mirror of
https://github.com/nomic-ai/kompute.git
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169 lines
4.5 KiB
C++
169 lines
4.5 KiB
C++
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#include "gtest/gtest.h"
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#include <chrono>
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#include "kompute/Kompute.hpp"
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TEST(TestAsyncOperations, TestManagerParallelExecution)
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{
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// This test is built for NVIDIA 1650. It assumes:
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// * Queue family 0 and 2 have compute capabilities
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// * GPU is able to process parallel shader code across different families
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uint32_t size = 10;
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uint32_t numParallel = 2;
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std::string shader(R"(
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#version 450
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layout (local_size_x = 1) in;
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layout(set = 0, binding = 0) buffer b { float pb[]; };
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shared uint sharedTotal[1];
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void main() {
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uint index = gl_GlobalInvocationID.x;
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sharedTotal[0] = 0;
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for (int i = 0; i < 100000000; i++)
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{
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atomicAdd(sharedTotal[0], 1);
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}
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pb[index] = sharedTotal[0];
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}
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)");
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std::vector<float> data(size, 0.0);
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std::vector<float> resultSync(size, 100000000);
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std::vector<float> resultAsync(size, 100000000);
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kp::Manager mgr;
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std::vector<std::shared_ptr<kp::Tensor>> inputsSyncB;
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for (uint32_t i = 0; i < numParallel; i++) {
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inputsSyncB.push_back(std::make_shared<kp::Tensor>(kp::Tensor(data)));
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}
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mgr.rebuildTensors(inputsSyncB);
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auto startSync = std::chrono::high_resolution_clock::now();
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for (uint32_t i = 0; i < numParallel; i++) {
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mgr.evalOpDefault<kp::OpAlgoBase>(
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{ inputsSyncB[i] }, std::vector<char>(shader.begin(), shader.end()));
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}
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auto endSync = std::chrono::high_resolution_clock::now();
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auto durationSync =
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std::chrono::duration_cast<std::chrono::microseconds>(endSync - startSync)
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.count();
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mgr.evalOpDefault<kp::OpTensorSyncLocal>(inputsSyncB);
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for (uint32_t i = 0; i < numParallel; i++) {
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EXPECT_EQ(inputsSyncB[i]->data(), resultSync);
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}
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kp::Manager mgrAsync(0, { 0, 2 });
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std::vector<std::shared_ptr<kp::Tensor>> inputsAsyncB;
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for (uint32_t i = 0; i < numParallel; i++) {
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inputsAsyncB.push_back(std::make_shared<kp::Tensor>(kp::Tensor(data)));
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}
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mgrAsync.rebuildTensors(inputsAsyncB);
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for (uint32_t i = 0; i < numParallel; i++) {
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mgrAsync.createManagedSequence("async" + std::to_string(i), i);
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}
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auto startAsync = std::chrono::high_resolution_clock::now();
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for (uint32_t i = 0; i < numParallel; i++) {
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mgrAsync.evalOpAsync<kp::OpAlgoBase>(
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{ inputsAsyncB[i] },
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"async" + std::to_string(i),
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std::vector<char>(shader.begin(), shader.end()));
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}
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for (uint32_t i = 0; i < numParallel; i++) {
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mgrAsync.evalOpAwait("async" + std::to_string(i));
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}
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auto endAsync = std::chrono::high_resolution_clock::now();
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auto durationAsync = std::chrono::duration_cast<std::chrono::microseconds>(
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endAsync - startAsync)
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.count();
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mgrAsync.evalOpDefault<kp::OpTensorSyncLocal>({ inputsAsyncB });
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for (uint32_t i = 0; i < numParallel; i++) {
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EXPECT_EQ(inputsAsyncB[i]->data(), resultAsync);
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}
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// The speedup should be at least 40%
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EXPECT_LT(durationAsync, durationSync * 0.6);
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}
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TEST(TestAsyncOperations, TestManagerAsyncExecution)
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{
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uint32_t size = 10;
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std::string shader(R"(
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#version 450
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layout (local_size_x = 1) in;
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layout(set = 0, binding = 0) buffer b { float pb[]; };
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shared uint sharedTotal[1];
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void main() {
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uint index = gl_GlobalInvocationID.x;
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sharedTotal[0] = 0;
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for (int i = 0; i < 100000000; i++)
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{
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atomicAdd(sharedTotal[0], 1);
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}
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pb[index] = sharedTotal[0];
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}
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)");
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std::vector<float> data(size, 0.0);
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std::vector<float> resultAsync(size, 100000000);
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kp::Manager mgr;
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std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor(data) };
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std::shared_ptr<kp::Tensor> tensorB{ new kp::Tensor(data) };
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mgr.createManagedSequence("asyncOne");
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mgr.createManagedSequence("asyncTwo");
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mgr.rebuildTensors({ tensorA, tensorB });
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mgr.evalOpAsync<kp::OpAlgoBase>(
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{ tensorA }, "asyncOne", std::vector<char>(shader.begin(), shader.end()));
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mgr.evalOpAsync<kp::OpAlgoBase>(
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{ tensorB }, "asyncTwo", std::vector<char>(shader.begin(), shader.end()));
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mgr.evalOpAwait("asyncOne");
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mgr.evalOpAwait("asyncTwo");
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mgr.evalOpAsyncDefault<kp::OpTensorSyncLocal>({ tensorA, tensorB });
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mgr.evalOpAwaitDefault();
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EXPECT_EQ(tensorA->data(), resultAsync);
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EXPECT_EQ(tensorB->data(), resultAsync);
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}
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