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e0643fa3fc
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/66744 Modified loops in files under fbsource/fbcode/caffe2/ from the format `for(TYPE var=x0;var<x_max;x++)` to the format `for(const auto var: irange(xmax))` This was achieved by running r-barnes's loop upgrader script (D28874212) with some modification to exclude all files under /torch/jit and a number of reversions or unused variable suppression warnings added by hand. Test Plan: Sandcastle Reviewed By: ngimel Differential Revision: D31705358 fbshipit-source-id: d6ea350cbaa8f452fc78f238160e5374be637a48
106 lines
3.7 KiB
C++
106 lines
3.7 KiB
C++
#include <gtest/gtest.h>
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#include <c10/util/irange.h>
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#include <torch/csrc/distributed/rpc/utils.h>
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#include <torch/torch.h>
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#include <memory>
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#include <string>
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#include <vector>
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using ::testing::IsSubstring;
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TEST(WireSerialize, Base) {
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auto run = [](const std::string& payload,
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const std::vector<at::Tensor>& tensors) {
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std::string serialized;
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{
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std::vector<char> mpayload(payload.begin(), payload.end());
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std::vector<at::Tensor> mtensors = tensors;
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serialized = torch::distributed::rpc::wireSerialize(
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std::move(mpayload), std::move(mtensors));
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}
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auto deser = torch::distributed::rpc::wireDeserialize(
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serialized.data(), serialized.size());
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EXPECT_EQ(payload.size(), deser.first.size());
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EXPECT_EQ(tensors.size(), deser.second.size());
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if (payload.size() > 0) {
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EXPECT_TRUE(
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memcmp(deser.first.data(), payload.data(), payload.size()) == 0);
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}
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for (const auto i : c10::irange(tensors.size())) {
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EXPECT_TRUE(torch::equal(tensors[i], deser.second[i]));
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}
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};
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run("", {});
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run("hi", {});
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run("", {torch::randn({5, 5})});
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run("hi", {torch::randn({5, 5})});
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run("more", {torch::randn({5, 5}), torch::rand({10, 10})});
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}
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TEST(WireSerialize, RecopySparseTensors) {
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// Take a 1K row of a 1M tensors, and make sure we don't send across 1M rows.
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constexpr size_t k1K = 1024;
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at::Tensor main = torch::randn({k1K, k1K});
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at::Tensor tiny = main.select(0, 2); // Select a row in the middle
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EXPECT_EQ(tiny.numel(), k1K);
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EXPECT_EQ(tiny.storage().nbytes() / tiny.dtype().itemsize(), k1K * k1K);
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auto ser = torch::distributed::rpc::wireSerialize({}, {tiny});
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auto deser = torch::distributed::rpc::wireDeserialize(ser.data(), ser.size());
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EXPECT_TRUE(torch::equal(tiny, deser.second[0]));
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EXPECT_LT(ser.size(), (tiny.element_size() * k1K) + k1K);
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}
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TEST(WireSerialize, CloneSparseTensors) {
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constexpr size_t k1K = 1024;
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at::Tensor big = torch::randn({k1K, k1K});
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auto v1 = torch::distributed::rpc::cloneSparseTensors({big});
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EXPECT_EQ(v1.get(0).storage(), big.storage()); // Not cloned
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at::Tensor tiny = big.select(0, 2); // Select a row in the middle
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auto v2 = torch::distributed::rpc::cloneSparseTensors({tiny});
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EXPECT_NE(&v2.get(0).storage(), &tiny.storage()); // Cloned.
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EXPECT_TRUE(torch::equal(v2.get(0), tiny));
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at::Tensor sparse = at::empty({2, 3}, at::dtype<float>().layout(at::kSparse));
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auto v3 = torch::distributed::rpc::cloneSparseTensors({sparse});
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// There is no storage() to compare, but at least confirm equality.
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EXPECT_TRUE(v3.get(0).is_same(sparse));
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}
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TEST(WireSerialize, Errors) {
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auto checkMessage = [](auto&& f, const char* msg) {
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try {
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f();
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FAIL();
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} catch (const std::exception& e) {
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EXPECT_PRED_FORMAT2(IsSubstring, msg, e.what());
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} catch (...) {
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FAIL();
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}
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};
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checkMessage(
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[]() { (void)torch::distributed::rpc::wireDeserialize("", 0); },
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"failed parse");
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checkMessage(
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[]() { (void)torch::distributed::rpc::wireDeserialize(" ", 1); },
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"failed parse");
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auto serialized =
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torch::distributed::rpc::wireSerialize({}, {torch::randn({5, 5})});
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checkMessage(
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[&]() {
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(void)torch::distributed::rpc::wireDeserialize(
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serialized.data(), serialized.size() / 2);
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},
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"failed bounds");
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}
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// Enable this once JIT Pickler supports sparse tensors.
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TEST(WireSerialize, DISABLED_Sparse) {
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at::Tensor main = at::empty({2, 3}, at::dtype<float>().layout(at::kSparse));
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auto ser = torch::distributed::rpc::wireSerialize({}, {main.to(at::kSparse)});
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auto deser = torch::distributed::rpc::wireDeserialize(ser.data(), ser.size());
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EXPECT_TRUE(torch::equal(main, deser.second[0]));
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}
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