test_autograd.py

import contextlib
import gc
import sys
import math
import torch
import unittest
from copy import deepcopy
from collections import OrderedDict
from itertools import product
from torch.autograd import gradcheck

from common import TestCase, run_tests
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


class TestAutograd(TestCase):

    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_variables=True)
        self.assertEqual(counter[0], 1)

        test2 = z.register_hook(lambda *args: bw_hook(2, *args))
        z.backward(torch.ones(5, 5), retain_variables=True)
        self.assertEqual(counter[0], 4)

        test2.remove()
        z.backward(torch.ones(5, 5), retain_variables=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_variables=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_backward(self):
        self._test_backward()

    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)

    @unittest.skip("BasicEngine is out of date")
    def test_backward_basic_engine(self):
        with backward_engine(torch.autograd.engine.BasicEngine):
            self._test_backward()

    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_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.creator)
        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.creator)

    def test_indexing(self):
        x = torch.arange(1, 17).resize_(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)

            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)))

    def test_basic_op_grad(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)

        def y():
            # .mm() depends on the grad_output being of correct size
            return b.mm(Variable(torch.rand(1, 2) + 1e-2))

        (x + y()).sum().backward()
        (x - y()).sum().backward()
        (x * y()).sum().backward()
        (x / y()).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_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_previous_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.creator)
        previous_functions = a.creator.previous_functions
        self.assertEqual(len(previous_functions), 2)
        self.assertIs(previous_functions[0][0], x)
        self.assertEqual(previous_functions[0][1], 0)
        self.assertIs(previous_functions[1][0], y)
        self.assertEqual(previous_functions[1][1], 0)

        b = a + 5
        previous_functions = b.creator.previous_functions
        self.assertEqual(len(previous_functions), 1)
        self.assertIs(previous_functions[0][0], a.creator)

    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_variables=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_variables=True)
        # r doesn't use the modified value in backward, so it should succeed
        r.backward(torch.ones(5, 5), retain_variables=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_variables=True)
        self.assertEqual(x.grad.data, torch.ones(5, 5) / 2)
        w.backward(torch.ones(5, 5), retain_variables=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_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_tensor((5, 5), 3)
        self._test_setitem_tensor((5,), 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_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), creator=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.creator)
        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_variables=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]:
            y = Variable(torch.randn(5, 5).type(t))
            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)[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.creator, 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.creator, None)
        x.sum().backward()
        self.assertEqual(y.grad.data, torch.ones(5))
        self.assertEqual(z.grad.data, torch.ones(5) * 2)

    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)
            self.assertEqual(fn(*args).data, fn(*unpacked_args))

        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)))

    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).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_variables=True))
        samples_multi.reinforce(torch.randn(2, 5))
        self.assertRaises(RuntimeError, lambda: z.backward(retain_variables=True))
        samples_multi_flat.reinforce(torch.randn(5))
        self.assertRaises(RuntimeError, lambda: z.backward(retain_variables=True))
        samples_bernoulli.reinforce(torch.randn(2, 10))
        self.assertRaises(RuntimeError, lambda: z.backward(retain_variables=True))
        samples_norm.reinforce(torch.randn(2, 10))
        self.assertRaises(RuntimeError, lambda: z.backward(retain_variables=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_variables=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 gather_variable(shape, index_dim, max_indices):
    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]])
    return Variable(index, requires_grad=False)


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)


L = 20
M = 10
S = 5
function_tests = [
    (Add, (), ((M, M), (M, M))),
    (Sub, (), ((M, M), (M, M))),
    (Mul, (), ((M, M), (M, M))),
    (Div, (), ((M, M), torch.rand(M, M) + 5e-2)),
    (Pow, (), (torch.rand(M, M) + 1e-3, torch.rand(M, M) + 0.1)),
    (AddConstant, (3.14,), ((L, L),)),
    (SubConstant, (3.14,), ((L, L),)),
    (SubConstant, (3.14, True), ((L, L),), 'from_tensor'),
    (MulConstant, (3.14,), ((L, L),)),
    (DivConstant, (3.14, True), (torch.rand(L, L) + 1e-1,), 'by_tensor'),
    (PowConstant, (3.14,), (torch.rand(L, L),)),
    (PowConstant, (3.14, True), (torch.rand(L, L),), 'tensor_power'),
    (Transpose, (0, 1), (torch.rand(L, L),), '2d', [0, 1]),
    (Transpose, (2, 0), (torch.rand(S, S, S),), '3d', [0, 1]),
    (Permute, ((0, 4, 3, 5, 1, 2),), ((1, 2, 3, 4, 5, 6),)),
    (Index, ((1, 2),), (torch.rand(S, S, S),)),
    (Index, (slice(0, 3),), (torch.rand(S, S, S),), 'slice'),
    (Index, ((slice(0, 3), 1),), (torch.rand(S, S, S),), 'slice_index'),
    (View, (S * S, S), (torch.rand(S, S, S),)),
    (Expand, ((5, S, 5, S, 5),), ((1, S, 1, S, 1),)),
    (Expand, ((S, S, S),), ((S, 1),), 'new_dim'),
    (Expand, ((S, S, S),), ((1, S),), 'new_dim_front'),
    (Expand, ((S, S, S),), ((1,),), '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, (0, 1), ((S, S, S),)),
    (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))),
    (Cmin, (), ((S, S, S), (S, S, S))),
    (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, (1.5,), ((S, S, S),)),
    (Lerp, (0.2,), ((S, S, S), (S, S, S))),
    (Rsqrt, (), (torch.rand(S, S, S) + 1e-2,)),
    (Remainder, (1.5,), ((S, S, S),)),
    (CmaxConstant, (0.5,), ((S, S, S),)),
    (CminConstant, (0.5,), ((S, S, S),)),
    (Mean, (), ((S, S, S),)),
    (Mean, (1,), ((S, S, S),), 'dim', [0]),
    (Sum, (), ((S, S, S),)),
    (Sum, (1,), ((S, S, S),), 'dim', [0]),
    (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, (1,), ((S, S, S),), 'dim', [0]),
    (Prod, (1,), (prod_zeros(S, [0, 1]),), 'zeros_dim2', [0]),
    (Prod, (1,), (prod_zeros(S, [0, 2]),), 'zeros_dim1', [0]),
    (Prod, (1,), (prod_zeros(S, [1, 2]),), 'zeros_dim0', [0]),
    (Addmm, (), ((S, M), (S, S), (S, M)),),
    (Addmm, (0.1, 1), ((S, M), (S, S), (S, M)), 'coef'),
    (Addbmm, (), ((S, M), (S, S, S), (S, S, M)),),
    (Addbmm, (0.1, 0.4), ((S, M), (S, S, S), (S, S, M)), 'coef'),
    (Baddbmm, (), ((S, S, M), (S, S, S), (S, S, M)),),
    (Baddbmm, (0.1, 0.4), ((S, S, M), (S, S, S), (S, S, M)), 'coef'),
    (Addmv, (), ((S,), (S, M), (M,)),),
    (Addmv, (0.1, 0.4), ((S,), (S, M), (M,)), 'coef'),
    (Addr, (), ((S, M), (S,), (M,)),),
    (Addr, (0.1, 0.4), ((S, M), (S,), (M,)), 'coef'),
    (Dot, (), ((L,), (L,)),),
    (Max, (), ((S, S, S),),),
    (Repeat, (torch.Size([2, 3, 1, 2]),), ((S, S, S, S),)),
    (Cumsum, (0,), ((S, S, S),)),
    (Cumsum, (1,), ((S, S, S),), 'dim1'),
    (Cumsum, (0,), ((S,),), '1d'),
    (Min, (), ((S, S, S),),),
    (Max, (1,), ((S, S, S),), 'dim', [0]),
    (Min, (1,), ((S, S, S),), 'dim', [0]),
    (Mode, (1,), ((S, S, S),), 'dim', [0]),
    (Kthvalue, (2, 0), ((S, S, S),),),
    (Median, (0,), ((S, S, S),),),
    (Norm, (1.5,), (torch.rand(S, S, S),), '1_5'),
    (Norm, (), ((S, S, S),), '2'),
    (Norm, (3,), ((S, S, S),), '3'),
    (Norm, (1.5, 1), (torch.rand(S, S, S),), '1_5_dim', [1]),
    (Norm, (2, 1), ((S, S, S),), '2_dim', [1]),
    (Norm, (3, 1), ((S, S, S),), '3_dim', [1]),
    (Addcmul, (), ((S, S), (S, S), (S, S))),
    (Addcmul, (0.6,), ((S, S), (S, S), (S, S)), 'scale'),
    (Addcdiv, (), ((S, S), (S, S), torch.rand(S, S) + 5e-2)),
    (Addcdiv, (0.6,), ((S, S), (S, S), torch.rand(S, S) + 5e-2), 'scale'),
    (IndexAdd, (0,), ((S, S), index_variable(2, S), (2, S))),
    # (IndexCopy,     (0,),               ((S, S), index_variable(2, S), (2, S))      ),
    (IndexFill, (0, 2), ((S, S), index_variable(2, S))),
    (IndexSelect, (0,), ((S, S), index_variable(2, S))),
    (Gather, (0,), ((M, S), gather_variable((S, S), 1, M))),
    (Gather, (1,), ((M, S), gather_variable((M, S // 2), 0, S)), 'dim1', [0]),
    (Scatter, (0,), ((M, S), gather_variable((S, S), 1, M), (S, S))),
    (Scatter, (1,), ((M, S), gather_variable((M, S // 2), 0, S), (M, S // 2)), 'dim1', [0]),
    (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), ((S, S, S),)),
    (Diag, (), ((S, S),), '2d'),
    (Diag, (), ((S,),), '1d'),
    (Diag, (1,), ((S, S),), '2d_1'),
    (Diag, (2,), ((S, S),), '2d_2'),
    (Tril, (), ((S, S),)),
    (Tril, (2,), ((S, S),), 'idx'),
    (Triu, (), ((S, S),)),
    (Triu, (2,), ((S, S),), 'idx'),
    (Trace, (), ((S, S),)),
    (Cross, (), ((S, 3), (S, 3))),
    (Cross, (1,), ((S, 3, S), (S, 3, S)), 'dim'),
    (Clone, (), ((S, M, S),)),
    (Squeeze, (), ((S, 1, M, 1),)),
    (Squeeze, (1,), ((S, 1, M, 1),), 'dim', [0]),
    (Unsqueeze, (0,), ((S, M, S),), '0'),
    (Unsqueeze, (1,), ((S, M, S),), '1', [0]),
    (Unsqueeze, (2,), ((S, M, S),), '2', [0]),
    # (MaskedCopy,    (),                 ((S, S), Variable(torch.randn(S, S).gt(0), requires_grad=False), (S, S),)),
    (MaskedFill, (10,), ((S, S), Variable(torch.randn(S, S).gt(0), requires_grad=False))),
    (MaskedSelect, (), ((S, S), Variable(torch.randn(S, S).gt(0), requires_grad=False))),
    (Sort, (), ((S, M, S),)),
    (Sort, (1,), ((S, M, S),), 'dim', [0]),
    (Sort, (1, True), ((S, M, S),), 'dim_desc', [0]),
    (Topk, (3,), ((S, M, S),)),
    (Topk, (3, 1), ((S, M, S),), 'dim', [1]),
    (Topk, (3, 1, True), ((S, M, S),), 'dim_desc', [1]),
    (Topk, (3, 1, True, True), ((S, M, S),), 'dim_desc_sort', [1]),
]


method_tests = [
    ('add', (S, S, S), ((S, S, S),)),
    ('add', (S, S, S), (3.14,), 'constant'),
    ('sub', (S, S, S), ((S, S, S),)),
    ('sub', (S, S, S), (3.14,), 'constant'),
    ('mul', (S, S, S), ((S, S, S),)),
    ('mul', (S, S, S), (3.14,), 'constant'),
    ('div', (S, S, S), ((S, S, S),)),
    ('div', (S, S, S), (3.14,), 'constant'),
    ('pow', (S, S, S), ((S, S, S),)),
    ('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'),
    ('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,)),
    ('remainder', (S, S, S), (1.5,)),
    ('lerp', (S, S, S), ((S, S, S), 0.4)),
    ('max', (S, S, S), ()),
    ('max', (S, S, S), (1,), 'dim', [0]),
    ('max', (S, S, S), ((S, S, S),), 'elementwise'),
    ('min', (S, S, S), ()),
    ('min', (S, S, S), (1,), 'dim', [0]),
    ('min', (S, S, S), ((S, S, S),), 'elementwise'),
    ('mean', (S, S, S), ()),
    ('mean', (S, S, S), (1,), 'dim', [0]),
    ('sum', (S, S, S), ()),
    ('sum', (S, S, S), (1,), 'dim', [0]),
    ('prod', (S, S, S), ()),
    ('prod', (S, S, S), (1,), 'dim', [0]),
    ('var', (S, S, S), ()),
    ('var', (S, S, S), (1,), 'dim', [0]),
    ('std', (S, S, S), ()),
    ('std', (S, S, S), (1,), 'dim', [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'),
    ('addmm', (S, M), ((S, S), (S, M)),),
    ('addmm', (S, M), (0.2, 0.6, (S, S), (S, M)), 'coef'),
    ('addbmm', (S, M), ((S, S, S), (S, S, M)),),
    ('addbmm', (S, M), (0.2, 0.6, (S, S, S), (S, S, M)), 'coef'),
    ('baddbmm', (S, S, M), ((S, S, S), (S, S, M)),),
    ('baddbmm', (S, S, M), (0.2, 0.6, (S, S, S), (S, S, M)), 'coef'),
    ('addmv', (S,), ((S, M), (M,)),),
    ('addmv', (S,), (0.2, 0.6, (S, M), (M,)), 'coef'),
    ('addr', (S, M), ((S,), (M,)),),
    ('addr', (S, M), (0.2, 0.6, (S,), (M,)), 'coef'),
    ('dot', (L,), ((L,),),),
    ('addcmul', (S, S), ((S, S), (S, S))),
    ('addcmul', (S, S), (0.5, (S, S), (S, S)), 'scale'),
    ('addcdiv', (S, S), ((S, S), (S, S))),
    ('addcdiv', (S, S), (0.5, (S, S), (S, S)), 'scale'),
    ('norm', (S, S, S), (2,)),
    ('norm', (S, S, S), (2, 1), 'dim', [1]),
    ('dist', (S, S, S), ((S, S, S),)),
    ('dist', (S, S, S), ((S, S, S), 4), '4'),
    ('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'),
    ('clone', (S, M, S), ()),
    ('eq', (S, S, S), ((S, S, S),)),
    ('ne', (S, S, S), ((S, S, S),)),
    ('gt', (S, S, S), ((S, S, S),)),
    ('ge', (S, S, S), ((S, S, S),)),
    ('lt', (S, S, S), ((S, S, S),)),
    ('le', (S, S, S), ((S, S, S),)),
    ('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(torch.ByteTensor(M, M).bernoulli_(), requires_grad=False),)),
    ('masked_fill_', (M, M), (Variable(torch.ByteTensor(M, M).bernoulli_(), requires_grad=False), 10)),
    ('masked_copy_', (M, M), (Variable(torch.ByteTensor(M, M).bernoulli_(), requires_grad=False), (M, M))),
]
# TODO: mm, bmm, mv, ger
# TODO: max, min with dim (problem with indices)
# TODO: mode, median, sort, kthvalue, 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, 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',
))


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 []

    for dim_perm in product([-1, 1], repeat=len(dim_args_idx)):
        test_name = basic_test_name
        new_constructor_args = [arg * dim_perm[dim_args_idx.index(i)] if i in dim_args_idx else arg
                                for i, arg in enumerate(constructor_args)]
        test_name = basic_test_name + ''.join('_neg' + str(i) for i, idx in enumerate(dim_perm) if idx < 0)
        new_constructor_args = tuple(new_constructor_args)

        def do_test(self, cls=cls, constructor_args=new_constructor_args,
                    call_args=call_args, test_name=test_name):
            input = create_input(call_args)
            self.assertEqual(gradcheck(cls(*constructor_args), input, eps=1e-6, atol=PRECISION), True)

            if test_name not in ignore_inplace and issubclass(cls, InplaceFunction):
                output = cls(*constructor_args)(*input)
                if not isinstance(output, tuple):
                    output = (output,)
                inplace_input = deepcopy(input)
                inplace_input_copy = tuple(i + 0 for i in inplace_input)
                fn = cls(*constructor_args, inplace=True)
                inplace_output = 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 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):
                    self.assertEqual(inp_i.grad, i.grad)

        assert not hasattr(TestAutograd, test_name), 'Two tests have the same name: ' + test_name
        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 []

    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(name)
            inplace_name = name + '_'
            if hasattr(Variable(torch.ones(1)), inplace_name):
                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
        setattr(TestAutograd, test_name, do_test)


if __name__ == '__main__':
    run_tests()