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test_argument_spec.cpp
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test_argument_spec.cpp
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#include <gtest/gtest.h>
#include <torch/csrc/jit/api/function_impl.h>
#include <torch/csrc/jit/runtime/argument_spec.h>
#include <torch/jit.h>
#include "test/cpp/jit/test_utils.h"
namespace torch {
namespace jit {
namespace {
at::Device device(const autograd::Variable& v) {
// NOLINTNEXTLINE(cppcoreguidelines-narrowing-conversions,bugprone-narrowing-conversions)
return v.device();
}
bool isEqual(at::IntArrayRef lhs, at::IntArrayRef rhs) {
return lhs.size() == rhs.size() &&
std::equal(lhs.begin(), lhs.end(), rhs.begin());
}
bool isEqual(const CompleteArgumentInfo& ti, const autograd::Variable& v) {
if (!ti.defined())
return ti.defined() == v.defined();
return ti.device() == device(v) && ti.requires_grad() == v.requires_grad() &&
ti.type() == v.scalar_type() && isEqual(ti.sizes(), v.sizes()) &&
isEqual(ti.strides(), v.strides());
}
bool isEqual(const ArgumentInfo& ti, const autograd::Variable& v) {
if (!ti.defined())
return ti.defined() == v.defined();
return ti.device() == device(v) && ti.requires_grad() == v.requires_grad() &&
ti.type() == v.scalar_type() && ti.dim() == v.dim();
}
autograd::Variable var(
at::TensorOptions t,
at::IntArrayRef sizes,
bool requires_grad) {
return autograd::make_variable(at::rand(sizes, t), requires_grad);
}
autograd::Variable undef() {
return autograd::Variable();
}
} // namespace
TEST(ArgumentSpecTest, CompleteArgumentSpec_CUDA) {
auto const CF = at::CPU(at::kFloat);
auto const CD = at::CPU(at::kDouble);
auto const GF = at::CUDA(at::kFloat);
auto const GD = at::CUDA(at::kDouble);
auto list = createStack(
{var(CF, {1}, true),
var(CD, {1, 2}, false),
var(GF, {}, true),
var(GD, {4, 5, 6}, false),
undef()});
// make sure we have some non-standard strides
list[1].toTensor().transpose_(0, 1);
// same list but different backing values
auto list2 = createStack(
{var(CF, {1}, true),
var(CD, {1, 2}, false),
var(GF, {}, true),
var(GD, {4, 5, 6}, false),
undef()});
list2[1].toTensor().transpose_(0, 1);
CompleteArgumentSpec a(true, list);
CompleteArgumentSpec b(true, list);
ASSERT_EQ(a.hashCode(), b.hashCode());
ASSERT_EQ(a, b);
CompleteArgumentSpec d(true, list2);
ASSERT_EQ(d, a);
ASSERT_EQ(d.hashCode(), a.hashCode());
for (size_t i = 0; i < list.size(); ++i) {
ASSERT_TRUE(isEqual(a.at(i), list[i].toTensor()));
}
CompleteArgumentSpec no_grad(/*with_grad=*/false, list);
ASSERT_TRUE(no_grad != a);
std::unordered_set<CompleteArgumentSpec> spec;
spec.insert(a); // we use a below, so no move
ASSERT_TRUE(spec.count(b) > 0);
ASSERT_EQ(spec.count(no_grad), 0);
spec.insert(std::move(no_grad));
ASSERT_EQ(spec.count(CompleteArgumentSpec(true, list)), 1);
list2[1].toTensor().transpose_(0, 1);
CompleteArgumentSpec c(true, list2); // same as list, except for one stride
ASSERT_FALSE(c == a);
ASSERT_EQ(spec.count(c), 0);
Stack stack = {var(CF, {1, 2}, true), 3, var(CF, {1, 2}, true)};
CompleteArgumentSpec with_const(true, stack);
ASSERT_EQ(with_const.at(2).sizes().size(), 2);
}
// TODO: this test was disabled for unknown reasons and doesn't run.
// static size_t hashCode(const TensorTypePtr& ptr) {
// return std::hash<TensorType>()(*ptr.get());
// }
// TEST(ArgumentSpecTest, VaryingShape) {
// c10::VaryingShape<int64_t> vs(c10::optional<size_t>{});
// auto ptt_empty1 = TensorType::create({}, {}, vs, vs, false);
// auto ptt_empty2 = TensorType::create({}, {}, vs, vs, false);
// ASSERT_EQ(hashCode(ptt_empty1), hashCode(ptt_empty2));
// c10::VaryingShape<int64_t> vs22(std::vector<int64_t>{2, 2});
// auto ptt_vs22_vs22_1 = TensorType::create({}, {}, vs22, vs22, false);
// auto ptt_vs22_vs22_2 = TensorType::create({}, {}, vs22, vs22, false);
// ASSERT_EQ(hashCode(ptt_vs22_vs22_1), hashCode(ptt_vs22_vs22_2));
// c10::VaryingShape<int64_t> vs23(std::vector<int64_t>{2, 3});
// auto ptt_vs22_vs23_2 = TensorType::create({}, {}, vs22, vs23, false);
// ASSERT_NE(hashCode(ptt_vs22_vs22_1), hashCode(ptt_vs22_vs23_2));
// auto ptt_vs22_vs22_1_true = TensorType::create({}, {}, vs22, vs22, true);
// auto ptt_vs22_vs22_2_true = TensorType::create({}, {}, vs22, vs22, true);
// ASSERT_EQ(hashCode(ptt_vs22_vs22_1_true), hashCode(ptt_vs22_vs22_2_true));
// auto ptt_vs22_vs22_1_false = TensorType::create({}, {}, vs22, vs22, false);
// ASSERT_NE(hashCode(ptt_vs22_vs22_1_true), hashCode(ptt_vs22_vs22_1_false));
// }
TEST(ArgumentSpecTest, Basic_CUDA) {
auto& CF = at::CPU(at::kFloat);
auto& CD = at::CPU(at::kDouble);
auto& GF = at::CUDA(at::kFloat);
auto& GD = at::CUDA(at::kDouble);
auto graph = toGraphFunction(jit::compile(R"JIT(
def fn(a, b, c, d, e):
return a, b, c, d, e
)JIT")
->get_function("fn"))
.graph();
ArgumentSpecCreator arg_spec_creator(*graph);
auto list = createStack(
{var(CF, {1}, true),
var(CD, {1, 2}, false),
var(GF, {}, true),
var(GD, {4, 5, 6}, false),
undef()});
// make sure we have some non-standard strides
list[1].toTensor().transpose_(0, 1);
// same list but different backing values
auto list2 = createStack(
{var(CF, {1}, true),
var(CD, {1, 2}, false),
var(GF, {}, true),
var(GD, {4, 5, 6}, false),
undef()});
list2[1].toTensor().transpose_(0, 1);
ArgumentSpec a = arg_spec_creator.create(true, list);
ArgumentSpec b = arg_spec_creator.create(true, list);
ASSERT_EQ(a.hashCode(), b.hashCode());
ASSERT_EQ(a, b);
ArgumentSpec d = arg_spec_creator.create(true, list2);
ASSERT_EQ(d, a);
ASSERT_EQ(d.hashCode(), a.hashCode());
for (size_t i = 0; i < list.size(); ++i) {
ASSERT_TRUE(isEqual(a.tensorAt(i), list[i].toTensor()));
}
ArgumentSpec no_grad = arg_spec_creator.create(/*with_grad=*/false, list);
ASSERT_TRUE(no_grad != a);
std::unordered_set<ArgumentSpec> spec;
spec.insert(a); // we still need a for the test below
ASSERT_TRUE(spec.count(b) > 0);
ASSERT_EQ(spec.count(no_grad), 0);
spec.insert(std::move(no_grad));
ASSERT_EQ(spec.count(arg_spec_creator.create(true, list)), 1);
list2[1].toTensor().transpose_(0, 1);
ArgumentSpec c = arg_spec_creator.create(
true, list2); // same as list, except for one stride, used to be
// different, now the same
ASSERT_TRUE(c == a);
ASSERT_EQ(spec.count(c), 1);
}
} // namespace jit
} // namespace torch