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#if GOOGLE_CUDA
#include "tensorflow/core/common_runtime/gpu/pool_allocator.h"
#include "tensorflow/stream_executor/multi_platform_manager.h"
#include "tensorflow/stream_executor/platform.h"
#include <gtest/gtest.h>
namespace gpu = ::perftools::gputools;
namespace tensorflow {
namespace {
TEST(PoolAllocatorTest, ZeroSizeBuffers) {
gpu::Platform* platform =
gpu::MultiPlatformManager::PlatformWithName("cuda").ValueOrDie();
PoolAllocator pool(
2 /*pool_size_limit*/, false /*auto_resize*/,
new CUDAHostAllocator(
platform->GetExecutor(gpu::StreamExecutorConfig(/*ordinal=*/0))
.ValueOrDie()),
new NoopRounder, "pool");
EXPECT_EQ(nullptr, pool.AllocateRaw(4 /*alignment*/, 0 /*num_bytes*/));
pool.DeallocateRaw(nullptr); // Should not crash.
EXPECT_EQ(0, pool.get_from_pool_count());
EXPECT_EQ(0, pool.put_count());
EXPECT_EQ(0, pool.allocated_count());
EXPECT_EQ(0, pool.evicted_count());
}
TEST(PoolAllocatorTest, ZeroSizePool) {
gpu::Platform* platform =
gpu::MultiPlatformManager::PlatformWithName("cuda").ValueOrDie();
PoolAllocator pool(
0 /*pool_size_limit*/, false /*auto_resize*/,
new CUDAHostAllocator(
platform->GetExecutor(gpu::StreamExecutorConfig(/*ordinal=*/0))
.ValueOrDie()),
new NoopRounder, "pool");
EXPECT_EQ(0, pool.get_from_pool_count());
EXPECT_EQ(0, pool.put_count());
EXPECT_EQ(0, pool.allocated_count());
EXPECT_EQ(0, pool.evicted_count());
// All allocations should bypass the pool and return valid pointers.
for (int i = 0; i < 3; ++i) {
void* p0 = pool.AllocateRaw(4, 0);
void* p4 = pool.AllocateRaw(4, 4);
void* p12 = pool.AllocateRaw(4, 12);
EXPECT_EQ(nullptr, p0);
EXPECT_NE(nullptr, p4);
EXPECT_NE(nullptr, p12);
pool.DeallocateRaw(p0);
pool.DeallocateRaw(p4);
pool.DeallocateRaw(p12);
}
EXPECT_EQ(0, pool.get_from_pool_count());
EXPECT_EQ(0, pool.put_count());
EXPECT_EQ(0, pool.allocated_count());
EXPECT_EQ(0, pool.evicted_count());
}
TEST(PoolAllocatorTest, Alignment) {
gpu::Platform* platform =
gpu::MultiPlatformManager::PlatformWithName("cuda").ValueOrDie();
PoolAllocator pool(
0 /*pool_size_limit*/, false /*auto_resize*/,
new CUDAHostAllocator(
platform->GetExecutor(gpu::StreamExecutorConfig(/*ordinal=*/0))
.ValueOrDie()),
new NoopRounder, "pool");
for (int i = 0; i < 16; ++i) {
size_t alignment = 1 << i;
void* p = pool.AllocateRaw(alignment, 111);
EXPECT_TRUE(p != nullptr);
EXPECT_EQ(0, reinterpret_cast<int64>(p) & (alignment - 1))
<< "ptr: " << p << " alignment " << alignment;
// Intentionally don't deallocate, to test that destruction of
// the PoolAllocator frees all pending memory.
}
}
TEST(PoolAllocatorTest, AutoResize) {
PoolAllocator pool(2 /*pool_size_limit*/, true /*auto_resize*/,
new BasicCPUAllocator, new NoopRounder, "pool");
// Alloc/dealloc 10 sizes just a few times, confirming pool size
// stays at 2.
for (int i = 0; i < 10; ++i) {
void* p = pool.AllocateRaw(4, 64 << i);
pool.DeallocateRaw(p);
}
EXPECT_EQ(0, pool.get_from_pool_count());
EXPECT_EQ(10, pool.allocated_count());
EXPECT_EQ(10, pool.put_count());
EXPECT_EQ(8, pool.evicted_count());
EXPECT_EQ(2, pool.size_limit());
// Then repeat 1200 times. Pool size limit should jump to 100.
for (int j = 0; j < 120; ++j) {
for (int i = 0; i < 10; ++i) {
void* p = pool.AllocateRaw(4, 64 << i);
pool.DeallocateRaw(p);
}
}
EXPECT_EQ(100, pool.size_limit());
}
TEST(PoolAllocatorTest, CudaHostAllocator) {
gpu::Platform* platform =
gpu::MultiPlatformManager::PlatformWithName("cuda").ValueOrDie();
PoolAllocator pool(
2 /*pool_size_limit*/, false /*auto_resize*/,
new CUDAHostAllocator(
platform->GetExecutor(gpu::StreamExecutorConfig(/*ordinal=*/0))
.ValueOrDie()),
new NoopRounder, "pool");
// Repeatedly Get a 16-byte value, confirming that there's only
// one real allocation.
void* p1_16 = pool.AllocateRaw(4, 16);
EXPECT_EQ(0, pool.get_from_pool_count());
EXPECT_EQ(1, pool.allocated_count());
EXPECT_NE(nullptr, p1_16);
pool.DeallocateRaw(p1_16);
// Pool contents {16}
EXPECT_EQ(1, pool.put_count());
void* p2_16 = pool.AllocateRaw(4, 16); // Get it again.
EXPECT_EQ(1, pool.get_from_pool_count());
EXPECT_EQ(1, pool.allocated_count());
EXPECT_EQ(p1_16, p2_16); // Same pointer value
pool.DeallocateRaw(p2_16); // Put it back.
// Pool contents {16}
EXPECT_EQ(2, pool.put_count());
// Get two more values of different sizes.
void* p3_4 = pool.AllocateRaw(4, 4);
EXPECT_EQ(2, pool.allocated_count());
EXPECT_NE(p1_16, p3_4); // Different pointer value
EXPECT_NE(nullptr, p3_4);
pool.DeallocateRaw(p3_4); // Put it back. Pool is now full.
// Pool contents {4, 16}
EXPECT_EQ(3, pool.put_count());
void* p4_2 = pool.AllocateRaw(4, 2); // Get a third size buffer.
EXPECT_NE(nullptr, p4_2);
EXPECT_EQ(0, pool.evicted_count());
// The pool is full: when we put back p4_2, the 16-byte buffer
// should be evicted since it was least recently inserted.
pool.DeallocateRaw(p4_2);
// Pool contents {2, 4}
EXPECT_EQ(4, pool.put_count());
EXPECT_EQ(1, pool.evicted_count());
// Re-getting and putting size 2 or 4 should not alter pool size or
// num-evicted.
void* p5_4 = pool.AllocateRaw(4, 4);
EXPECT_NE(nullptr, p5_4);
pool.DeallocateRaw(p5_4);
void* p6_2 = pool.AllocateRaw(4, 2);
EXPECT_NE(nullptr, p6_2);
pool.DeallocateRaw(p6_2);
EXPECT_EQ(3, pool.get_from_pool_count());
EXPECT_EQ(6, pool.put_count());
EXPECT_EQ(3, pool.allocated_count());
EXPECT_EQ(1, pool.evicted_count());
pool.Clear();
EXPECT_EQ(0, pool.get_from_pool_count());
EXPECT_EQ(0, pool.put_count());
EXPECT_EQ(0, pool.allocated_count());
EXPECT_EQ(0, pool.evicted_count());
}
TEST(PoolAllocatorTest, Pow2Rounder) {
Pow2Rounder rounder;
EXPECT_EQ(1, rounder.RoundUp(1));
EXPECT_EQ(2, rounder.RoundUp(2));
EXPECT_EQ(16, rounder.RoundUp(9));
EXPECT_EQ(16, rounder.RoundUp(16));
EXPECT_EQ(65536, rounder.RoundUp(41234));
EXPECT_EQ(65536, rounder.RoundUp(65535));
EXPECT_EQ(65536, rounder.RoundUp(65536));
}
TEST(PoolAllocatorTest, Name) {
gpu::Platform* platform =
gpu::MultiPlatformManager::PlatformWithName("cuda").ValueOrDie();
PoolAllocator pool(
2 /*pool_size_limit*/, false /*auto_resize*/,
new CUDAHostAllocator(
platform->GetExecutor(gpu::StreamExecutorConfig(/*ordinal=*/0))
.ValueOrDie()),
new NoopRounder, "pool");
EXPECT_EQ("pool", pool.Name());
}
} // namespace
} // namespace tensorflow
#endif // GOOGLE_CUDA
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