_grad + sparse_grad1 + sparse_grad2)
# dense first
x = Variable(torch.randn(5, 5), requires_grad=True)
(dense_fn(x) + sparse_fn1(x) + sparse_fn2(x)).sum().backward()
self.assertEqual(x.grad.data, dense_grad + sparse_grad1 + sparse_grad2)
# sparse only
x = Variable(torch.randn(5, 5), requires_grad=True)
(sparse_fn1(x) + sparse_fn2(x)).sum().backward()
self.assertEqual(x.grad.data, sparse_grad1 + sparse_grad2)
def test_multi_backward(self):
x = Variable(torch.randn(5, 5), requires_grad=True)
y = Variable(torch.randn(5, 5), requires_grad=True)
q = Variable(torch.randn(5, 5), requires_grad=True)
a = Variable(torch.randn(5, 5), requires_grad=True)
b = Variable(torch.randn(5, 5), requires_grad=True)
q2 = q * 2
z = x + y + q2
c = a * b + q2
grad_z = torch.randn(5, 5)
grad_c = torch.randn(5, 5)
torch.autograd.backward([z, c], [grad_z, grad_c])
self.assertEqual(x.grad.data, grad_z)
self.assertEqual(y.grad.data, grad_z)
self.assertEqual(a.grad.data, grad_c * b.data)
self.assertEqual(b.grad.data, grad_c * a.data)
self.assertEqual(q.grad.data, (grad_c + grad_z) * 2)
def test_multi_backward_stochastic(self):
x = Variable(torch.randn(5, 5), requires_grad=True)
y = Variable(torch.randn(5, 5), requires_grad=True)
z = x + y
q = torch.normal(x)
q.reinforce(torch.randn(5, 5))
torch.autograd.backward([z, q], [torch.ones(5, 5), None])
def test_multi_backward_no_grad(self):
x = Variable(torch.randn(5, 5), requires_grad=True)
y = Variable(torch.randn(5, 5), requires_grad=False)
z = x + y
q = y * 2
torch.autograd.backward([z, q], [torch.ones(5, 5), torch.ones(5, 5)])
self.assertEqual(x.grad.data, torch.ones(5, 5))
def test_dependent_backward(self):
x = Variable(torch.randn(10), requires_grad=True)
y = x ** 2
z = y ** 3
go_y = torch.randn(10)
go_z = torch.randn(10)
torch.autograd.backward([y, z], [go_y, go_z])
xd = x.data
self.assertEqual(x.grad.data, 2 * xd * go_y + 6 * xd.pow(5) * go_z)
def test_volatile(self):
x = Variable(torch.ones(5, 5), requires_grad=True)
y = Variable(torch.ones(5, 5) * 4, volatile=True)
z = x ** 2
self.assertFalse(z.volatile)
self.assertTrue(z.requires_grad)
self.assertIsNotNone(z.grad_fn)
z.backward(torch.ones(5, 5))
self.assertEqual(x.grad.data, torch.ones(5, 5) * 2)
w = z + y
self.assertTrue(w.volatile)
self.assertFalse(w.requires_grad)
self.assertRaises(RuntimeError, lambda: w.backward(torch.ones(5, 5)))
self.assertIsNone(w.grad_fn)
def test_indexing(self):
x = torch.arange(1, 17).view(4, 4)
y = Variable(x, requires_grad=True)
def check_index(idx):
if y.grad is not None:
y.grad.data.zero_()
indexed_tensor = x[idx]
indexed_var = y[idx]
indexed_var_t = indexed_var.data
if not torch.is_tensor(indexed_tensor):
indexed_var_t = indexed_var_t[0]
self.assertEqual(indexed_tensor, indexed_var_t)
indexed_var.sum().backward()
expected_grad = torch.zeros(4, 4)
expected_grad[idx] = 1
self.assertEqual(y.grad.data, expected_grad)
check_index(1)
check_index((1, 1))
check_index(slice(1, None))
check_index(slice(None, 2))
check_index((slice(None, 2), 2))
check_index((slice(1, 2), 2))
check_index((1, slice(2, None)))
check_index((slice(None, None), slice(2, None)))
check_index(torch.LongTensor([0, 2]))
check_index(torch.rand(4, 4).bernoulli().byte())
check_index((Ellipsis, slice(2, None)))
check_index(([0], [0]))
check_index(([1, 2, 3], [0]))
check_index(([1, 2], [2, 1]))
check_index(([[1, 2], [3, 0]], [[0, 1], [2, 3]]))
def test_indexing_duplicates(self):
x = torch.arange(1, 17).view(4, 4)
y = Variable(x, requires_grad=True)
idx = torch.LongTensor([1, 1, 3, 2, 1, 2])
y[idx].sum().backward()
expected_grad = torch.zeros(4, 4)
for i in idx:
expected_grad[i] += 1
self.assertEqual(y.grad.data, expected_grad)
# with advanced indexing
x = torch.arange(1, 17).view(4, 4)
y = Variable(x, requires_grad=True)
idx = [[1, 1, 3, 2, 1, 2], [0]]
y[idx].sum().backward()
expected_grad = torch.zeros(4, 4)
for i in idx[0]:
for j in idx[1]:
expected_grad[i][j] += 1
self.assertEqual(y.grad.data, expected_grad)
x = torch.arange(1, 17).view(4, 4)
y = Variable(x, requires_grad=True)
idx = [[[1, 2], [0, 0]], [[0, 1], [1, 1]]]
y[idx].sum().backward()
expected_grad = torch.Tensor([[0, 2, 0, 0],
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 0, 0]])
self.assertEqual(y.grad.data, expected_grad)
def test_basic_op_grad_fallback(self):
"""Grad output might need to be reshaped to match the second argument."""
x = Variable(torch.randn(4, 6), requires_grad=True)
b = Variable(torch.rand(12, 1) + 1e-2, requires_grad=True)
c = Variable(torch.rand(8, 1) + 1e-2, requires_grad=True)
def y():
# .mm() depends on the grad_output being of correct size
return b.mm(Variable(torch.rand(1, 2) + 1e-2))
def z():
return c.mm(Variable(torch.rand(1, 3) + 1e-2))
# suppress broadcastable warning
with warnings.catch_warnings(record=True):
(x + y()).sum().backward()
(x - y()).sum().backward()
(x * y()).sum().backward()
(x / y()).sum().backward()
(x.dist(y())).sum().backward()
(x.lerp(y(), 0.5)).sum().backward()
(x.max(y())).sum().backward()
(x.min(y())).sum().backward()
(x.masked_fill(y() < 0, 0.5)).sum().backward()
(x.masked_scatter(Variable(y().data < 0.25), z())).sum().backward()
(x.masked_select(Variable(y().data < 0.25))).sum().backward()
(x.addcmul(1, y(), z())).sum().backward()
(x.addcdiv(1, y(), z())).sum().backward()
(x.abs() ** y()).sum().backward()
def test_requires_grad(self):
x = Variable(torch.randn(5, 5))
y = Variable(torch.randn(5, 5))
z = Variable(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_inplace(self):
a = Variable(torch.randn(5, 5))
b = Variable(torch.randn(5, 5), requires_grad=True)
a += b
self.assertTrue(a.requires_grad)
# non-leaf Variable
a = Variable(torch.randn(5, 5)) + 0
b = Variable(torch.randn(5, 5), requires_grad=True)
a += b
self.assertTrue(a.requires_grad)
def test_grad_assignment(self):
x = Variable(torch.randn(5, 5))
a = Variable(torch.randn(2, 2)) # size mismatch
b = Variable(torch.randn(5, 5).long()) # type mismatch
with self.assertRaises(RuntimeError):
x.grad = Variable(torch.randn(2, 2))
with self.assertRaises(RuntimeError):
x.grad = Variable(torch.randn(5, 5).long())
with self.assertRaises(RuntimeError):
x.grad = x
if not torch.cuda.is_available():
raise unittest.SkipTest("CUDA not available")
with self.assertRaises(RuntimeError):
x.grad = Variable(torch.randn(5, 5).cuda())
if torch.cuda.device_count() < 2:
raise unittest.SkipTest("At least 2 CUDA devices needed")
x = Variable(torch.randn(5, 5).cuda(0))
with self.assertRaises(RuntimeError):
x.grad = Variable(torch.randn(5, 5).cuda(1))
def test_duplicate_backward_root(self):
a = Variable(torch.randn(5, 5), requires_grad=True)
b = Variable(torch.randn(5, 5), requires_grad=True)
x = a * b
grad_output = x.data.clone().normal_()
torch.autograd.backward([x, x], [grad_output, grad_output])
self.assertEqual(a.grad.data, b.data * grad_output * 2)
self.assertEqual(b.grad.data, a.data * grad_output * 2)
def test_backward_no_grad(self):
a = Variable(torch.randn(5, 5), requires_grad=True)
b = a + 2
with self.assertRaises(RuntimeError):
torch.autograd.backward([b], [None])
def test_next_functions(self):
x = Variable(torch.randn(5, 5), requires_grad=True)
y = Variable(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), 1)
self.assertIs(next_functions[0][0], a.grad_fn)
def test_inplace(self):
x = Variable(torch.ones(5, 5), requires_grad=True)
y = Variable(torch.ones(5, 5) * 4, requires_grad=True)
z = x * y
q = z + y
w = z * y
z.add_(2)
# Add doesn't need it's inputs to do backward, so it shouldn't raise
q.backward(torch.ones(5, 5), retain_graph=True)
# Mul saves both inputs in forward, so it should raise
self.assertRaises(RuntimeError, lambda: w.backward(torch.ones(5, 5)))
z = x * y
q = z * y
r = z + y
w = z.add_(y)
# w is a the last expression, so this should succeed
w.backward(torch.ones(5, 5), retain_graph=True)
# r doesn't use the modified value in backward, so it should succeed
r.backward(torch.ones(5, 5), retain_graph=True)
# q uses dirty z, so it should raise
self.assertRaises(RuntimeError, lambda: q.backward(torch.ones(5, 5)))
x.grad.data.zero_()
m = x / 2
z = m + y / 8
q = z * y
r = z + y
prev_version = z._version
w = z.exp_()
self.assertNotEqual(z._version, prev_version)
r.backward(torch.ones(5, 5), retain_graph=True)
self.assertEqual(x.grad.data, torch.ones(5, 5) / 2)
w.backward(torch.ones(5, 5), retain_graph=True)
self.assertEqual(x.grad.data, torch.Tensor(5, 5).fill_((1 + math.e) / 2))
self.assertRaises(RuntimeError, lambda: q.backward(torch.ones(5, 5)))
leaf = Variable(torch.ones(5, 5), requires_grad=True)
x = leaf.clone()
x.add_(10)
self.assertEqual(x.data, torch.ones(5, 5) * 11)
# x should be still usable
y = x + 2
y.backward(torch.ones(5, 5))
self.assertEqual(leaf.grad.data, torch.ones(5, 5))
z = x * y
x.add_(2)
self.assertRaises(RuntimeError, lambda: z.backward(torch.ones(5, 5)))
def test_mark_non_differentiable(self):
class MyFunction(Function):
@staticmethod
def forward(ctx, input):
output = input > 0
ctx.mark_non_differentiable(output)
return output
@staticmethod
def backward(ctx, grad_output):
return (grad_output * 0).type(torch.DoubleTensor)
x = Variable(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_resize(self):
x = Variable(torch.ones(2, 3))
self.assertTrue(x.resize(3, 2).size() == (3, 2))
def test_shared_storage(self):
x = Variable(torch.ones(5, 5))
y = x.t()
z = x[1]
self.assertRaises(RuntimeError, lambda: x.add_(2))
self.assertRaises(RuntimeError, lambda: y.add_(2))
self.assertRaises(RuntimeError, lambda: z.add_(2))
def _test_setitem(self, size, index):
x = Variable(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)
if isinstance(index, Variable):
index = index.data
expected_grad[index] = 0
self.assertEqual(x.grad.data, expected_grad)
def _test_setitem_tensor(self, size, index):
x = Variable(torch.ones(*size), requires_grad=True)
y = x + 2
y_version = y._version
value = Variable(torch.Tensor(x[index].size()).fill_(7), requires_grad=True)
y[index] = value
self.assertNotEqual(y._version, y_version)
y.backward(torch.ones(*size))
expected_grad_input = torch.ones(*size)
if isinstance(index, Variable):
index = index.data
expected_grad_input[index] = 0
self.assertEqual(x.grad.data, expected_grad_input)
self.assertEqual(value.grad.data, torch.ones(value.size()))
# case when x is not same shape as y[1]
x = Variable(torch.randn(1, 2), requires_grad=True)
y = Variable(torch.zeros(10, 2))
y[1] = x
y.backward(torch.randn(10, 2))
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_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,), [[0, 1, 2, 3]])
def test_setitem_mask(self):
mask = torch.ByteTensor(5, 5).bernoulli_()
self._test_setitem((5, 5), Variable(mask))
self._test_setitem((5,), Variable(mask[0]))
self._test_setitem((1,), Variable(mask[0, 0:1]))
self._test_setitem_tensor((5, 5), Variable(mask))
self._test_setitem_tensor((5,), Variable(mask[0]))
def test_stack(self):
x = Variable(torch.randn(10, 10), requires_grad=True)
y = Variable(torch.randn(10, 10), requires_grad=True)
z = Variable(torch.randn(10, 10), requires_grad=True)
stacked = torch.stack([x, y, z], 0)
grad = torch.randn(3, 10, 10)
stacked.backward(grad)
self.assertEqual(x.grad.data, grad[0])
self.assertEqual(y.grad.data, grad[1])
self.assertEqual(z.grad.data, grad[2])
def test_unused_output(self):
x = Variable(torch.randn(10, 10), requires_grad=True)
outputs = x.chunk(5)
o = outputs[2]
o = o * 4 + 2
o.sum().backward()
expected_grad = torch.zeros(10, 10)
expected_grad[4:6] = 4
self.assertEqual(x.grad.data, expected_grad)
x.grad.data.zero_()
grad_output = torch.randn(2, 10)
outputs = x.chunk(5)
outputs[0].backward(grad_output)
expected_grad = torch.zeros(10, 10)
expected_grad[:2] = grad_output
self.assertEqual(x.grad.data, expected_grad)
def test_gc_in_destructor(self):
"""
Previously, if a Function destructor triggered a garbage collection,
the Variable's tp_dealloc handler would get called twice leading to a
segfault.
"""
class CollectOnDelete(Function):
def __del__(self):
gc.collect()
for i in range(10):
Variable(torch.randn(10, 10), _grad_fn=CollectOnDelete())
@unittest.skipIf(not torch.cuda.is_available() or torch.cuda.device_count() < 2,
"CUDA not available or <2 GPUs detected")
def test_unused_output_gpu(self):
from torch.nn.parallel._functions import Broadcast
x = Variable(torch.randn(5, 5).float().cuda(), requires_grad=True)
outputs = Broadcast(list(range(torch.cuda.device_count())))(x)
y = outputs[-1] * 2
y.sum().backward()
self.assertEqual(x.grad.data, torch.ones(5, 5) * 2)
def test_detach(self):
x = Variable(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 = Variable(torch.randn(10, 10), requires_grad=True)
y = x * 2
y = y.detach()
self.assertFalse(y.requires_grad)
self.assertIsNone(y.grad_fn)
z = x + y
z.sum().backward()
# This is an incorrect gradient, but we assume that's what the user
# wanted. detach() is an advanced option.
self.assertEqual(x.grad.data, torch.ones(10, 10))
# detach() should preserve volatile flag
x = Variable(torch.randn(10, 10), volatile=True)
y = x * 2
y = y.detach()
self.assertTrue(y.volatile)
# in-place detach
x = Variable(torch.randn(10, 10), requires_grad=True)
y = Variable(torch.randn(10, 10), requires_grad=True)
a = x * 2
(y + a).sum().backward(retain_graph=True)
a.detach_()
self.assertFalse(a.requires_grad)
(y + a).sum().backward() # this won't backprop to x
self.assertEqual(x.grad.data, torch.ones(10, 10) * 2)
self.assertEqual(y.grad.data, torch.ones(10, 10) * 2)
def test_type_conversions(self):
x = Variable(torch.randn(5, 5))
self.assertIs(type(x.float().data), torch.FloatTensor)
self.assertIs(type(x.int().data), torch.IntTensor)
if torch.cuda.is_available():
self.assertIs(type(x.float().cuda().data), torch.cuda.FloatTensor)
self.assertIs(type(x.int().cuda().data), torch.cuda.IntTensor)
self.assertIs(type(x.int().cuda().cpu().data), torch.IntTensor)
if torch.cuda.device_count() > 2:
x2 = x.float().cuda(1)
self.assertIs(type(x2.data), torch.cuda.FloatTensor)
self.assertIs(x2.get_device(), 1)
x2 = x.float().cuda()
self.assertIs(type(x2.data), torch.cuda.FloatTensor)
self.assertIs(x2.get_device(), 0)
x2 = x2.cuda(1)
self.assertIs(type(x2.data), torch.cuda.FloatTensor)
self.assertIs(x2.get_device(), 1)
for t in [torch.DoubleTensor, torch.FloatTensor, torch.IntTensor, torch.ByteTensor]:
for var in (True, False):
y = torch.randn(5, 5).type(t)
if var:
y = Variable(y)
self.assertIs(type(x.type_as(y).data), t)
def test_isolated_node(self):
x = Variable(torch.randn(5, 5), requires_grad=True)
y = Variable(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_return_leaf(self):
class Identity(Function):
def forward(self, a, b):
return a, a + b
def backward(self, grad_a, grad_b):
return grad_a + grad_b, grad_b
class Inplace(InplaceFunction):
def forward(self, a, b):
self.mark_dirty(a)
return a.add_(b), b + 2
def backward(self, grad_a, grad_b):
return grad_a, grad_a + grad_b
x = Variable(torch.randn(5, 5), requires_grad=True)
y = Variable(torch.randn(5, 5), requires_grad=True)
q, p = Identity()(x, y)
# Make sure hooks only receive grad from usage of q, not x.
q.register_hook(
lambda grad: self.assertEqual(grad.data, torch.ones(5, 5)))
(q + p + x).sum().backward()
self.assertEqual(x.grad.data, torch.ones(5, 5) * 3)
self.assertEqual(y.grad.data, torch.ones(5, 5))
del q, p # these need to be freed, or next part will raise an error
def test_return_leaf_inplace(self):
class Inplace(InplaceFunction):
def forward(self, a, b):
self.mark_dirty(a)
return a.add_(b), b + 2
def backward(self, grad_a, grad_b):
return grad_a, grad_a + grad_b
x = Variable(torch.randn(5, 5))
y = Variable(torch.randn(5, 5), requires_grad=True)
fn = Inplace(True)
q, p = fn(x, y)
self.assertIs(q, x)
self.assertIs(q.grad_fn, fn)
self.assertTrue(q.requires_grad)
q.sum().backward()
self.assertEqual(y.grad.data, torch.ones(5, 5))
def test_leaf_assignment(self):
x = Variable(torch.randn(5, 5))
y = Variable(torch.randn(5), requires_grad=True)
z = Variable(torch.randn(5), requires_grad=True)
x[0] = y
x[1] = 2 * z
self.assertTrue(x.requires_grad)
self.assertIsNot(x.grad_fn, None)
x.sum().backward()
self.assertEqual(y.grad.data, torch.ones(5))
self.assertEqual(z.grad.data, torch.ones(5) * 2)
def test_volatile_assignment(self):
x = Variable(torch.randn(5, 5))
y = Variable(torch.randn(5), volatile=True)
x[0] = y
self.assertTrue(x.volatile)
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 = Variable(torch.ones(5, 5), requires_grad=True)
y = Variable(torch.ones(5, 5), requires_grad=True)
# Simulate that we're in the middle of the graph
a = x + 2
b = y + 2
c = x + 2
# This op will just return grad_output two times in backward
add1 = a + b
add2 = add1 + c
# Simulate a long branch, so grad_output will get buffered.
for i in range(4):
a = a * 2
b = b * 2
c = c * 2
branch = a + b + c
out = add2 + branch
# expected gradients are:
# for x: 34 (16 from final a, 16 from final c, 2 from add2)
# for y: 17 (16 from final b, 1 from add2)
grad_output = torch.ones(5, 5)
out.backward(grad_output)
self.assertEqual(x.grad.data, torch.ones(5, 5) * 34)
self.assertEqual(y.grad.data, torch.ones(5, 5) * 17)
def test_functional_blas(self):
def compare(fn, *args):
unpacked_args = tuple(arg.data if isinstance(arg, Variable) else arg
for arg in args)
unpacked_result = fn(*unpacked_args)
packed_result = fn(*args).data
# if non-Variable torch function returns a scalar, compare to scalar
if not torch.is_tensor(unpacked_result):
assert packed_result.dim() == 1
assert packed_result.nelement() == 1
packed_result = packed_result[0]
self.assertEqual(packed_result, unpacked_result)
def test_blas_add(fn, x, y, z):
# Checks all signatures
compare(fn, x, y, z)
compare(fn, 0.5, x, y, z)
compare(fn, 0.5, x, 0.25, y, z)
def test_blas(fn, x, y):
compare(fn, x, y)
test_blas(torch.mm, Variable(torch.randn(2, 10)),
Variable(torch.randn(10, 4)))
test_blas_add(torch.addmm, Variable(torch.randn(2, 4)),
Variable(torch.randn(2, 10)), Variable(torch.randn(10, 4)))
test_blas(torch.bmm, Variable(torch.randn(4, 2, 10)),
Variable(torch.randn(4, 10, 4)))
test_blas_add(torch.addbmm, Variable(torch.randn(2, 4)),
Variable(torch.randn(4, 2, 10)), Variable(torch.randn(4, 10, 4)))
test_blas_add(torch.baddbmm, Variable(torch.randn(4, 2, 4)),
Variable(torch.randn(4, 2, 10)), Variable(torch.randn(4, 10, 4)))
test_blas(torch.mv, Variable(torch.randn(2, 10)),
Variable(torch.randn(10)))
test_blas_add(torch.addmv, Variable(torch.randn(2)),
Variable(torch.randn(2, 10)), Variable(torch.randn(10)))
test_blas(torch.ger, Variable(torch.randn(5)),
Variable(torch.randn(6)))
test_blas_add(torch.addr, Variable(torch.randn(5, 6)),
Variable(torch.randn(5)), Variable(torch.randn(6)))
test_blas(torch.matmul, Variable(torch.randn(6)), Variable(torch.randn(6)))
test_blas(torch.matmul, Variable(torch.randn(10, 4)), Variable(torch.randn(4)))
test_blas(torch.matmul, Variable(torch.randn(5)), Variable(torch.randn(5, 6)))
test_blas(torch.matmul, Variable(torch.randn(2, 10)), Variable(torch.randn(10, 4)))
test_blas(torch.matmul, Variable(torch.randn(5, 2, 10)), Variable(torch.randn(5, 10, 4)))
test_blas(torch.matmul, Variable(torch.randn(3, 5, 2, 10)), Variable(torch.randn(3, 5, 10, 4)))
test_blas(torch.matmul, Variable(torch.randn(3, 5, 2, 10)), Variable(torch.randn(10)))
test_blas(torch.matmul, Variable(torch.randn(10)), Variable(torch.randn(3, 5, 10, 4)))
def test_save_none_for_backward(self):
test_case = self
class MyFn(Function):
def forward(self, input):
self.save_for_backward(None, input, None)
return input * input
def backward(self, grad_output):
n1, input, n2 = self.saved_tensors
test_case.assertIsNone(n1)
test_case.assertIsNone(n2)
return 2 * input * grad_output
x = Variable(torch.randn(5, 5), requires_grad=True)
y = MyFn()(x)
y.sum().backward()
self.assertEqual(x.grad.data, 2 * x.data)
def test_too_many_grads(self):
class MyFn(Function):
def forward(self, input):
return input
def backward(self, grad_output):
return grad_output, None, None
x = Variable(torch.randn(5, 5), requires_grad=True)
y = MyFn()(x)
y.sum().backward()
self.assertEqual(x.grad.data, x.data.clone().fill_(1))
def test_reinforce_check(self):
x = Variable(torch.randn(5, 5), requires_grad=True)
# these should be ok
y = torch.normal(x)
y.reinforce(torch.randn(5, 5))
y = torch.normal(x)
y.reinforce(2)
# can't call reinforce on non-stochastic variables
self.assertRaises(RuntimeError, lambda: x.reinforce(2))
# can't call reinforce twice
y = torch.normal(x)
y.reinforce(2)
self.assertRaises(RuntimeError, lambda: y.reinforce(2))
# check type of reward
y = torch.normal(x)
self.assertRaises(TypeError, lambda: y.reinforce(torch.randn(5, 5).long()))
# check size of reward
y = torch.normal(x)
self.assertRaises(ValueError, lambda: y.reinforce(torch.randn(4, 5)))
def test_stochastic(self):
x = Variable(torch.rand(2, 10), requires_grad=True)
stddevs = Variable(torch.rand(2, 10) * 5, requires_grad=True)
y = (x * 2).clamp(0, 1)
y = y / y.sum(1, True).expand_as(y)
samples_multi = y.multinomial(5)
samples_multi_flat = y[0].multinomial(5)
samples_bernoulli = y.bernoulli()
samples_norm = torch.normal(y)
samples_norm_std = torch.normal(y, stddevs)
z = samples_multi * 2 + 4
z = z + samples_multi_flat.unsqueeze(0).expand_as(samples_multi)
z = torch.cat([z, z], 1)
z = z.double()
z = z + samples_bernoulli + samples_norm + samples_norm_std
last_sample = torch.normal(z, 4)
z = last_sample + 2
self.assertFalse(z.requires_grad)
self.assertRaises(RuntimeError, lambda: z.backward(retain_graph=True))
samples_multi.reinforce(torch.randn(2, 5))
self.assertRaises(RuntimeError, lambda: z.backward(retain_graph=True))
samples_multi_flat.reinforce(torch.randn(5))
self.assertRaises(RuntimeError, lambda: z.backward(retain_graph=True))
samples_bernoulli.reinforce(torch.randn(2, 10))
self.assertRaises(RuntimeError, lambda: z.backward(retain_graph=True))
samples_norm.reinforce(torch.randn(2, 10))
self.assertRaises(RuntimeError, lambda: z.backward(retain_graph=True))
samples_norm_std.reinforce(torch.randn(2, 10))
# We don't have to specify rewards w.r.t. last_sample - it doesn't
# require gradient
last_sample.backward(retain_graph=True)
z.backward()
self.assertGreater(x.grad.data.abs().sum(), 0)
def test_stochastic_require_grad(self):
# This tests a DSD function sequence (D=deterministic, S=stochastic),
# where all functions require grad.
x = Variable(torch.randn(2, 10), requires_grad=True)
y = Variable(torch.randn(2, 10), requires_grad=True)
z = torch.normal(x + 2, 2)
o = z + y
z.reinforce(torch.randn(2, 10))
o.sum().backward()
self.assertEqual(y.grad.data, torch.ones(2, 10))
self.assertGreater(x.grad.data.abs().sum(), 0)
def test_stochastic_sequence(self):
x = Variable(torch.rand(10).clamp_(0, 1), requires_grad=True)
b = x.bernoulli()
n1 = torch.normal(b, x)
n2 = torch.normal(n1, 2)
b.reinforce(torch.randn(10))
n1.reinforce(torch.randn(10))
n2.reinforce(torch.randn(10))
n2.backward()
self.assertGreater(x.grad.data.abs().sum(), 0)
def test_stochastic_output(self):
x = Variable(torch.rand(10), requires_grad=True)
b = x.clone().clamp(0, 1).bernoulli()
b.reinforce(torch.randn(10))
b.backward()
self.assertGreater(x.grad.data.abs().sum(), 0)
def test_pickle(self):
x = Variable(torch.randn(10, 10), requires_grad=True)
y = Variable(torch.randn(10, 10), volatile=True)
z = Variable(torch.randn(10, 10), requires_grad=False)
def assert_strict_equal(var1, var2):
self.assertEqual(var1.data, var2.data)
self.assertEqual(var1.requires_grad, var2.requires_grad)
self.assertEqual(var1.volatile, var2.volatile)
serialized = [pickle.dumps([x, y, z], protocol=p) for p in range(3)]
for dump in serialized:
xc, yc, zc = pickle.loads(dump)
assert_strict_equal(xc, x)
assert_strict_equal(yc, y)
assert_strict_equal(zc, z)
def test_dep_nograd(self):
class F1(Function):
def forward(self, input):
out = torch.randn(input.size())
self.mark_non_differentiable(out)
return input, out
def backward(self, grad_output, ignored):
return grad_output
class F2(Function):
def forward(self, input, ignored):
return input
def backward(self, grad_output):
return grad_output, None
x = Variable(torch.randn(5), requires_grad=True)
a, b = F1()(x)
b = b + 1 # separate F1 from F2 by another op
self.assertTrue(a.requires_grad)
self.assertFalse(b.requires_grad)
c = F2()(a, b)
c.backward(torch.ones(c.size()))
self.assertEqual(x.grad.data, torch.ones(x.size()))
def index_variable(shape, max_indices):
if not isinstance(shape, tuple):
shape = (shape,)
index = torch.rand(*shape).mul_(max_indices).floor_().long()
return Variable(index, requires_grad=False)
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 Variable(index, requires_grad=False)
def gather_variable(shape, index_dim, max_indices, duplicate=False):
assert len(shape) == 2
assert index_dim < 2
batch_dim = 1 - index_dim
index = torch.LongTensor(*shape)
for i in range(shape[index_dim]):
index.select(index_dim, i).copy_(
torch.randperm(max_indices)[:shape[batch_dim]])
if duplicate:
index.select(batch_dim, 0).copy_(index.select(batch_dim, 1))
return Variable(index, requires_grad=False)
def mask_not_all_zeros(shape):
assert len(shape) > 0
while True:
result = torch.randn(shape).gt(0)
if result.sum() > 0:
return result
def prod_zeros(dim_size, dim_select):
assert len(dim_select) == 2
result = torch.randn(dim_size, dim_size, dim_size)
result.narrow(dim_select[0], 0, 1).narrow(dim_select[1], 1, 1).zero_()
result.narrow(dim_select[0], 2, 1).narrow(dim_select[1], 3, 1).zero_()
result.narrow(dim_select[0], 4, 1).narrow(dim_select[1], 3, 1).zero_()
return Variable(result, requires_grad=True)
def prod_single_zero(dim_size):
result = torch.randn(dim_size, dim_size)
result[0, 1] = 0
return Variable(result, requires_grad=True)
class dont_convert(tuple):
pass
L = 20
M = 10
S = 5
function_tests = [
(Add, (), ((M, M), (M, M))),
(Add, (), ((M, M), (M, )), 'broadcast_rhs'),
(Add, (), ((M, ), (M, M)), 'broadcast_lhs'),
(Add, (), ((M, 1, M), (S, M)), 'broadcast_all'),
(Sub, (), ((M, M), (M, M))),
(Sub, (), ((M, M), (M, )), 'broadcast_rhs'),
(Sub, (), ((M, ), (M, M)), 'broadcast_lhs'),
(Sub, (), ((M, 1, M), (S, M)), 'broadcast_all'),
(Mul, (), ((M, M), (M, M))),
(Mul, (), ((M, M), (M, )), 'broadcast_rhs'),
(Mul, (), ((M, ), (M, M)), 'broadcast_lhs'),
(Mul, (), ((M, 1, M), (S, M)), 'broadcast_all'),
(Div, (), ((M, M), torch.rand(M, M) + 5e-2)),
(Div, (), ((M, M), torch.rand(M, ) + 5e-2), 'broadcast_rhs'),
(Div, (), ((M, ), torch.rand(M, M) + 5e-2), 'broadcast_lhs'),
(Div, (), ((M, 1, M), torch.rand(S, M) + 5e-2), 'broadcast_all'),
(Pow, (), (torch.rand(M, M) + 1e-3, torch.rand(M, M) + 0.1)),
(Pow, (), (torch.rand(M, M) + 1e-3, torch.rand(M,) + 0.1), 'broadcast_rhs'),
(Pow, (), (torch.rand(M, ) + 1e-3, torch.rand(M, M) + 0.1), 'broadcast_lhs'),
(Pow, (), (torch.rand(M, 1) + 1e-3, torch.rand(1, M) + 0.1), 'broadcast_all'),
(AddConstant, (), ((2, 2), 3.14)),
(AddConstant, (), (3.14, (2, 2)), 'add_tensor'),
(SubConstant, (), ((L, L), 3.14)),
(SubConstant, (), (3.14, (L, L),), 'sub_tensor'),
(MulConstant, (), ((L, L), 3.14)),
(MulConstant, (), (3.14, (L, L)), 'mul_tensor'),
(DivConstant, (), (torch.rand(L, L) + 1e-1, 3.14)),
(DivConstant, (), (3.14, torch.rand(L, L) + 0.5,), 'div_tensor'),
(PowConstant, (), (torch.rand(L, L), 3)),
(PowConstant, (), (3.14, torch.rand(L, L)), 'tensor_power'),
# TODO: enable neg dim checks
(Transpose, (), (torch.rand(L, L), 0, 1)),
(Transpose, (), (torch.rand(S, S, S), 2, 0), '3d'),
(Permute, (), ((1, 2, 3, 4, 5, 6), torch.Size([0, 4, 3, 5, 1, 2]))),
(Index, (), (torch.rand(S, S, S), dont_convert([1, 2]))),
(Index, (), (torch.rand(S, S, S), slice(0, 3)), 'slice'),
(Index, (), (torch.rand(S, S, S), dont_convert([slice(0, 3), 1])), 'slice_index'),
(Index, (), (torch.rand(S, S, S), dont_convert([[0, 2, 3], [1, 3, 3], [0, 0, 2]])), 'adv_index'),
(Index, (), (torch.rand(S, S, S), dont_convert([[0, 0, 3], [1, 1, 3], [0, 0, 2]])), 'adv_index_dup'),
(View, (), (torch.rand(S, S, S), torch.Size([S * S, S]))),
(Expand, (), ((1, S, 1, S, 1), torch.Size([5, S, 5, S, 5]))),
(Expand, (), ((S, 1), torch.Size([S, S, S])), 'new_dim'),
(Expand, (), ((1, S), torch.Size([S, S, S])), 'new_dim_front'),
(Expand, (), ((1, S), torch.Size([1, 1, S])), 'new_dim_front_old_front_1'),
(Expand, (), ((1,), torch.Size([S, S, S])), 'scalar'),
(Exp, (), (torch.rand(S, S, S),)),
(Log, (), (torch.rand(S, S, S) + 1e-2,)),
(Log1p, (), (torch.rand(S, S, S),)),
(Tanh, (), ((S, S, S),)),
(Sigmoid, (), ((S, S, S),)),
(Sinh, (), ((S, S, S),)),
(Cosh, (), ((S, S, S),)),
(Abs, (), ((S, S, S),)),
(Clamp, (), ((S, S, S), 0, 1)),
(Sqrt, (), (torch.rand(S, S, S) + 5e-4,)),
(Sin, (), ((S, S, S),)),
(Cos, (), ((S, S, S),)),
(Tan, (), (torch.randn(S, S, S).clamp(-1, 1),)),
(Asin, (), (torch.randn(S, S, S).clamp(-0.9, 0.9),)),
(Acos, (), (torch.randn(S, S, S).clamp(-0.9, 0.9),)),
(Atan, (), ((S, S, S),)),
(Reciprocal, (), (torch.rand(S, S, S) + 0.1,)),
(Cmax, (), ((S, S, S), (S, S, S))),
(Cmax, (), ((S, S, S), (S,)), 'broadcast_rhs'),
(Cmax, (), ((S,), (S, S, S)), 'broadcast_lhs'),
(Cmax, (), ((S, 1, S), (S, S)), 'broadcast_all'),
(Cmin, (), ((S, S, S), (S, S, S))),
(Cmin, (), ((S, S, S), (S,)), 'broadcast_rhs'),
(Cmin, (), ((S,), (S, S, S)), 'broadcast_lhs'),
(Cmin, (), ((S, 1, S), (S, S)), 'broadcast_all'),
(Round, (), ((S, S, S),)),
(Sign, (), ((S, S, S),)),
(Trunc, (), ((S, S, S),)),
(Floor, (), ((S, S, S),)),
(Ceil, (), ((S, S, S),)),
(Frac, (), ((S, S, S),)),
(Fmod, (), ((S, S, S), 1.5)),
(Fmod, (), ((S, S, S), Variable(torch.rand(S, S, S) + 5e-2, requires_grad=False)), 'tensor'),
(Fmod, (), ((S, S, S), Variable(torch.rand(S) + 5e-2, requires_grad=False)), 'tensor_broadcast_rhs'),
(Fmod, (), ((S,), Variable(torch.rand(S, S, S) + 5e-2, requires_grad=False)), 'tensor_broadcast_lhs'),
(Fmod, (), ((S, 1, S), Variable(torch.rand(S, S) + 5e-2, requires_grad=False)), 'tensor_broadcast_all'),
(Lerp, (), ((S, S, S), (S, S, S), 0.2)),
(Lerp, (), ((S, S, S), (S,), 0.2), 'broadcast_rhs'),
(Lerp, (), ((S,), (S, S, S), 0.2), 'broadcast_lhs'),
(Lerp, (), ((S, 1, S), (S, S), 0.2), 'broadcast_all'),
(Rsqrt, (), (torch.rand(S, S, S) + 1e-2,)),
(Remainder, (), ((S, S, S), 1.5)),
(Remainder, (), ((S, S, S), Variable(torch.rand(S, S, S) + 5e-2, requires_grad=False)), 'tensor'),
(Remainder, (), ((S, S, S), Variable(torch.rand(S) + 5e-2, requires_grad=False)), 'tensor_broadcast_rhs'),
(Remainder, (), ((S,), Variable(torch.rand(S, S, S) + 5e-2, requires_grad=False)), 'tensor_broadcast_lhs'),
(Remainder, (), ((S, 1, S), Variable(torch.rand(S, S) + 5e-2, requires_grad=False)), 'tensor_broadcast_all'),
(CmaxConstant, (), ((S, S, S), 0.5)),
(CminConstant, (), ((S, S, S), 0.5)),
(Mean, (), ((S, S, S),)),
(Mean, (), ((S, S, S), 1), 'dim', [1]),
(Mean, (), ((S, S, S), 1, True), 'keepdim_dim', [1]),
(Mean, (), ((S,), 0), 'dim_1d', [1]),
(Mean, (), ((S,), 0, True), 'keepdim_1d', [1]),
(Sum, (), ((S, S, S),)),
(Sum, (), ((S, S, S), 1), 'dim', [1]),
(Sum, (), ((S, S, S), 1, True), 'keepdim_dim', [1]),
(Sum, (), ((S,), 0), 'dim_1d', [1]),
(Sum, (), ((S,), 0, True), 'keepdim_1d', [1]),
(Prod, (), ((S, S, S),)),
(Prod, (), (prod_zeros(S, [0, 1]),), 'zerosdim2'),
(Prod, (), (prod_zeros(S, [0, 2]),), 'zerosdim1'),
(Prod, (), (prod_zeros(S, [1, 2]),), 'zerosdim0'),
(Prod, (), (prod_single_zero(S),), 'single_zero'),
(Prod, (), ((S, S, S), 1), 'dim', [1]),
(Prod, (), (prod_zeros(S, [0, 1]), 1), 'zeros_dim2', [1]),
(Prod, (), (prod_zeros(S, [0, 2]), 1), 'zeros_dim1', [1]),
(Prod, (), (prod_zeros(S, [1, 2]), 1), 'zeros_dim0', [1]),
(Prod, (), ((S, S, S), 1, True), 'keepdim_dim', [1]),
(Prod, (), (prod_zeros(S, [0, 1]), 1, True), 'keepdim_zeros_dim2', [1]),
(Prod, (), (prod_zeros(S, [0, 2]), 1, True), 'keepdim_zeros_dim1', [1]),
(Prod, (), (prod_zeros(S, [1, 2]), 1, True), 'keepdim_zeros_dim0', [1]),
(Prod, (), ((S,), 0), 'dim_1d', [1]),
(Prod, (), ((S,), 0, True), 'keepdim_1d', [1]),
(Addmm, (), ((S, M), (S, S), (S, M)),),
(Addmm, (), ((1,), (S, S), (S, M)), 'broadcast_lhs'),
(Addmm, (), ((S, M), (S, S), (S, M), 0.1, 1), 'coef'),
(Addmm, (), ((1,), (S, S), (S, M), 0.1, 1), 'broadcast_lhs_coef'),
(Addbmm, (), ((S, M), (S, S, S), (S, S, M)),),
(Addbmm, (), ((1,), (S, S, S), (S, S, M)), 'broadcast_lhs'),
(Addbmm, (), ((S, M), (S, S, S), (S, S, M), 0.1, 0.4), 'coef'),
(Addbmm, (), ((1,), (S, S, S), (S, S, M), 0.1, 0.4), 'broadcast_lhs_coef'),
(Baddbmm, (), ((S, S, M), (S, S, S), (S, S, M)),),
(Baddbmm, (), ((1,), (S, S, S), (S, S, M)), 'broadcast_lhs'),
(Baddbmm, (), ((S, S, M), (S, S, S), (S, S, M), 0.1, 0.4), 'coef'),
(Baddbmm, (), ((1,), (S, S, S), (S, S, M), 0.1, 0.4), 'broadcast_lhs_coef'),
(Addmv, (), ((S,), (S, M), (M,)),),
(Addmv, (), ((1,), (S, M), (M,)), 'broadcast_lhs'),
(Addmv, (), ((S,), (S, M), (M,), 0.1, 0.4), 'coef'),
(Addmv, (), ((1,), (S, M), (M,), 0.1, 0.4), 'broadcast_lhs_coef'),
(Addr, (), ((S, M), (S,), (M,)),),
(Addr, (), ((1,), (S,), (M,)), 'broadcast_lhs'),
(Addr, (), ((S, M), (S,), (M,), 0.1, 0.4), 'coef'),
(Addr, (), ((1,), (S,), (M,), 0.1, 0.4), 'broadcast_lhs_coef'),
(Dot, (), ((L,), (L,)),),
(Max, (), ((S, S, S),),),
(Repeat, (), ((S, S, S, S), torch.Size([2, 3, 1, 2]))),
(Cumsum, (), ((S, S, S), 0), 'dim0', [1]),
(Cumsum, (), ((S, S, S), 1), 'dim1', [1]),
(Cumsum, (), ((S,), 0), '1d', [1]),
(Cumprod, (), ((S, S, S), 0),),
(Cumprod, (), ((S, S, S), 1), 'dim1'),
(Cumprod, (), ((S,), 0), '1d'),
(Cumprod, (), (prod_zeros(S, [0, 1]), 1), 'zeros_dim2', [1]),
(Cumprod, (), (prod_zeros(S, [0, 2]), 1), 'zeros_dim1', [1]),
(Cumprod, (), (prod_zeros(S, [1, 2]), 1), 'zeros_dim0', [1]),
(Unfold, (), ((S, S, S), 1, 3, 1)),
(Unfold, (), ((S, S, S), 2, 3, 2), 'lastdim'),
(Min, (), ((S, S, S),),),
(Max, (), ((S, S, S), 1), 'dim', [1]),
(Min, (), ((S, S, S), 1), 'dim', [1]),
(Min, (), ((S,), 0), 'dim_1d', [1]),
(Max, (), ((S,), 0), 'dim_1d', [1]),
(Max, (), ((S, S, S), 1, True), 'keepdim_dim', [1]),
(Min, (), ((S, S, S), 1, True), 'keepdim_dim', [1]),
(Max, (), ((S,), 0, True), 'keepdim_dim_1d', [1]),
(Min, (), ((S,), 0, True), 'keepdim_dim_1d', [1]),
(Mode, (), ((S, S, S),),),
(Mode, (), ((S, S, S), 1), 'dim', [1]),
(Mode, (), ((S, S, S), 1, True), 'keepdim_dim', [1]),
(Mode, (), ((S,), 0), 'dim_1d', [1]),
(Mode, (), ((S,), 0, True), 'keepdim_dim_1d', [1]),
(Kthvalue, (), ((S, S, S), 2),),
(Kthvalue, (), ((S, S, S), 2, 0), 'dim0'),
(Kthvalue, (), ((S, S, S), 2, 0, True), "keepdim"),
(Kthvalue, (), ((S,), 2, 0), 'dim0_1d'),
(Kthvalue, (), ((S,), 2, 0, True), "keepdim_1d"),
(Median, (), ((S, S, S),),),
(Median, (), ((S, S, S), 0), 'dim0'),
(Median, (), ((S, S, S), 0, True), "keepdim"),
(Median, (), ((S,), 0), 'dim0_1d'),
(Median, (), ((S,), 0, True), "keepdim_1d"),
(Norm, (), (torch.rand(S, S, S), 1.5), '1_5'),
(Norm, (), ((S, S, S),), '2'),
(Norm, (), ((S, S, S), 3), '3'),
(Norm, (), (torch.rand(S, S, S), 1.5, 1), '1_5_dim', [2]),
(Norm, (), ((S, S, S), 2, 1), '2_dim', [2]),
(Norm, (), ((S, S, S), 3, 1), '3_dim', [2]),
(Norm, (), (torch.rand(S, S, S), 1.5, 1, True), 'keepdim_1_5_dim', [2]),
(Norm, (), ((S, S, S), 2, 1, True), 'keepdim_2_dim', [2]),
(Norm, (), ((S, S, S), 3, 1, True), 'keepdim_3_dim', [2]),
(Norm, (), ((S,), 2, 0), '2_dim_1d', [2]),
(Norm, (), ((S,), 3, 0), '3_dim_1d', [2]),
(Norm, (), ((S,), 2, 0, True), 'keepdim_2_dim_1d', [2]),
(Norm, (), ((S,), 3, 0, True), 'keepdim_3_dim_1d', [2]),
(Addcmul, (), ((S, S), (S, S), (S, S))),
(Addcmul, (), ((S, S), (S, 1), (1, S)), 'broadcast_rhs'),
(Addcmul, (), ((1,), (S, S, 1), (1, S)), 'broadcast_all'),
(Addcmul, (), ((S, S), (S, S), (S, S), 0.6), 'scale'),
(Addcmul, (), ((S, S), (S, 1), (1, S), 0.6), 'broadcast_rhs_scale'),
(Addcmul, (), ((1,), (S, S, 1), (1, S), 0.6), 'broadcast_all_scale'),
(Addcdiv, (), ((S, S), (S, S), torch.rand(S, S) + 5e-2)),
(Addcdiv, (), ((S, S), (S, 1), torch.rand(1, S) + 5e-2), 'broadcast_rhs'),
(Addcdiv, (), ((1,), (S, S, 1), torch.rand(1, S) + 5e-2), 'broadcast_all'),
(Addcdiv, (), ((S, S), (S, S), torch.rand(S, S) + 5e-2, 0.6), 'scale'),
(Addcdiv, (), ((S, S), (S, 1), torch.rand(1, S) + 5e-2, 0.6), 'broadcast_rhs_scale'),
(Addcdiv, (), ((1,), (S, S, 1), torch.rand(1, S) + 5e-2, 0.6), 'broadcast_all_scale'),
(IndexAdd, (), ((S, S), 0, index_variable(2, S), (2, S))),
(IndexCopy, (), ((S, S), 0, index_perm_variable(2, S), (2, S))),
(IndexFill, (), ((S, S), 0, index_variable(2, S), 2)),
(IndexSelect, (), ((S, S), 0, index_variable(2, S))),
(Gather, (), ((M, S), 0, gather_variable((S, S), 1, M, True))),
# TODO: enable neg dim checks
(Gather, (), ((M, S), 1, gather_variable((M, S // 2), 0, S, True)), 'dim1'),
(Scatter, (), ((M, S), 0, gather_variable((S, S), 1, M), (S, S))),
(Scatter, (), ((M, S), 1, gather_variable((M, S // 2), 0, S), (M, S // 2)), 'dim1'),
(ScatterAdd, (), ((M, S), 0, gather_variable((S, S), 1, M), (S, S))),
(ScatterAdd, (), ((M, S), 1, gather_variable((M, S // 2), 0, S), (M, S // 2)), 'dim1'),
(Concat, (), (0, (1, S, S), (2, S, S), (3, S, S))),
(Concat, (), (-1, (S, S, 1), (S, S, 2), (S, S, 3)), 'negdim-1'),
(Concat, (), (-2, (S, 1, S), (S, 2, S), (S, 3, S)), 'negdim-2'),
(Resize, (), ((S, S, S), torch.Size([S * S, S]))),
(Diag, (), ((S, S),), '2d'),
(Diag, (), ((S,),), '1d'),
(Diag, (), ((S, S), 1), '2d_1'),
(Diag, (), ((S, S), 2), '2d_2'),
(Tril, (), ((S, S),)),
(Tril, (), ((S, S), 2), 'idx'),
(Triu, (), ((S, S),)),
(Triu, (), ((S, S), 2), 'idx'),
(Trace, (), ((S, S),)),
(Cross, (), ((S, 3), (S, 3))),
(Cross, (), ((S, 3, S), (S, 3, S), 1), 'dim'),
(Inverse, (), ((S, S),), '', (), [skipIfNoLapack]),
(Gesv, (), ((S, S), (S, S)), '', (), [skipIfNoLapack]),
(Clone, (), ((S, M, S),)),
(Squeeze, (), ((S, 1, M, 1), None)),
# TODO: enable neg dim checks
(Squeeze, (), ((S, 1, M, 1), 1), 'dim'),
(Unsqueeze, (), ((S, M, S), 0), '0'),
(Unsqueeze, (), ((S, M, S), 1), '1'),
# no lhs or all broadcast on MaskedScatter because it's always inplace
(MaskedScatter, (), ((S, S), Variable(torch.randn(S, S).gt(0), requires_grad=False), (S, S),)),
(MaskedScatter, (), ((S, S), Variable(torch.ones(S,).gt(0), requires_grad=False), (S, S),), 'broadcast_rhs'),
(MaskedFill, (), ((S, S), Variable(torch.randn(S, S).gt(0), requires_grad=False), 10)),
# no lhs or all broadcast on MaskedFill because it's always inplace
(MaskedFill, (), ((S, S), Variable(torch.randn(S,).gt(0), requires_grad=False), 10), 'broadcast_rhs'),
# ensure the mask isn't all zeros or else we get a tensor with 0 dimensions
(MaskedSelect, (), ((S, S), Variable(mask_not_all_zeros((S, S)), requires_grad=False))),
(MaskedSelect, (), ((S, S), Variable(mask_not_all_zeros((S,)), requires_grad=False)), 'broadcast_rhs'),
(MaskedSelect, (), ((S,), Variable(mask_not_all_zeros((S, S,)), requires_grad=False)), 'broadcast_lhs'),
(MaskedSelect, (), ((S, 1, S), Variable(mask_not_all_zeros((S, S)), requires_grad=False)), 'broadcast_all'),
(Sort, (), ((S, M, S),)),
(Sort, (), ((S, M, S), 1), 'dim'),
(Sort, (), ((S, M, S), 1, True), 'dim_desc'),
(Topk, (), ((S, M, S), 3)),
(Topk, (), ((S, M, S), 3, 1), 'dim'),
(Topk, (), ((S, M, S), 3, 1, True), 'dim_desc'),
(Topk, (), ((S, M, S), 3, 1, True, True), 'dim_desc_sort'),
]
# (name, size, args...)
method_tests = [
('add', (S, S, S), ((S, S, S),)),
('add', (S, S, S), ((S, S),), 'broadcast_rhs'),
('add', (S, S), ((S, S, S),), 'broadcast_lhs'),
('add', (S, 1, S), ((M, S),), 'broadcast_all'),
('add', (S, S, S), (3.14,), 'constant'),
('sub', (S, S, S), ((S, S, S),)),
('sub', (S, S, S), ((S, S),), 'broadcast_rhs'),
('sub', (S, S), ((S, S, S),), 'broadcast_lhs'),
('sub', (S, 1, S), ((M, S),), 'broadcast_all'),
('sub', (S, S, S), (3.14,), 'constant'),
('mul', (S, S, S), ((S, S, S),)),
('mul', (S, S, S), ((S, S),), 'broadcast_rhs'),
('mul', (S, S), ((S, S, S),), 'broadcast_lhs'),
('mul', (S, 1, S), ((M, S),), 'broadcast_all'),
('mul', (S, S, S), (3.14,), 'constant'),
('div', (S, S, S), ((S, S, S),)),
('div', (S, S, S), ((S, S),), 'broadcast_rhs'),
('div', (S, S), ((S, S, S),), 'broadcast_lhs'),
('div', (S, 1, S), ((M, S),), 'broadcast_all'),
('div', (S, S, S), (3.14,), 'constant'),
('pow', (S, S, S), ((S, S, S),)),
('pow', (S, S, S), ((1,),), 'broadcast_rhs'),
('pow', (1,), ((S, S, S),), 'broadcast_lhs'),
('pow', (S, 1, S), ((1, S, 1),), 'broadcast_all'),
('pow', (S, S, S), (3.14,), 'constant'),
('transpose', (1, 2, 3), (1, 2), 'dim', [0, 1]),
('t', (1, 2), ()),
('view', (S, S, S), (S * S, S),),
('view_as', (S, S, S), ((S * S, S),)),
('expand', (S, 1, 1), (S, S, S)),
('expand', (torch.Size([S, 1, S]),), (S, S, S), 'size'),
('expand', (S, 1), (S, S, S), 'new_dim'),
('expand', (1,), (S, S, S), 'scalar'),
('expand', (1, S), (1, 1, S), 'new_dim_front_old_front_1'),
('exp', (S, S, S), ()),
('log', (S, S, S), ()),
('log1p', (S, S, S), ()),
('tanh', (S, S, S), ()),
('sigmoid', (S, S, S), ()),
('sinh', (S, S, S), ()),
('cosh', (S, S, S), ()),
('abs', (S, S, S), ()),
('clamp', (S, S, S), (0, 1)),
('sqrt', (S, S, S), ()),
('sin', (S, S, S), ()),
('cos', (S, S, S), ()),
('tan', (S, S, S), ()),
('asin', (S, S, S), ()),
('acos', (S, S, S), ()),
('atan', (S, S, S), ()),
('reciprocal', (S, S, S), ()),
('round', (S, S, S), ()),
('sign', (S, S, S), ()),
('trunc', (S, S, S), ()),
('floor', (S, S, S), ()),
('ceil', (S, S, S), ()),
('rsqrt', (S, S, S), ()),
('fmod', (S, S, S), (1.5,)),
('fmod', (S, S, S), (Variable(torch.rand(S, S, S) + 5e-2, requires_grad=False),), 'tensor'),
('fmod', (S,), (Variable(torch.rand(S, S, S) + 5e-2, requires_grad=False),), 'tensor_broadcast_lhs'),
('fmod', (S, 1, S), (Variable(torch.rand(S, S) + 5e-2, requires_grad=False),), 'tensor_broacast_all'),
('remainder', (S, S, S), (1.5,)),
('remainder', (S, S, S), (Variable(torch.rand(S, S, S) + 5e-2, requires_grad=False),), 'tensor'),
('remainder', (S,), (Variable(torch.rand(S, S, S) + 5e-2, requires_grad=False),), 'tensor_broadcast_lhs'),
('remainder', (S, 1, S), (Variable(torch.rand(S, S) + 5e-2, requires_grad=False),), 'tensor_broacast_all'),
('lerp', (S, S, S), ((S, S, S), 0.4)),
('lerp', (S, S, S), ((S,), 0.4), 'broadcast_rhs'),
('lerp', (S,), ((S, S, S), 0.4), 'broadcast_lhs'),
('lerp', (S, 1, S), ((S, S), 0.4), 'broadcast_all'),
('max', (S, S, S), ()),
('max', (S, S, S), (1,), 'dim', [0]),
('max', (S, S, S), (1, True,), 'keepdim_dim', [0]),
('max', (S,), (0,), 'dim_1d', [0]),
('max', (S,), (0, True,), 'keepdim_dim_1d', [0]),
('max', (S, S, S), ((S, S, S),), 'elementwise'),
('max', (S, S, S), ((S,),), 'elementwise_broadcast_rhs'),
('max', (S,), ((S, S, S),), 'elementwise_broadcast_lhs'),
('max', (S, 1, S), ((S, S),), 'elementwise_broadcast_all'),
('min', (S, S, S), ()),
('min', (S, S, S), (1,), 'dim', [0]),
('min', (S, S, S), (1, True,), 'keepdim_dim', [0]),
('min', (S,), (0,), 'dim_1d', [0]),
('min', (S,), (0, True,), 'keepdim_dim_1d', [0]),
('min', (S, S, S), ((S, S, S),), 'elementwise'),
('min', (S, S, S), ((S,),), 'elementwise_broadcast_rhs'),
('min', (S,), ((S, S, S),), 'elementwise_broadcast_lhs'),
('min', (S, 1, S), ((S, S),), 'elementwise_broadcast_all'),
('mean', (S, S, S), ()),
('mean', (S, S, S), (1,), 'dim', [0]),
('mean', (S, S, S), (1, True,), 'keepdim_dim', [0]),
('mean', (S,), (0,), 'dim_1d', [0]),
('mean', (S,), (0, True), 'keepdimdim_1d', [0]),
('kthvalue', (S, S, S), (2,)),
('kthvalue', (S, S, S), (2, 1,), 'dim', [1]),
('kthvalue', (S, S, S), (2, 1, True,), 'keepdim_dim', [1]),
('kthvalue', (S,), (2, 0,), 'dim_1d', [1]),
('kthvalue', (S,), (2, 0, True,), 'keepdim_dim_1d', [1]),
('median', (S, S, S), ()),
('median', (S, S, S), (1,), 'dim', [0]),
('median', (S, S, S), (1, True,), 'keepdim_dim', [0]),
('median', (S,), (0,), 'dim_1d', [0]),
('median', (S,), (0, True,), 'keepdim_dim_1d', [0]),
('mode', (S, S, S), ()),
('mode', (S, S, S), (1,), 'dim', [0]),
('mode', (S, S, S), (1, True,), 'keepdim_dim', [0]),
('mode', (S,), (0,), 'dim_1d', [0]),
('mode', (S,), (0, True,), 'keepdim_dim_1d', [0]),
('sum', (S, S, S), ()),
('sum', (S, S, S), (1,), 'dim', [0]),
('sum', (S, S, S), (1, True,), 'keepdim_dim', [0]),
('sum', (S,), (0,), 'dim_1d', [0]),
('sum', (S,), (0, True), 'keepdim_1d', [0]),
('prod', (S, S, S), ()),
('prod', (S, S, S), (1,), 'dim', [0]),
('prod', (S, S, S), (1, True,), 'keepdim_dim', [0]),
('prod', (S,), (0,), 'dim_1d', [0]),
('prod', (S,), (0, True), 'keepdim_1d', [0]),
('var', (S, S, S), ()),
('var', (S, S, S), (1,), 'dim', [0]),
('var', (S, S, S), (1, True), 'keepdim_dim', [0]),
('var', (S,), (0,), 'dim_1d', [0]),
('var', (S,), (0, True), 'keepdim_dim_1d', [0]),
('std', (S, S, S), ()),
('std', (S, S, S), (1,), 'dim', [0]),
('std', (S, S, S), (1, True), 'keepdim_dim', [0]),
('std', (S,), (0,), 'dim_1d', [0]),
('std', (S,), (0, True), 'keepdim_dim_1d', [0]),
('renorm', (S, S, S), (2, 1, 0.5), 'dim', [1]),
('renorm', (S, S, S), (1, 2, 3), 'norm_1'),
('repeat', (S, S, S, S), (2, 3, 1, 4)),
('cumsum', (S, S, S), (1,)),
('cumsum', (S,), (0,), '1d'),
('cumprod', (S, S, S), (1,), 'dim1', [0]),
('cumprod', prod_zeros(S, [0, 1]), (1,), 'zeros_dim', [0]),
('unfold', (S, S, S, S), (1, 3, 1)),
('unfold', (S, S, S), (2, 3, 2), 'lastdim'),
('addmm', (S, M), ((S, S), (S, M)),),
('addmm', (1,), ((S, S), (S, M)), 'broadcast_lhs'),
('addmm', (S, M), (0.2, 0.6, (S, S), (S, M)), 'coef'),
('addmm', (1,), (0.2, 0.6, (S, S), (S, M)), 'broadcast_lhs_coef'),
('addbmm', (S, M), ((S, S, S), (S, S, M)),),
('addbmm', (1,), ((S, S, S), (S, S, M)), 'broadcast_lhs'),
('addbmm', (S, M), (0.2, 0.6, (S, S, S), (S, S, M)), 'coef'),
('addbmm', (1,), (0.2, 0.6, (S, S, S), (S, S, M)), 'broadcast_lhs_coef'),
('baddbmm', (S, S, M), ((S, S, S), (S, S, M)),),
('baddbmm', (1,), ((S, S, S), (S, S, M)), 'broadcast_lhs'),
('baddbmm', (S, S, M), (0.2, 0.6, (S, S, S), (S, S, M)), 'coef'),
('baddbmm', (1,), (0.2, 0.6, (S, S, S), (S, S, M)), 'broadcast_lhs_coef'),
('addmv', (S,), ((S, M), (M,)),),
('addmv', (1,), ((S, M), (M,)), 'broadcast_lhs'),
('addmv', (S,), (0.2, 0.6, (S, M), (M,)), 'coef'),
('addmv', (1,), (0.2, 0.6, (S, M), (M,)), 'broadcast_lhs_coef'),
('addr', (S, M), ((S,), (M,)),),
('addr', (1,), ((S,), (M,)), 'broadcast_lhs'),
('addr', (S, M), (0.2, 0.6, (S,), (M,)), 'coef'),
('addr', (1,), (0.2, 0.6, (S,), (M,)), 'broadcast_lhs_coef'),
('dot', (L,), ((L,),),),
('mm', (S, M), ((M, S),)),
('bmm', (M, S, M), ((M, M, S),)),
('mv', (S, M), ((M,),)),
('ger', (S,), ((M,),)),
('matmul', (L,), ((L,),),),
('matmul', (S, M), ((M,),), "2d_1d"),
('matmul', (M, ), ((M, S),), "1d_2d"),
('matmul', (S, M), ((M, S),), "2d_2d"),
('matmul', (S, S, M, M), ((S, S, M, S),), "4d_4d"),
('matmul', (S, S, M, M), ((M,),), "4d_1d"),
('matmul', (M,), ((S, S, M, S),), "1d_4d"),
('addcmul', (S, S), ((S, S), (S, S))),
('addcmul', (S, S), ((S, 1), (1, S)), 'broadcast_rhs'),
('addcmul', (1,), ((S, S, 1), (1, S)), 'broadcast_all'),
('addcmul', (S, S), (0.5, (S, S), (S, S)), 'scale'),
('addcmul', (S, S), (0.5, (S, 1), (1, S)), 'scale_broadcast_rhs'),
('addcmul', (1,), (0.5, (S, S, 1), (1, S)), 'scale_broadcast_all'),
('addcdiv', (S, S), ((S, S), (S, S))),
('addcdiv', (S, S), ((S, 1), (1, S)), 'broadcast_rhs'),
('addcdiv', (1,), ((S, S, 1), (1, S)), 'broadcast_all'),
('addcdiv', (S, S), (0.5, (S, S), (S, S)), 'scale'),
('addcdiv', (S, S), (0.5, (S, 1), (1, S)), 'scale_broadcast_rhs'),
('addcdiv', (1,), (0.5, (S, S, 1), (1, S)), 'scale_broadcast_all'),
('norm', (S, S, S), (2,)),
('norm', (S, S, S), (2, 1), 'dim', [1]),
('norm', (S, S, S), (2, 1, True), 'keepdim_dim', [0]),
('norm', (S,), (2, 0), 'dim_1d', [1]),
('norm', (S,), (2, 0, True), 'keepdim_dim_1d', [0]),
('dist', (S, S, S), ((S, S, S),)),
('dist', (S, S, S), ((S,),), 'broadcast_rhs'),
('dist', (S,), ((S, S, S),), 'broadcast_lhs'),
('dist', (S, 1, S), ((S, S),), 'broadcast_all'),
('dist', (S, S, S), ((S, S, S), 4), '4'),
('dist', (S, S, S), ((S,), 4), '4_broadcast_rhs'),
('dist', (S,), ((S, S, S), 4), '4_broadcast_lhs'),
('dist', (S, 1, S), ((S, S), 4), '4_broadcast_all'),
('index_select', (S, S, S), (0, index_variable(2, S)), 'dim', [0]),
('diag', (M, M), (), '2d'),
('diag', (M,), (), '1d'),
('tril', (M, M), ()),
('triu', (M, M), ()),
('trace', (M, M), ()),
('cross', (S, 3), ((S, 3),)),
('cross', (S, 3, S), ((S, 3, S), 1), 'dim'),
('inverse', (S, S), (), '', (), [skipIfNoLapack]),
('gesv', (S, S), ((S, S),), '', (), [skipIfNoLapack]),
('clone', (S, M, S), ()),
('eq', (S, S, S), ((S, S, S),)),
('eq', (S, S, S), ((1,),), 'broadcast_rhs'),
('eq', (1,), ((S, S, S),), 'broadcast_lhs'),
('eq', (S, 1, S), ((S, S),), 'broadcast_all'),
('ne', (S, S, S), ((S, S, S),)),
('ne', (S, S, S), ((1,),), 'broadcast_rhs'),
('ne', (1,), ((S, S, S),), 'broadcast_lhs'),
('ne', (S, 1, S), ((S, S),), 'broadcast_all'),
('gt', (S, S, S), ((S, S, S),)),
('gt', (S, S, S), ((1,),), 'broadcast_rhs'),
('gt', (1,), ((S, S, S),), 'broadcast_lhs'),
('gt', (S, 1, S), ((S, S),), 'broadcast_all'),
('ge', (S, S, S), ((S, S, S),)),
('ge', (S, S, S), ((1,),), 'broadcast_rhs'),
('ge', (1,), ((S, S, S),), 'broadcast_lhs'),
('ge', (S, 1, S), ((S, S),), 'broadcast_all'),
('lt', (S, S, S), ((S, S, S),)),
('lt', (S, S, S), ((1,),), 'broadcast_rhs'),
('lt', (1,), ((S, S, S),), 'broadcast_lhs'),
('lt', (S, 1, S), ((S, S),), 'broadcast_all'),
('le', (S, S, S), ((S, S, S),)),
('le', (S, S, S), ((1,),), 'broadcast_rhs'),
('le', (1,), ((S, S, S),), 'broadcast_lhs'),
('le', (S, 1, S), ((S, S),), 'broadcast_all'),
('eq', (S, S, S), (0,), 'scalar'),
('ne', (S, S, S), (0,), 'scalar'),
('gt', (S, S, S), (0,), 'scalar'),
('ge', (S, S, S), (0,), 'scalar'),
('lt', (S, S, S), (0,), 'scalar'),
('le', (S, S, S), (0,), 'scalar'),
('permute', (1, 2, 3, 4), (0, 2, 3, 1)),
('select', (S, S, S), (1, 2), 'dim', [0]),
('narrow', (S, S, S), (1, 2, 2), 'dim', [0]),
('squeeze', (S, 1, S, 1), ()),
('squeeze', (S, 1, S, 1), (1,), '1_dim', [0]),
('squeeze', (S, 1, S, 1), (2,), 'not_1_dim', [0]),
('unsqueeze', (S, S, S), (0,), 'first', [0]),
('unsqueeze', (S, S, S), (1,), 'middle', [0]),
('unsqueeze', (S, S, S), (3,), 'last', [0]),
('masked_select', (M, M), (Variable(mask_not_all_zeros((M, M)), requires_grad=False),)),
('masked_select', (M, M), (Variable(mask_not_all_zeros((M,)), requires_grad=False),), 'broadcast_rhs'),
('masked_select', (M,), (Variable(mask_not_all_zeros((M, M)), requires_grad=False),), 'broadcast_lhs'),
('masked_select', (M, 1, M), (Variable(mask_not_all_zeros((M, M)), requires_grad=False),),
'broadcast_all'),
('masked_fill_', (M, M), (Variable(torch.ByteTensor(M, M).bernoulli_(), requires_grad=False), 10)),
# no lhs or all broadcast on masked_fill or masked_scatter because it's always inplace
('masked_fill_', (M, M), (Variable(torch.ByteTensor(M,).bernoulli_(), requires_grad=False), 10), 'broadcast_rhs'),
('masked_scatter_', (M, M), (Variable(torch.ByteTensor(M, M).bernoulli_(), requires_grad=False), (M, M))),
('masked_scatter_', (M, M), (Variable(torch.ByteTensor(M,).bernoulli_(), requires_grad=False), (M, M)),
'broadcast_rhs'),
]
# TODO: mm, bmm, mv, ger
# TODO: sort, topk (problem with indices)
# TODO: indexAdd, indexCopy, indexFill
# TODO: resize, resize_as (tensors only have resize_ and resize_as_)
# TODO: clamp with min/max
def create_input(call_args, requires_grad=True):
if not isinstance(call_args, tuple):
call_args = (call_args,)
def map_arg(arg):
if isinstance(arg, torch.Size) or isinstance(arg, dont_convert):
return arg
elif isinstance(arg, tuple) and not isinstance(arg[0], Variable):
return Variable(torch.randn(*arg).double(), requires_grad=requires_grad)
elif torch.is_tensor(arg):
if isinstance(arg, torch.FloatTensor):
return Variable(arg.double(), requires_grad=requires_grad)
else:
return Variable(arg, requires_grad=requires_grad)
else:
return arg
return tuple(map_arg(arg) for arg in call_args)
def unpack_variables(args):
if isinstance(args, Variable):
return args.data
elif isinstance(args, tuple):
return tuple(unpack_variables(elem) for elem in args)
else:
return args
ignore_inplace = set((
'test_DivConstantFunction_by_tensor',
))
gradgradcheck_exclude_classes = set((
'Norm',
'Prod',
))
for test in function_tests:
cls, constructor_args, call_args = test[:3]
basic_test_name = 'test_{}Function'.format(cls.__name__)
if len(test) >= 4:
basic_test_name += '_' + test[3]
dim_args_idx = test[4] if len(test) == 5 else []
skipTestIf = test[5] if len(test) == 6 else []
for dim_perm in product([-1, 1], repeat=len(dim_args_idx)):
test_name = basic_test_name + ''.join('_neg' + str(i) for i, idx in enumerate(dim_perm) if idx < 0)
def make_neg_dims(args):
for i in dim_args_idx:
assert isinstance(args[i], int), test_name
return tuple(arg * dim_perm[dim_args_idx.index(i)] if i in dim_args_idx else arg
for i, arg in enumerate(args))
if cls._is_legacy:
new_constructor_args = make_neg_dims(constructor_args)
new_call_args = call_args
else:
assert len(constructor_args) == 0, test_name
new_constructor_args = constructor_args
new_call_args = make_neg_dims(call_args)
def do_test(self, cls=cls, constructor_args=new_constructor_args,
call_args=new_call_args, test_name=test_name):
input = create_input(call_args)
if cls._is_legacy:
def apply_fn(*input):
return cls(*constructor_args)(*input)
def apply_inplace_fn(*input):
return cls(*constructor_args, inplace=True)(*input)
else:
def apply_fn(*input):
return cls.apply(*input)
def apply_inplace_fn(*input):
args = input + (True,) # for Python 2.7
return cls.apply(*args)
self.assertTrue(gradcheck(apply_fn, input, eps=1e-6, atol=PRECISION))
# check for correct type of input.data and input.grad.data
output = apply_fn(*input)
if isinstance(output, torch.autograd.Variable):
output.backward(torch.randn(*output.size()).type_as(output.data))
for inp in input:
if isinstance(inp, torch.autograd.Variable) and inp.grad is not None:
self.assertTrue(type(inp.data) == type(inp.grad.data))
self.assertTrue(inp.size() == inp.grad.size())
if cls.__name__ not in gradgradcheck_exclude_classes:
dummy_out = apply_fn(*input)
if isinstance(dummy_out, tuple):
grad_y = tuple(Variable(torch.randn(x.size()), requires_grad=x.requires_grad)
for x in dummy_out if isinstance(x, Variable))
else:
grad_y = (Variable(torch.randn(dummy_out.size()), requires_grad=dummy_out.requires_grad),)
self.assertTrue(gradgradcheck(apply_fn, input, grad_y,))
# can't broadcast inplace to left hand side
broadcast_skip_inplace = 'broadcast_lhs' in test_name or 'broadcast_all' in test_name
if test_name not in ignore_inplace and not broadcast_skip_inplace and issubclass(cls, InplaceFunction):
output = apply_fn(*input)
if not isinstance(output, tuple):
output = (output,)
inplace_input = deepcopy(input)
inplace_input_copy = tuple(i + 0 if i is not None else None for i in inplace_input)
inplace_output = apply_inplace_fn(*inplace_input_copy)
if not isinstance(inplace_output, tuple):
inplace_output = (inplace_output,)
self.assertEqual(inplace_output, output)
# Check that gradient is the same
for inp_i, i in zip(inplace_input, input):
if not isinstance(inp_i, Variable):
assert not isinstance(i, Variable)
continue
if inp_i.grad is not None:
inp_i.grad.data.zero_()
if i.grad is not None:
i.grad.data.zero_()
for io, o in zip(inplace_output, output):
grad = torch.randn(*io.size()).double()
io.backward(grad)
o.backward(grad)
for inp_i, i in zip(inplace_input, input):
if not isinstance(inp_i, Variable):
continue
self.assertEqual(inp_i.grad, i.grad)
assert not hasattr(TestAutograd, test_name), 'Two tests have the same name: ' + test_name
for skip in skipTestIf:
do_test = skip(do_test)
setattr(TestAutograd, test_name, do_test)
EXCLUDE_FUNCTIONAL = {
'addmm',
'addbmm',
'baddbmm',
'addmv',
'addr',
}
for test in method_tests:
name, self_size, args = test[:3]
basic_test_name = 'test_' + name + ('_' + test[3] if len(test) >= 4 else '')
dim_args_idx = test[4] if len(test) == 5 else []
skipTestIf = test[5] if len(test) == 6 else []
for dim_perm in product([-1, 1], repeat=len(dim_args_idx)):
test_name = basic_test_name
new_args = [arg * dim_perm[dim_args_idx.index(i)] if i in dim_args_idx else arg for i, arg in enumerate(args)]
test_name = basic_test_name + ''.join('_neg' + str(i) for i, idx in enumerate(dim_perm) if idx < 0)
new_args = tuple(new_args)
def do_test(self, name=name, self_size=self_size, args=new_args, test_name=test_name):
def check(name):
self_variable = create_input((self_size,), requires_grad=False)[0]
args_variable = create_input(args, requires_grad=False)
self_tensor = deepcopy(self_variable.data)
args_tensor = deepcopy(unpack_variables(args_variable))
output_variable = getattr(self_variable, name)(*args_variable)
output_tensor = getattr(self_tensor, name)(*args_tensor)
if not torch.is_tensor(output_tensor) and not isinstance(output_tensor, tuple):
output_tensor = torch.DoubleTensor((output_tensor,))
self.assertEqual(unpack_variables(output_variable), output_tensor)
# TODO: check that both have changed after adding all inplace ops
# functional interface tests
if hasattr(torch, name) and name not in EXCLUDE_FUNCTIONAL:
f_args_variable = (self_variable,) + args_variable
f_args_tensor = (self_tensor,) + args_tensor
output_variable = getattr(torch, name)(*f_args_variable)
output_tensor = getattr(torch, name)(*f_args_tensor)
if not torch.is_tensor(output_tensor) and not isinstance(output_tensor, tuple):
output_tensor = torch.DoubleTensor((output_tensor,))
self.assertEqual(unpack_variables(output_variable), output_tensor)
# check for correct type of input.data and input.grad.data
if name[-1] != '_':
self_variable = create_input((self_size,), requires_grad=True)[0]
args_variable = create_input(args, requires_grad=False)
output_variable = getattr(self_variable, name)(*args_variable)
if isinstance(output_variable, torch.autograd.Variable):
output_variable.backward(torch.randn(*output_variable.size()).type_as(output_variable.data))
self.assertTrue(type(self_variable.data) == type(self_variable.grad.data))
self.assertTrue(self_variable.size() == self_variable.grad.size())
check(name)
inplace_name = name + '_'
# can't broadcast inplace to left hand side
broadcast_skip_inplace = 'broadcast_lhs' in test_name or 'broadcast_all' in test_name
if hasattr(Variable(torch.ones(1)), inplace_name) and not broadcast_skip_inplace:
try:
check(inplace_name)
except Exception as e:
if 'only supports scalar' not in e.args[0]:
raise
assert not hasattr(TestAutograd, test_name), 'Two tests have the same name: ' + test_name
for skip in skipTestIf:
do_test = skip(do_test)
setattr(TestAutograd, test_name, do_test)
if __name__ == '__main__':
run_tests()