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Impl for ParameterList (#41259)
Summary:
This is a new PR for https://github.com/pytorch/pytorch/issues/40850, https://github.com/pytorch/pytorch/issues/40987 and https://github.com/pytorch/pytorch/issues/41206(I unintentionally closed), as I have some issues for rebates for that one. Very sorry about that. And I have fixed the tests failed in that PR.
This diff contains the implementation of C++ API for ParameterList from https://github.com/pytorch/pytorch/issues/25883.
Refer to the Python API: bc9e8af218/torch/nn/modules/container.py (L376)
Not sure about some naming difference between C++ API and Python API, like `append`, should it be called `push_back`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41259
Test Plan: Add unit tests in this diff
Differential Revision: D22495780
Pulled By: glaringlee
fbshipit-source-id: 79ea3592db640f35477d445ecdaeafbdad814bec
This commit is contained in:
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162
test/cpp/api/parameterlist.cpp
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162
test/cpp/api/parameterlist.cpp
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#include <gtest/gtest.h>
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#include <torch/torch.h>
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#include <algorithm>
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#include <memory>
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#include <vector>
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#include <test/cpp/api/support.h>
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using namespace torch::nn;
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using namespace torch::test;
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struct ParameterListTest : torch::test::SeedingFixture {};
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TEST_F(ParameterListTest, ConstructsFromSharedPointer) {
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torch::Tensor ta = torch::randn({1, 2}, torch::requires_grad(true));
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torch::Tensor tb = torch::randn({1, 2}, torch::requires_grad(false));
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torch::Tensor tc = torch::randn({1, 2});
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ASSERT_TRUE(ta.requires_grad());
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ASSERT_FALSE(tb.requires_grad());
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ParameterList list(ta, tb, tc);
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ASSERT_EQ(list->size(), 3);
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}
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TEST_F(ParameterListTest, isEmpty) {
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torch::Tensor ta = torch::randn({1, 2}, torch::requires_grad(true));
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ParameterList list;
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ASSERT_TRUE(list->is_empty());
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list->append(ta);
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ASSERT_FALSE(list->is_empty());
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ASSERT_EQ(list->size(), 1);
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}
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TEST_F(ParameterListTest, PushBackAddsAnElement) {
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ParameterList list;
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torch::Tensor ta = torch::randn({1, 2}, torch::requires_grad(true));
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torch::Tensor tb = torch::randn({1, 2}, torch::requires_grad(false));
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torch::Tensor tc = torch::randn({1, 2});
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torch::Tensor td = torch::randn({1, 2, 3});
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ASSERT_EQ(list->size(), 0);
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ASSERT_TRUE(list->is_empty());
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list->append(ta);
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ASSERT_EQ(list->size(), 1);
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list->append(tb);
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ASSERT_EQ(list->size(), 2);
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list->append(tc);
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ASSERT_EQ(list->size(), 3);
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list->append(td);
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ASSERT_EQ(list->size(), 4);
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}
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TEST_F(ParameterListTest, ForEachLoop) {
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torch::Tensor ta = torch::randn({1, 2}, torch::requires_grad(true));
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torch::Tensor tb = torch::randn({1, 2}, torch::requires_grad(false));
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torch::Tensor tc = torch::randn({1, 2});
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torch::Tensor td = torch::randn({1, 2, 3});
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ParameterList list(ta, tb, tc, td);
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std::vector<torch::Tensor> params = {ta, tb, tc, td};
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ASSERT_EQ(list->size(), 4);
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int idx = 0;
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for (const auto& pair : *list) {
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ASSERT_TRUE(
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torch::all(torch::eq(pair.value(), params[idx++])).item<bool>());
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}
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}
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TEST_F(ParameterListTest, AccessWithAt) {
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torch::Tensor ta = torch::randn({1, 2}, torch::requires_grad(true));
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torch::Tensor tb = torch::randn({1, 2}, torch::requires_grad(false));
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torch::Tensor tc = torch::randn({1, 2});
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torch::Tensor td = torch::randn({1, 2, 3});
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std::vector<torch::Tensor> params = {ta, tb, tc, td};
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ParameterList list;
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for (auto& param : params) {
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list->append(param);
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}
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ASSERT_EQ(list->size(), 4);
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// returns the correct module for a given index
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for (size_t i = 0; i < params.size(); ++i) {
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ASSERT_TRUE(torch::all(torch::eq(list->at(i), params[i])).item<bool>());
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}
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for (size_t i = 0; i < params.size(); ++i) {
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ASSERT_TRUE(torch::all(torch::eq(list[i], params[i])).item<bool>());
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}
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// throws for a bad index
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ASSERT_THROWS_WITH(list->at(params.size() + 100), "Index out of range");
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ASSERT_THROWS_WITH(list->at(params.size() + 1), "Index out of range");
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ASSERT_THROWS_WITH(list[params.size() + 1], "Index out of range");
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}
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TEST_F(ParameterListTest, ExtendPushesParametersFromOtherParameterList) {
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torch::Tensor ta = torch::randn({1, 2}, torch::requires_grad(true));
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torch::Tensor tb = torch::randn({1, 2}, torch::requires_grad(false));
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torch::Tensor tc = torch::randn({1, 2});
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torch::Tensor td = torch::randn({1, 2, 3});
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torch::Tensor te = torch::randn({1, 2});
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torch::Tensor tf = torch::randn({1, 2, 3});
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ParameterList a(ta, tb);
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ParameterList b(tc, td);
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a->extend(*b);
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ASSERT_EQ(a->size(), 4);
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ASSERT_TRUE(torch::all(torch::eq(a[0], ta)).item<bool>());
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ASSERT_TRUE(torch::all(torch::eq(a[1], tb)).item<bool>());
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ASSERT_TRUE(torch::all(torch::eq(a[2], tc)).item<bool>());
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ASSERT_TRUE(torch::all(torch::eq(a[3], td)).item<bool>());
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ASSERT_EQ(b->size(), 2);
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ASSERT_TRUE(torch::all(torch::eq(b[0], tc)).item<bool>());
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ASSERT_TRUE(torch::all(torch::eq(b[1], td)).item<bool>());
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std::vector<torch::Tensor> c = {te, tf};
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b->extend(c);
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ASSERT_EQ(b->size(), 4);
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ASSERT_TRUE(torch::all(torch::eq(b[0], tc)).item<bool>());
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ASSERT_TRUE(torch::all(torch::eq(b[1], td)).item<bool>());
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ASSERT_TRUE(torch::all(torch::eq(b[2], te)).item<bool>());
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ASSERT_TRUE(torch::all(torch::eq(b[3], tf)).item<bool>());
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}
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TEST_F(ParameterListTest, PrettyPrintParameterList) {
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torch::Tensor ta = torch::randn({1, 2}, torch::requires_grad(true));
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torch::Tensor tb = torch::randn({1, 2}, torch::requires_grad(false));
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torch::Tensor tc = torch::randn({1, 2});
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ParameterList list(ta, tb, tc);
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ASSERT_EQ(
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c10::str(list),
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"torch::nn::ParameterList(\n"
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"(0): Parameter containing: [Float of size [1, 2]]\n"
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"(1): Parameter containing: [Float of size [1, 2]]\n"
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"(2): Parameter containing: [Float of size [1, 2]]\n"
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")");
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}
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TEST_F(ParameterListTest, IncrementAdd) {
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torch::Tensor ta = torch::randn({1, 2}, torch::requires_grad(true));
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torch::Tensor tb = torch::randn({1, 2}, torch::requires_grad(false));
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torch::Tensor tc = torch::randn({1, 2});
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torch::Tensor td = torch::randn({1, 2, 3});
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torch::Tensor te = torch::randn({1, 2});
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torch::Tensor tf = torch::randn({1, 2, 3});
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ParameterList listA(ta, tb, tc);
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ParameterList listB(td, te, tf);
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std::vector<torch::Tensor> tensors{ta, tb, tc, td, te, tf};
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int idx = 0;
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*listA += *listB;
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ASSERT_TRUE(torch::all(torch::eq(listA[0], ta)).item<bool>());
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ASSERT_TRUE(torch::all(torch::eq(listA[1], tb)).item<bool>());
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ASSERT_TRUE(torch::all(torch::eq(listA[2], tc)).item<bool>());
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ASSERT_TRUE(torch::all(torch::eq(listA[3], td)).item<bool>());
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ASSERT_TRUE(torch::all(torch::eq(listA[4], te)).item<bool>());
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ASSERT_TRUE(torch::all(torch::eq(listA[5], tf)).item<bool>());
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for (const auto& P : listA->named_parameters(false))
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ASSERT_TRUE(torch::all(torch::eq(P.value(), tensors[idx++])).item<bool>());
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ASSERT_EQ(idx, 6);
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}
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