mirror of
https://github.com/zebrajr/pytorch.git
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de85abf226
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/22084 For DictPtr/ListPtr, default construction was disallowed because it was ambigious if it's supposed to create an empty list or a nullptr. But since we renamed them to Dict/List, we can now allow default construction without ambiguity. Differential Revision: D15948098 fbshipit-source-id: 942a9235b51608d1870ee4a2f2f0a5d0d45ec6e6
65 lines
1.7 KiB
C++
65 lines
1.7 KiB
C++
#pragma once
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#include <ATen/ATen.h>
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#include "ATen/core/ivalue.h"
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#include "test/cpp/jit/test_base.h"
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#include "test/cpp/jit/test_utils.h"
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namespace torch {
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namespace jit {
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namespace {
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using Var = SymbolicVariable;
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using namespace torch::autograd;
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void testIValue() {
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c10::List<int64_t> foo({3, 4, 5});
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ASSERT_EQ(foo.use_count(), 1);
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IValue bar{foo};
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ASSERT_EQ(foo.use_count(), 2);
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auto baz = bar;
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ASSERT_EQ(foo.use_count(), 3);
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auto foo2 = std::move(bar);
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ASSERT_EQ(foo.use_count(), 3);
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ASSERT_TRUE(foo2.isIntList());
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ASSERT_TRUE(bar.isNone());
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foo2 = IValue(4.0);
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ASSERT_TRUE(foo2.isDouble());
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ASSERT_EQ(foo2.toDouble(), 4.0);
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ASSERT_EQ(foo.use_count(), 2);
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ASSERT_TRUE(baz.toIntListRef().equals({3, 4, 5}));
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auto move_it = std::move(baz).toIntList();
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ASSERT_EQ(foo.use_count(), 2);
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ASSERT_TRUE(baz.isNone());
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IValue i(4);
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ASSERT_TRUE(i.isInt());
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ASSERT_EQ(i.toInt(), 4);
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IValue dlist(c10::List<double>({3.5}));
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ASSERT_TRUE(dlist.isDoubleList());
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ASSERT_TRUE(dlist.toDoubleListRef()
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.equals({3.5}));
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std::move(dlist).toDoubleList();
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ASSERT_TRUE(dlist.isNone());
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dlist = IValue(c10::List<double>({3.4}));
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ASSERT_TRUE(dlist.toDoubleListRef().equals({3.4}));
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IValue the_list(ivalue::Tuple::create({IValue(3.4), IValue(4), IValue(foo)}));
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ASSERT_EQ(foo.use_count(), 3);
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ASSERT_TRUE(the_list.isTuple());
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auto first = the_list.toTuple()->elements()[1];
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ASSERT_EQ(first.toInt(), 4);
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at::Tensor tv = at::rand({3, 4});
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IValue ten(tv);
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ASSERT_EQ(tv.use_count(), 2);
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auto ten2 = ten;
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ASSERT_EQ(tv.use_count(), 3);
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ASSERT_TRUE(ten2.toTensor().equal(ten.toTensor()));
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std::move(ten2).toTensor();
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ASSERT_EQ(tv.use_count(), 2);
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}
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} // namespace
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} // namespace jit
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} // namespace torch
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