forked from pytorch/pytorch
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blob_test.cc
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/
blob_test.cc
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#include <iostream>
#include <memory>
#include <mutex>
#include <gtest/gtest.h>
#include "c10/util/Registry.h"
#include "caffe2/core/blob.h"
#include "caffe2/core/blob_serialization.h"
#include "caffe2/core/common.h"
#include "caffe2/core/context.h"
#include "caffe2/core/db.h"
#include "caffe2/core/operator.h"
#include "caffe2/core/qtensor.h"
#include "caffe2/core/qtensor_serialization.h"
#include "caffe2/core/tensor.h"
#include "caffe2/core/test_utils.h"
#include "caffe2/core/types.h"
#include "caffe2/core/workspace.h"
#include "caffe2/proto/caffe2_pb.h"
#include "caffe2/utils/proto_utils.h"
C10_DEFINE_int64(caffe2_test_big_tensor_size, 100000000, "");
C10_DECLARE_int(caffe2_tensor_chunk_size);
C10_DECLARE_bool(caffe2_serialize_fp16_as_bytes);
C10_DECLARE_bool(caffe2_serialize_using_bytes_as_holder);
namespace caffe2 {
using namespace ::caffe2::db;
namespace {
class BlobTestFoo {
public:
int32_t val;
};
class BlobTestBar {};
class BlobTestNonDefaultConstructible {
public:
BlobTestNonDefaultConstructible() = delete;
BlobTestNonDefaultConstructible(int x) : val(x) {}
int32_t val;
};
} // namespace
CAFFE_KNOWN_TYPE(BlobTestFoo);
CAFFE_KNOWN_TYPE(BlobTestBar);
CAFFE_KNOWN_TYPE(BlobTestNonDefaultConstructible);
class BlobTestFooSerializer : public BlobSerializerBase {
public:
BlobTestFooSerializer() {}
~BlobTestFooSerializer() override {}
/**
* Serializes a Blob. Note that this blob has to contain Tensor,
* otherwise this function produces a fatal error.
*/
void Serialize(
const void* pointer,
TypeMeta typeMeta,
const string& name,
SerializationAcceptor acceptor) override {
CAFFE_ENFORCE(typeMeta.Match<BlobTestFoo>());
BlobProto blob_proto;
blob_proto.set_name(name);
blob_proto.set_type("BlobTestFoo");
// For simplicity we will just serialize the 4-byte content as a string.
blob_proto.set_content(std::string(
reinterpret_cast<const char*>(
&static_cast<const BlobTestFoo*>(pointer)->val),
sizeof(int32_t)));
acceptor(name, SerializeBlobProtoAsString_EnforceCheck(blob_proto));
}
};
class BlobTestFooDeserializer : public BlobDeserializerBase {
public:
void Deserialize(const BlobProto& proto, Blob* blob) override {
blob->GetMutable<BlobTestFoo>()->val =
reinterpret_cast<const int32_t*>(proto.content().c_str())[0];
}
};
REGISTER_BLOB_SERIALIZER((TypeMeta::Id<BlobTestFoo>()), BlobTestFooSerializer);
REGISTER_BLOB_DESERIALIZER(BlobTestFoo, BlobTestFooDeserializer);
namespace {
TEST(BlobTest, Blob) {
Blob blob;
int* int_unused CAFFE2_UNUSED = blob.GetMutable<int>();
EXPECT_TRUE(blob.IsType<int>());
EXPECT_FALSE(blob.IsType<BlobTestFoo>());
EXPECT_FALSE(BlobIsTensorType(blob, CPU));
BlobTestFoo* foo_unused CAFFE2_UNUSED = blob.GetMutable<BlobTestFoo>();
EXPECT_TRUE(blob.IsType<BlobTestFoo>());
EXPECT_FALSE(blob.IsType<int>());
EXPECT_FALSE(BlobIsTensorType(blob, CPU));
Tensor* tensor_unused CAFFE2_UNUSED = BlobGetMutableTensor(&blob, CPU);
EXPECT_TRUE(BlobIsTensorType(blob, CPU));
EXPECT_FALSE(blob.IsType<BlobTestFoo>());
EXPECT_FALSE(blob.IsType<int>());
}
TEST(BlobTest, BlobUninitialized) {
Blob blob;
ASSERT_THROW(blob.Get<int>(), EnforceNotMet);
}
TEST(BlobTest, BlobWrongType) {
Blob blob;
BlobTestFoo* foo_unused CAFFE2_UNUSED = blob.GetMutable<BlobTestFoo>();
EXPECT_TRUE(blob.IsType<BlobTestFoo>());
EXPECT_FALSE(blob.IsType<int>());
// When not null, we should only call with the right type.
EXPECT_NE(&blob.Get<BlobTestFoo>(), nullptr);
ASSERT_THROW(blob.Get<int>(), EnforceNotMet);
}
TEST(BlobTest, BlobReset) {
Blob blob;
std::unique_ptr<BlobTestFoo> foo(new BlobTestFoo());
EXPECT_TRUE(blob.Reset(foo.release()) != nullptr);
// Also test that Reset works.
blob.Reset();
}
TEST(BlobTest, BlobMove) {
Blob blob1;
std::unique_ptr<BlobTestFoo> foo(new BlobTestFoo());
auto* fooPtr = foo.get();
EXPECT_TRUE(blob1.Reset(foo.release()) != nullptr);
Blob blob2;
blob2 = std::move(blob1);
ASSERT_THROW(blob1.Get<BlobTestFoo>(), EnforceNotMet);
EXPECT_EQ(&blob2.Get<BlobTestFoo>(), fooPtr);
Blob blob3{std::move(blob2)};
EXPECT_EQ(&blob3.Get<BlobTestFoo>(), fooPtr);
}
TEST(BlobTest, BlobNonConstructible) {
Blob blob;
ASSERT_THROW(blob.Get<BlobTestNonDefaultConstructible>(), EnforceNotMet);
// won't work because it's not default constructible
// blob.GetMutable<BlobTestNonDefaultConstructible>();
EXPECT_FALSE(
blob.GetMutableOrNull<BlobTestNonDefaultConstructible>() != nullptr);
EXPECT_TRUE(blob.Reset(new BlobTestNonDefaultConstructible(42)) != nullptr);
ASSERT_NO_THROW(blob.Get<BlobTestNonDefaultConstructible>());
ASSERT_TRUE(
blob.GetMutableOrNull<BlobTestNonDefaultConstructible>() != nullptr);
EXPECT_EQ(blob.Get<BlobTestNonDefaultConstructible>().val, 42);
blob.GetMutableOrNull<BlobTestNonDefaultConstructible>()->val = 37;
EXPECT_EQ(blob.Get<BlobTestNonDefaultConstructible>().val, 37);
}
TEST(BlobTest, BlobShareExternalPointer) {
Blob blob;
std::unique_ptr<BlobTestFoo> foo(new BlobTestFoo());
EXPECT_EQ(blob.ShareExternal<BlobTestFoo>(foo.get()), foo.get());
EXPECT_TRUE(blob.IsType<BlobTestFoo>());
// Also test that Reset works.
blob.Reset();
}
TEST(BlobTest, BlobShareExternalObject) {
Blob blob;
BlobTestFoo foo;
EXPECT_EQ(blob.ShareExternal<BlobTestFoo>(&foo), &foo);
EXPECT_TRUE(blob.IsType<BlobTestFoo>());
// Also test that Reset works.
blob.Reset();
}
TEST(BlobTest, StringSerialization) {
const std::string kTestString = "Hello world?";
Blob blob;
*blob.GetMutable<std::string>() = kTestString;
string serialized = SerializeBlob(blob, "test");
BlobProto proto;
CHECK(proto.ParseFromString(serialized));
EXPECT_EQ(proto.name(), "test");
EXPECT_EQ(proto.type(), "std::string");
EXPECT_FALSE(proto.has_tensor());
EXPECT_EQ(proto.content(), kTestString);
}
TEST(TensorNonTypedTest, TensorChangeType) {
vector<int> dims(3);
dims[0] = 2;
dims[1] = 3;
dims[2] = 5;
Tensor tensor(dims, CPU);
auto* ptr = tensor.mutable_data<int>();
EXPECT_TRUE(ptr != nullptr);
EXPECT_TRUE(tensor.data<int>() != nullptr);
EXPECT_TRUE(tensor.dtype().Match<int>());
// int and float are same size, so should retain the pointer
// NB: this is only true when the use_count of the underlying Storage is 1, if
// the underlying Storage is shared between multiple Tensors We'll create a
// new Storage when the data type changes
EXPECT_TRUE(tensor.mutable_data<float>() == (float*)ptr);
EXPECT_TRUE(tensor.data<float>() == (const float*)ptr);
EXPECT_TRUE(tensor.dtype().Match<float>());
// at::Half is smaller, so still should share buffer
EXPECT_TRUE(tensor.mutable_data<at::Half>() == (at::Half*)ptr);
EXPECT_TRUE(tensor.data<at::Half>() == (const at::Half*)ptr);
EXPECT_TRUE(tensor.dtype().Match<at::Half>());
// share the data with other tensor so that the pointer won't be reused
// when we reallocate
Tensor other_tensor = tensor.Alias();
// but double is bigger, so it should allocate a new one
auto* doubleptr = tensor.mutable_data<double>();
EXPECT_TRUE(doubleptr != (double*)ptr);
EXPECT_TRUE(doubleptr != nullptr);
EXPECT_TRUE(tensor.data<double>() != nullptr);
EXPECT_TRUE(tensor.dtype().Match<double>());
}
TEST(TensorNonTypedTest, NonDefaultConstructible) {
vector<int> dims(3);
dims[0] = 2;
dims[1] = 3;
dims[2] = 5;
Tensor tensor(dims, CPU);
// this doesn't compile - good!
// auto* ptr = tensor.mutable_data<BlobTestNonDefaultConstructible>();
EXPECT_THROW(
tensor.raw_mutable_data(
TypeMeta::Make<BlobTestNonDefaultConstructible>()),
EnforceNotMet);
}
template <typename T>
class TensorCPUTest : public ::testing::Test {};
template <typename T>
class TensorCPUDeathTest : public ::testing::Test {};
typedef ::testing::Types<char, int, float> TensorTypes;
TYPED_TEST_CASE(TensorCPUTest, TensorTypes);
TYPED_TEST_CASE(TensorCPUDeathTest, TensorTypes);
TYPED_TEST(TensorCPUTest, TensorInitializedEmpty) {
Tensor tensor(CPU);
EXPECT_EQ(tensor.dim(), 1);
EXPECT_EQ(tensor.numel(), 0);
vector<int> dims(3);
dims[0] = 2;
dims[1] = 3;
dims[2] = 5;
tensor.Resize(dims);
EXPECT_EQ(tensor.dim(), 3);
EXPECT_EQ(tensor.dim32(0), 2);
EXPECT_EQ(tensor.dim32(1), 3);
EXPECT_EQ(tensor.dim32(2), 5);
EXPECT_EQ(tensor.numel(), 2 * 3 * 5);
EXPECT_TRUE(tensor.mutable_data<TypeParam>() != nullptr);
EXPECT_TRUE(tensor.data<TypeParam>() != nullptr);
}
TYPED_TEST(TensorCPUTest, TensorInitializedNonEmpty) {
vector<int> dims(3);
dims[0] = 2;
dims[1] = 3;
dims[2] = 5;
Tensor tensor(dims, CPU);
EXPECT_EQ(tensor.dim(), 3);
EXPECT_EQ(tensor.dim32(0), 2);
EXPECT_EQ(tensor.dim32(1), 3);
EXPECT_EQ(tensor.dim32(2), 5);
EXPECT_TRUE(tensor.mutable_data<TypeParam>() != nullptr);
EXPECT_TRUE(tensor.data<TypeParam>() != nullptr);
dims[0] = 7;
dims[1] = 11;
dims[2] = 13;
dims.push_back(17);
tensor.Resize(dims);
EXPECT_EQ(tensor.dim(), 4);
EXPECT_EQ(tensor.dim32(0), 7);
EXPECT_EQ(tensor.dim32(1), 11);
EXPECT_EQ(tensor.dim32(2), 13);
EXPECT_EQ(tensor.dim32(3), 17);
EXPECT_TRUE(tensor.mutable_data<TypeParam>() != nullptr);
EXPECT_TRUE(tensor.data<TypeParam>() != nullptr);
}
TYPED_TEST(TensorCPUTest, TensorInitializedZeroDim) {
vector<int> dims(3);
dims[0] = 2;
dims[1] = 0;
dims[2] = 5;
Tensor tensor(dims, CPU);
EXPECT_EQ(tensor.dim(), 3);
EXPECT_EQ(tensor.dim32(0), 2);
EXPECT_EQ(tensor.dim32(1), 0);
EXPECT_EQ(tensor.dim32(2), 5);
EXPECT_TRUE(tensor.mutable_data<TypeParam>() == nullptr);
EXPECT_TRUE(tensor.data<TypeParam>() == nullptr);
}
TYPED_TEST(TensorCPUTest, TensorResizeZeroDim) {
vector<int> dims(3);
dims[0] = 2;
dims[1] = 3;
dims[2] = 5;
Tensor tensor(dims, CPU);
EXPECT_EQ(tensor.dim(), 3);
EXPECT_EQ(tensor.dim32(0), 2);
EXPECT_EQ(tensor.dim32(1), 3);
EXPECT_EQ(tensor.dim32(2), 5);
EXPECT_TRUE(tensor.mutable_data<TypeParam>() != nullptr);
EXPECT_TRUE(tensor.data<TypeParam>() != nullptr);
dims[0] = 7;
dims[1] = 0;
dims[2] = 13;
tensor.Resize(dims);
EXPECT_EQ(tensor.numel(), 0);
EXPECT_EQ(tensor.dim(), 3);
EXPECT_EQ(tensor.dim32(0), 7);
EXPECT_EQ(tensor.dim32(1), 0);
EXPECT_EQ(tensor.dim32(2), 13);
// output value can be arbitrary, but the call to data() shouldn't crash
tensor.mutable_data<TypeParam>();
tensor.data<TypeParam>();
}
TYPED_TEST(TensorCPUTest, TensorInitializedScalar) {
vector<int> dims;
Tensor tensor(dims, CPU);
EXPECT_EQ(tensor.dim(), 0);
EXPECT_EQ(tensor.numel(), 1);
EXPECT_TRUE(tensor.mutable_data<TypeParam>() != nullptr);
EXPECT_TRUE(tensor.data<TypeParam>() != nullptr);
}
TYPED_TEST(TensorCPUTest, TensorAlias) {
vector<int> dims(3);
dims[0] = 2;
dims[1] = 3;
dims[2] = 5;
Tensor tensor(dims, CPU);
EXPECT_TRUE(tensor.mutable_data<TypeParam>() != nullptr);
Tensor other_tensor = tensor.Alias();
EXPECT_TRUE(tensor.data<TypeParam>() != nullptr);
EXPECT_TRUE(other_tensor.data<TypeParam>() != nullptr);
EXPECT_EQ(tensor.data<TypeParam>(), other_tensor.data<TypeParam>());
// Set one value, check the other
for (int i = 0; i < tensor.numel(); ++i) {
tensor.mutable_data<TypeParam>()[i] = i;
EXPECT_EQ(other_tensor.data<TypeParam>()[i], i);
}
}
TYPED_TEST(TensorCPUTest, TensorShareDataRawPointer) {
vector<int> dims(3);
dims[0] = 2;
dims[1] = 3;
dims[2] = 5;
std::unique_ptr<TypeParam[]> raw_buffer(new TypeParam[2 * 3 * 5]);
Tensor tensor(dims, CPU);
tensor.ShareExternalPointer(raw_buffer.get());
EXPECT_EQ(tensor.mutable_data<TypeParam>(), raw_buffer.get());
EXPECT_EQ(tensor.data<TypeParam>(), raw_buffer.get());
// Set one value, check the other
for (int i = 0; i < tensor.numel(); ++i) {
raw_buffer.get()[i] = i;
EXPECT_EQ(tensor.data<TypeParam>()[i], i);
}
}
TYPED_TEST(TensorCPUTest, TensorShareDataRawPointerWithMeta) {
vector<int> dims(3);
dims[0] = 2;
dims[1] = 3;
dims[2] = 5;
std::unique_ptr<TypeParam[]> raw_buffer(new TypeParam[2 * 3 * 5]);
Tensor tensor(dims, CPU);
TypeMeta meta = TypeMeta::Make<TypeParam>();
tensor.ShareExternalPointer(raw_buffer.get(), meta);
EXPECT_EQ(tensor.mutable_data<TypeParam>(), raw_buffer.get());
EXPECT_EQ(tensor.data<TypeParam>(), raw_buffer.get());
// Set one value, check the other
for (int i = 0; i < tensor.numel(); ++i) {
raw_buffer.get()[i] = i;
EXPECT_EQ(tensor.data<TypeParam>()[i], i);
}
}
TYPED_TEST(TensorCPUTest, TensorAliasCanUseDifferentShapes) {
vector<int> dims(3);
dims[0] = 2;
dims[1] = 3;
dims[2] = 5;
vector<int> alternate_dims(1);
alternate_dims[0] = 2 * 3 * 5;
Tensor tensor(dims, CPU);
EXPECT_TRUE(tensor.mutable_data<TypeParam>() != nullptr);
Tensor other_tensor = tensor.Alias();
other_tensor.Resize(alternate_dims);
EXPECT_EQ(other_tensor.dim(), 1);
EXPECT_EQ(other_tensor.dim32(0), alternate_dims[0]);
EXPECT_TRUE(tensor.data<TypeParam>() != nullptr);
EXPECT_TRUE(other_tensor.data<TypeParam>() != nullptr);
EXPECT_EQ(tensor.data<TypeParam>(), other_tensor.data<TypeParam>());
// Set one value, check the other
for (int i = 0; i < tensor.numel(); ++i) {
tensor.mutable_data<TypeParam>()[i] = i;
EXPECT_EQ(other_tensor.data<TypeParam>()[i], i);
}
}
TYPED_TEST(TensorCPUTest, NoLongerAliassAfterNumelChanges) {
vector<int> dims(3);
dims[0] = 2;
dims[1] = 3;
dims[2] = 5;
Tensor tensor(dims, CPU);
EXPECT_TRUE(tensor.mutable_data<TypeParam>() != nullptr);
Tensor other_tensor = tensor.Alias();
EXPECT_EQ(tensor.data<TypeParam>(), other_tensor.data<TypeParam>());
auto* old_pointer = other_tensor.data<TypeParam>();
dims[0] = 7;
tensor.Resize(dims);
EXPECT_EQ(old_pointer, other_tensor.data<TypeParam>());
EXPECT_NE(old_pointer, tensor.mutable_data<TypeParam>());
}
TYPED_TEST(TensorCPUTest, NoLongerAliasAfterFreeMemory) {
vector<int> dims(3);
dims[0] = 2;
dims[1] = 3;
dims[2] = 5;
Tensor tensor(dims, CPU);
EXPECT_TRUE(tensor.mutable_data<TypeParam>() != nullptr);
Tensor other_tensor = tensor.Alias();
EXPECT_EQ(tensor.data<TypeParam>(), other_tensor.data<TypeParam>());
auto* old_pointer = other_tensor.data<TypeParam>();
tensor.FreeMemory();
EXPECT_EQ(old_pointer, other_tensor.data<TypeParam>());
EXPECT_NE(old_pointer, tensor.mutable_data<TypeParam>());
}
TYPED_TEST(TensorCPUTest, KeepOnShrink) {
// Set flags (defaults)
FLAGS_caffe2_keep_on_shrink = true;
FLAGS_caffe2_max_keep_on_shrink_memory = LLONG_MAX;
vector<int> dims{2, 3, 5};
Tensor tensor(dims, CPU);
TypeParam* ptr = tensor.mutable_data<TypeParam>();
EXPECT_TRUE(ptr != nullptr);
// Expanding - will reallocate
tensor.Resize(3, 4, 6);
TypeParam* larger_ptr = tensor.mutable_data<TypeParam>();
EXPECT_TRUE(larger_ptr != nullptr);
// This check can fail when malloc() returns the same recently freed address
// EXPECT_NE(ptr, larger_ptr);
// Shrinking - will not reallocate
tensor.Resize(1, 2, 4);
TypeParam* smaller_ptr = tensor.mutable_data<TypeParam>();
EXPECT_TRUE(smaller_ptr != nullptr);
EXPECT_EQ(larger_ptr, smaller_ptr);
// resize to 0 in the meantime;
tensor.Resize(3, 0, 6);
// Expanding but still under capacity - will not reallocate
tensor.Resize(2, 3, 5);
TypeParam* new_ptr = tensor.mutable_data<TypeParam>();
EXPECT_TRUE(new_ptr != nullptr);
EXPECT_EQ(larger_ptr, new_ptr);
}
TYPED_TEST(TensorCPUTest, MaxKeepOnShrink) {
// Set flags
FLAGS_caffe2_keep_on_shrink = true;
FLAGS_caffe2_max_keep_on_shrink_memory = 8 * 4 * sizeof(TypeParam);
vector<int> dims{1, 8, 8};
Tensor tensor(dims, CPU);
TypeParam* ptr = tensor.mutable_data<TypeParam>();
EXPECT_TRUE(ptr != nullptr);
// Shrinking - will not reallocate
tensor.Resize(1, 7, 8);
TypeParam* smaller_ptr = tensor.mutable_data<TypeParam>();
EXPECT_TRUE(smaller_ptr != nullptr);
EXPECT_EQ(ptr, smaller_ptr);
// Resize to more than maximum shrink, should reallocate
tensor.Resize(1, 1, 8);
TypeParam* new_ptr = tensor.mutable_data<TypeParam>();
EXPECT_TRUE(new_ptr != nullptr);
// This check can fail when malloc() returns the same recently freed address
// EXPECT_NE(ptr, new_ptr);
// Restore default flags
FLAGS_caffe2_max_keep_on_shrink_memory = LLONG_MAX;
}
TYPED_TEST(TensorCPUDeathTest, CannotAccessRawDataWhenEmpty) {
Tensor tensor(CPU);
EXPECT_EQ(tensor.dim(), 1);
EXPECT_EQ(tensor.numel(), 0);
ASSERT_ANY_THROW(tensor.raw_data());
}
TYPED_TEST(TensorCPUDeathTest, CannotAccessDataWhenEmpty) {
Tensor tensor(CPU);
EXPECT_EQ(tensor.dim(), 1);
EXPECT_EQ(tensor.numel(), 0);
ASSERT_ANY_THROW(tensor.data<TypeParam>());
}
TEST(TensorTest, TensorNonFundamentalType) {
Tensor tensor(vector<int>{2, 3, 4}, CPU);
EXPECT_TRUE(tensor.mutable_data<std::string>() != nullptr);
const std::string* ptr = tensor.data<std::string>();
for (int i = 0; i < tensor.numel(); ++i) {
EXPECT_TRUE(ptr[i] == "");
}
}
TEST(TensorTest, TensorNonFundamentalTypeClone) {
Tensor tensor(vector<int>{2, 3, 4}, CPU);
std::string* ptr = tensor.mutable_data<std::string>();
EXPECT_TRUE(ptr != nullptr);
for (int i = 0; i < tensor.numel(); ++i) {
EXPECT_TRUE(ptr[i] == "");
ptr[i] = "filled";
}
Tensor dst_tensor = tensor.Clone();
const std::string* dst_ptr = dst_tensor.data<std::string>();
for (int i = 0; i < dst_tensor.numel(); ++i) {
EXPECT_TRUE(dst_ptr[i] == "filled");
}
// Change the original tensor
for (int i = 0; i < tensor.numel(); ++i) {
EXPECT_TRUE(ptr[i] == "filled");
ptr[i] = "changed";
}
// Confirm that the cloned tensor is not affect
for (int i = 0; i < dst_tensor.numel(); ++i) {
EXPECT_TRUE(dst_ptr[i] == "filled");
}
}
TEST(TensorTest, Tensor64BitDimension) {
// Initialize a large tensor.
int64_t large_number =
static_cast<int64_t>(std::numeric_limits<int>::max()) + 1;
Tensor tensor(vector<int64_t>{large_number}, CPU);
EXPECT_EQ(tensor.dim(), 1);
EXPECT_EQ(tensor.size(0), large_number);
EXPECT_EQ(tensor.numel(), large_number);
try {
EXPECT_TRUE(tensor.mutable_data<char>() != nullptr);
} catch (const EnforceNotMet& e) {
string msg = e.what();
size_t found = msg.find("posix_memalign");
if (found != string::npos) {
msg = msg.substr(0, msg.find('\n'));
LOG(WARNING) << msg;
LOG(WARNING) << "Out of memory issue with posix_memalign;\n";
return;
} else {
throw e;
}
}
EXPECT_EQ(tensor.nbytes(), large_number * sizeof(char));
EXPECT_EQ(tensor.itemsize(), sizeof(char));
// Try to go even larger, but this time we will not do mutable_data because we
// do not have a large enough memory.
tensor.Resize(large_number, 100);
EXPECT_EQ(tensor.dim(), 2);
EXPECT_EQ(tensor.size(0), large_number);
EXPECT_EQ(tensor.size(1), 100);
EXPECT_EQ(tensor.numel(), large_number * 100);
}
TEST(TensorTest, UndefinedTensor) {
Tensor x;
EXPECT_FALSE(x.defined());
}
TEST(TensorTest, CopyAndAssignment) {
Tensor x(CPU);
x.Resize(16, 17);
testing::randomFill(x.template mutable_data<float>(), 16 * 17);
EXPECT_TRUE(x.defined());
Tensor y(x);
Tensor z = x;
testing::assertTensorEquals(x, y);
testing::assertTensorEquals(x, z);
}
TEST(TensorDeathTest, CannotCastDownLargeDims) {
int64_t large_number =
static_cast<int64_t>(std::numeric_limits<int>::max()) + 1;
Tensor tensor(vector<int64_t>{large_number}, CPU);
EXPECT_EQ(tensor.dim(), 1);
EXPECT_EQ(tensor.size(0), large_number);
ASSERT_THROW(tensor.dim32(0), EnforceNotMet);
}
#define TEST_SERIALIZATION_WITH_TYPE(TypeParam, field_name) \
TEST(TensorTest, TensorSerialization_##TypeParam) { \
Blob blob; \
Tensor* tensor = BlobGetMutableTensor(&blob, CPU); \
tensor->Resize(2, 3); \
for (int i = 0; i < 6; ++i) { \
tensor->mutable_data<TypeParam>()[i] = static_cast<TypeParam>(i); \
} \
string serialized = SerializeBlob(blob, "test"); \
BlobProto proto; \
CHECK(proto.ParseFromString(serialized)); \
EXPECT_EQ(proto.name(), "test"); \
EXPECT_EQ(proto.type(), "Tensor"); \
EXPECT_TRUE(proto.has_tensor()); \
const TensorProto& tensor_proto = proto.tensor(); \
EXPECT_EQ( \
tensor_proto.data_type(), \
TypeMetaToDataType(TypeMeta::Make<TypeParam>())); \
EXPECT_EQ(tensor_proto.field_name##_size(), 6); \
for (int i = 0; i < 6; ++i) { \
EXPECT_EQ(tensor_proto.field_name(i), static_cast<TypeParam>(i)); \
} \
Blob new_blob; \
EXPECT_NO_THROW(DeserializeBlob(serialized, &new_blob)); \
EXPECT_TRUE(BlobIsTensorType(new_blob, CPU)); \
const TensorCPU& new_tensor = blob.Get<TensorCPU>(); \
EXPECT_EQ(new_tensor.dim(), 2); \
EXPECT_EQ(new_tensor.size(0), 2); \
EXPECT_EQ(new_tensor.size(1), 3); \
for (int i = 0; i < 6; ++i) { \
EXPECT_EQ( \
tensor->data<TypeParam>()[i], new_tensor.data<TypeParam>()[i]); \
} \
} \
\
TEST(EmptyTensorTest, TensorSerialization_##TypeParam) { \
Blob blob; \
TensorCPU* tensor = BlobGetMutableTensor(&blob, CPU); \
tensor->Resize(0, 3); \
tensor->mutable_data<TypeParam>(); \
string serialized = SerializeBlob(blob, "test"); \
BlobProto proto; \
CHECK(proto.ParseFromString(serialized)); \
EXPECT_EQ(proto.name(), "test"); \
EXPECT_EQ(proto.type(), "Tensor"); \
EXPECT_TRUE(proto.has_tensor()); \
const TensorProto& tensor_proto = proto.tensor(); \
EXPECT_EQ( \
tensor_proto.data_type(), \
TypeMetaToDataType(TypeMeta::Make<TypeParam>())); \
EXPECT_EQ(tensor_proto.field_name##_size(), 0); \
Blob new_blob; \
EXPECT_NO_THROW(DeserializeBlob(serialized, &new_blob)); \
EXPECT_TRUE(BlobIsTensorType(new_blob, CPU)); \
const TensorCPU& new_tensor = blob.Get<TensorCPU>(); \
EXPECT_EQ(new_tensor.dim(), 2); \
EXPECT_EQ(new_tensor.size(0), 0); \
EXPECT_EQ(new_tensor.size(1), 3); \
}
TEST_SERIALIZATION_WITH_TYPE(bool, int32_data)
TEST_SERIALIZATION_WITH_TYPE(double, double_data)
TEST_SERIALIZATION_WITH_TYPE(float, float_data)
TEST_SERIALIZATION_WITH_TYPE(int, int32_data)
TEST_SERIALIZATION_WITH_TYPE(int8_t, int32_data)
TEST_SERIALIZATION_WITH_TYPE(int16_t, int32_data)
TEST_SERIALIZATION_WITH_TYPE(uint8_t, int32_data)
TEST_SERIALIZATION_WITH_TYPE(uint16_t, int32_data)
TEST_SERIALIZATION_WITH_TYPE(int64_t, int64_data)
TEST(TensorTest, TensorSerialization_CustomType) {
Blob blob;
TensorCPU* tensor = BlobGetMutableTensor(&blob, CPU);
tensor->Resize(2, 3);
for (int i = 0; i < 6; ++i) {
tensor->mutable_data<BlobTestFoo>()[i].val = i;
}
string serialized = SerializeBlob(blob, "test");
BlobProto proto;
CHECK(proto.ParseFromString(serialized));
EXPECT_EQ(proto.name(), "test");
EXPECT_EQ(proto.type(), "Tensor");
Blob new_blob;
EXPECT_NO_THROW(DeserializeBlob(serialized, &new_blob));
EXPECT_TRUE(BlobIsTensorType(new_blob, CPU));
const TensorCPU& new_tensor = blob.Get<TensorCPU>();
EXPECT_EQ(new_tensor.dim(), 2);
EXPECT_EQ(new_tensor.size(0), 2);
EXPECT_EQ(new_tensor.size(1), 3);
for (int i = 0; i < 6; ++i) {
EXPECT_EQ(
new_tensor.data<BlobTestFoo>()[i].val,
tensor->data<BlobTestFoo>()[i].val);
}
}
TEST(TensorTest, Half) {
const int64_t kSize = 3000000;
Blob blob;
TensorCPU* tensor = BlobGetMutableTensor(&blob, CPU);
tensor->Resize(kSize);
for (int i = 0; i < tensor->numel(); ++i) {
tensor->mutable_data<at::Half>()[i].x = i % 10000;
}
string serialized = SerializeBlob(blob, "test");
BlobProto proto;
CHECK(proto.ParseFromString(serialized));
EXPECT_EQ(proto.name(), "test");
EXPECT_EQ(proto.type(), "Tensor");
EXPECT_TRUE(proto.has_tensor());
const TensorProto& tensor_proto = proto.tensor();
EXPECT_EQ(
tensor_proto.data_type(), TypeMetaToDataType(TypeMeta::Make<at::Half>()));
if (FLAGS_caffe2_serialize_fp16_as_bytes) {
EXPECT_EQ(tensor_proto.byte_data().size(), 2 * kSize);
for (int i = 0; i < kSize; ++i) {
auto value = tensor->mutable_data<at::Half>()[i].x;
auto low_bits = static_cast<char>(value & 0xff);
auto high_bits = static_cast<char>(value >> 8);
EXPECT_EQ(tensor_proto.byte_data()[2 * i], low_bits);
EXPECT_EQ(tensor_proto.byte_data()[2 * i + 1], high_bits);
}
} else {
EXPECT_EQ(tensor_proto.int32_data().size(), kSize);
}
Blob new_blob;
EXPECT_NO_THROW(DeserializeBlob(serialized, &new_blob));
EXPECT_TRUE(BlobIsTensorType(new_blob, CPU));
const TensorCPU& new_tensor = blob.Get<TensorCPU>();
EXPECT_EQ(new_tensor.dim(), 1);
EXPECT_EQ(new_tensor.size(0), kSize);
for (int i = 0; i < kSize; ++i) {
EXPECT_EQ(new_tensor.data<at::Half>()[i].x, i % 10000);
}
}
TEST(TensorTest, TensorFactory) {
Tensor a = empty({1, 2, 3}, at::device(CPU).dtype<float>());
EXPECT_NE(a.data<float>(), nullptr);
a.mutable_data<float>()[0] = 3.0;
Tensor b = empty({1, 2, 3}, at::device(CPU).dtype<int>());
EXPECT_NE(b.data<int>(), nullptr);
b.mutable_data<int>()[0] = 3;
}
TEST(QTensorTest, QTensorSerialization) {
Blob blob;
QTensor<CPUContext>* qtensor = blob.GetMutable<QTensor<CPUContext>>();
qtensor->SetPrecision(5);
qtensor->SetSigned(false);
qtensor->SetScale(1.337);
qtensor->SetBias(-1.337);
qtensor->Resize(std::vector<int>{2, 3});
// "Randomly" set bits.
srand(0);
for (int i = 0; i < 6; ++i) {
for (int j = 0; j < 5; ++j) {
qtensor->SetBitAtIndex(j, i, rand() % 2);
}
}
string serialized = SerializeBlob(blob, "test");
BlobProto proto;
CHECK(proto.ParseFromString(serialized));
EXPECT_EQ(proto.name(), "test");
EXPECT_EQ(proto.type(), "QTensor");
EXPECT_TRUE(proto.has_qtensor());
const QTensorProto& qtensor_proto = proto.qtensor();
EXPECT_EQ(qtensor_proto.precision(), qtensor->precision());
EXPECT_EQ(qtensor_proto.scale(), qtensor->scale());
EXPECT_EQ(qtensor_proto.bias(), qtensor->bias());
EXPECT_EQ(qtensor_proto.is_signed(), qtensor->is_signed());
Blob new_blob;
DeserializeBlob(serialized, &new_blob);
EXPECT_TRUE(new_blob.IsType<QTensor<CPUContext>>());
const QTensor<CPUContext>& new_qtensor = blob.Get<QTensor<CPUContext>>();
EXPECT_EQ(new_qtensor.ndim(), 2);
EXPECT_EQ(new_qtensor.dim32(0), 2);
EXPECT_EQ(new_qtensor.dim32(1), 3);
for (int i = 0; i < 6; ++i) {
for (int j = 0; j < 5; ++j) {
EXPECT_EQ(qtensor->GetBitAtIndex(j, i), new_qtensor.GetBitAtIndex(j, i));
}
}
}
using StringMap = std::vector<std::pair<string, string>>;
class VectorCursor : public db::Cursor {
public:
explicit VectorCursor(StringMap* data) : data_(data) {
pos_ = 0;
}
~VectorCursor() override {}
void Seek(const string& /* unused */) override {}
void SeekToFirst() override {}
void Next() override {
++pos_;
}
string key() override {
return (*data_)[pos_].first;
}
string value() override {
return (*data_)[pos_].second;
}
bool Valid() override {
return pos_ < data_->size();
}
private:
StringMap* data_ = nullptr;
size_t pos_ = 0;
};
class VectorDB : public db::DB {
public:
VectorDB(const string& source, db::Mode mode)
: DB(source, mode), name_(source) {}
~VectorDB() override {
data_.erase(name_);
}
void Close() override {}
std::unique_ptr<db::Cursor> NewCursor() override {
return make_unique<VectorCursor>(getData());
}
std::unique_ptr<db::Transaction> NewTransaction() override {
CAFFE_THROW("Not implemented");
}
static void registerData(const string& name, StringMap&& data) {
std::lock_guard<std::mutex> guard(dataRegistryMutex_);
data_[name] = std::move(data);
}
private:
StringMap* getData() {
auto it = data_.find(name_);
CAFFE_ENFORCE(it != data_.end(), "Can't find ", name_);
return &(it->second);
}
private:
string name_;
static std::mutex dataRegistryMutex_;
static std::map<string, StringMap> data_;
};
std::mutex VectorDB::dataRegistryMutex_;
std::map<string, StringMap> VectorDB::data_;
REGISTER_CAFFE2_DB(vector_db, VectorDB);
template <typename TypeParam>
class TypedTensorTest : public ::testing::Test {};
typedef ::testing::
Types<float, bool, double, int, int8_t, int16_t, uint8_t, uint16_t, int64_t>
TensorDataTypes;
TYPED_TEST_CASE(TypedTensorTest, TensorDataTypes);
TYPED_TEST(TypedTensorTest, BigTensorSerialization) {
int64_t d1 = 2;
int64_t d2 = FLAGS_caffe2_test_big_tensor_size
? FLAGS_caffe2_test_big_tensor_size / d1
: static_cast<int64_t>(std::numeric_limits<int>::max()) + 1;
int64_t size = d1 * d2;
string db_source = (string)std::tmpnam(nullptr);
VLOG(1) << "db_source: " << db_source;
{
VLOG(1) << "Test begin";
Blob blob;
Tensor* tensor = BlobGetMutableTensor(&blob, CPU);
VLOG(1) << "Allocating blob";
tensor->Resize(d1, d2);
auto mutableData = tensor->mutable_data<TypeParam>();
VLOG(1) << "Filling out the blob";
for (int64_t i = 0; i < size; ++i) {
mutableData[i] = static_cast<TypeParam>(i);
}
StringMap data;
std::mutex mutex;
/*auto db = CreateDB("minidb", db_source, WRITE);*/
auto acceptor = [&](const std::string& key, const std::string& value) {
std::lock_guard<std::mutex> guard(mutex);
/*db->NewTransaction()->Put(key, value);*/
data.emplace_back(key, value);
};
SerializeBlob(blob, "test", acceptor);
VectorDB::registerData(db_source, std::move(data));
VLOG(1) << "finished writing to DB";
}
{
DeviceOption option;
option.set_device_type(PROTO_CPU);
Argument db_type_arg = MakeArgument<string>("db_type", "vector_db");
Argument absolute_path_arg = MakeArgument<bool>("absolute_path", true);
Argument db_source_arg = MakeArgument<string>("db", db_source);
auto op_def = CreateOperatorDef(
"Load",
"",
std::vector<string>{},
std::vector<string>({"test"}),
std::vector<Argument>{db_type_arg, db_source_arg, absolute_path_arg},
option,
"DUMMY_ENGINE");
Workspace ws;
auto load_op = CreateOperator(op_def, &ws);
EXPECT_TRUE(load_op != nullptr);
VLOG(1) << "Running operator";
load_op->Run();
VLOG(1) << "Reading blob from workspace";
auto new_blob = ws.GetBlob("test");
EXPECT_TRUE(BlobIsTensorType(*new_blob, CPU));
const auto& new_tensor = new_blob->Get<TensorCPU>();
EXPECT_EQ(new_tensor.dim(), d1);
EXPECT_EQ(new_tensor.size(0), d1);
EXPECT_EQ(new_tensor.size(1), d2);
for (int64_t i = 0; i < size; ++i) {
EXPECT_EQ(static_cast<TypeParam>(i), new_tensor.data<TypeParam>()[i]);
}
}
}
struct DummyType {
/* This struct is used to test serialization and deserialization of huge
* blobs, that are not tensors.
*/
/* implicit */ DummyType(int n_chunks_init = 0) : n_chunks(n_chunks_init) {}
std::string serialize(const std::string& name, const int32_t chunk_id) const {
BlobProto blobProto;
blobProto.set_name(name);
blobProto.set_type("DummyType");
std::string content("");
blobProto.set_content(content);
blobProto.set_content_num_chunks(n_chunks);
blobProto.set_content_chunk_id(chunk_id);
return blobProto.SerializeAsString();
}
void deserialize(const BlobProto& /* unused */) {
++n_chunks;
}
int n_chunks;
};
class DummyTypeSerializer : public BlobSerializerBase {
public:
DummyTypeSerializer() {}
~DummyTypeSerializer() override {}
void Serialize(
const void* pointer,
TypeMeta typeMeta,
const string& name,
SerializationAcceptor acceptor) override {
CAFFE_ENFORCE(typeMeta.Match<DummyType>());
const auto& container = *static_cast<const DummyType*>(pointer);
for (int k = 0; k < container.n_chunks; ++k) {
std::string serialized_chunk = container.serialize(name, k);
acceptor(c10::str(name, kChunkIdSeparator, k), serialized_chunk);
}
}
};
class DummyTypeDeserializer : public BlobDeserializerBase {
public:
void Deserialize(const BlobProto& proto, Blob* blob) override {
auto* container = blob->GetMutable<DummyType>();
container->deserialize(proto);
}
};
} // namespace
CAFFE_KNOWN_TYPE(DummyType);
namespace {
REGISTER_BLOB_SERIALIZER((TypeMeta::Id<DummyType>()), DummyTypeSerializer);
C10_REGISTER_TYPED_CLASS(
BlobDeserializerRegistry,
"DummyType",
DummyTypeDeserializer);
TEST(ContentChunks, Serialization) {