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//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "runtime/util/executor_data_util.h"
#include <optional>
#include <utility>
#include <vector>
#include <gmock/gmock.h>
#include <gtest/gtest.h>
#include "absl/status/status.h" // from @com_google_absl
#include "absl/types/span.h" // from @com_google_absl
#include "litert/cc/litert_element_type.h" // from @litert
#include "litert/cc/litert_layout.h" // from @litert
#include "litert/cc/litert_ranked_tensor_type.h" // from @litert
#include "litert/cc/litert_tensor_buffer.h" // from @litert
#include "litert/cc/litert_tensor_buffer_types.h" // from @litert
#include "litert/test/matchers.h" // from @litert
#include "runtime/executor/llm_executor_io_types.h"
#include "runtime/util/convert_tensor_buffer.h"
#include "runtime/util/tensor_buffer_util.h"
#include "runtime/util/test_utils.h" // NOLINT
namespace litert::lm {
namespace {
using ::litert::Dimensions;
using ::litert::ElementType;
using ::litert::Layout;
using ::litert::TensorBuffer;
using ::testing::ElementsAre;
using ::testing::status::StatusIs;
TEST(ExecutorDataUtilTest, CombineExecutorVisionDataTest) {
struct alignas(::litert::kHostMemoryBufferAlignment) {
float d[24] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f,
9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f,
17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f};
} data1;
auto tensor1 = TensorBuffer::CreateFromHostMemory(
::litert::RankedTensorType(ElementType::Float32,
Layout(Dimensions({1, 2, 4, 3}))),
data1.d, sizeof(data1.d));
ASSERT_TRUE(tensor1.HasValue());
ExecutorVisionData vision_data1(std::move(*tensor1), std::nullopt);
struct alignas(::litert::kHostMemoryBufferAlignment) {
float d[12] = {25.0f, 26.0f, 27.0f, 28.0f, 29.0f, 30.0f,
31.0f, 32.0f, 33.0f, 34.0f, 35.0f, 36.0f};
} data2;
auto tensor2 = TensorBuffer::CreateFromHostMemory(
::litert::RankedTensorType(ElementType::Float32,
Layout(Dimensions({1, 2, 2, 3}))),
data2.d, sizeof(data2.d));
ASSERT_TRUE(tensor2.HasValue());
ExecutorVisionData vision_data2(std::move(*tensor2), std::nullopt);
std::vector<ExecutorVisionData> vision_data_list;
vision_data_list.push_back(std::move(vision_data1));
vision_data_list.push_back(std::move(vision_data2));
auto combined_vision_data = CombineExecutorVisionData(vision_data_list);
ASSERT_OK(combined_vision_data);
auto mutable_embeddings_ptr = combined_vision_data->GetMutableEmbeddingsPtr();
ASSERT_OK(mutable_embeddings_ptr);
litert::TensorBuffer* embeddings_ptr = mutable_embeddings_ptr.value();
EXPECT_NE(embeddings_ptr, nullptr);
auto tensor_type = embeddings_ptr->TensorType();
ASSERT_TRUE(tensor_type.HasValue());
EXPECT_EQ(tensor_type->ElementType(), ElementType::Float32);
EXPECT_EQ(tensor_type->Layout(), Layout(Dimensions({1, 2, 6, 3})));
float read_data[36];
auto read_success = embeddings_ptr->Read<float>(absl::MakeSpan(read_data));
ASSERT_TRUE(read_success);
for (int i = 0; i < 36; ++i) {
EXPECT_EQ(read_data[i], static_cast<float>(i + 1));
}
}
TEST(ExecutorDataUtilTest, CombineExecutorAudioDataTest) {
struct alignas(::litert::kHostMemoryBufferAlignment) {
float d[12] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f,
7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f};
} data1;
auto tensor1 = TensorBuffer::CreateFromHostMemory(
::litert::RankedTensorType(ElementType::Float32,
Layout(Dimensions({1, 4, 3}))),
data1.d, sizeof(data1.d));
ASSERT_TRUE(tensor1.HasValue());
ExecutorAudioData audio_data1(std::move(*tensor1), std::nullopt, 4);
struct alignas(::litert::kHostMemoryBufferAlignment) {
float d[6] = {13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f};
} data2;
auto tensor2 = TensorBuffer::CreateFromHostMemory(
::litert::RankedTensorType(ElementType::Float32,
Layout(Dimensions({1, 2, 3}))),
data2.d, sizeof(data2.d));
ASSERT_TRUE(tensor2.HasValue());
ExecutorAudioData audio_data2(std::move(*tensor2), std::nullopt, 2);
std::vector<ExecutorAudioData> audio_data_list;
audio_data_list.push_back(std::move(audio_data1));
audio_data_list.push_back(std::move(audio_data2));
auto combined_audio_data = CombineExecutorAudioData(audio_data_list);
ASSERT_OK(combined_audio_data);
auto mutable_embeddings_ptr = combined_audio_data->GetMutableEmbeddingsPtr();
ASSERT_OK(mutable_embeddings_ptr);
litert::TensorBuffer* embeddings_ptr = mutable_embeddings_ptr.value();
EXPECT_NE(embeddings_ptr, nullptr);
auto tensor_type = embeddings_ptr->TensorType();
ASSERT_TRUE(tensor_type.HasValue());
EXPECT_EQ(tensor_type->ElementType(), ElementType::Float32);
EXPECT_EQ(tensor_type->Layout(), Layout(Dimensions({1, 6, 3})));
EXPECT_EQ(combined_audio_data->GetValidTokens(), 6);
float read_data[18];
auto read_success = embeddings_ptr->Read<float>(absl::MakeSpan(read_data));
ASSERT_TRUE(read_success);
for (int i = 0; i < 18; ++i) {
EXPECT_EQ(read_data[i], static_cast<float>(i + 1));
}
}
TEST(ExecutorDataUtilTest, CombineExecutorAudioDataEmptyFails) {
std::vector<ExecutorAudioData> executor_data;
EXPECT_THAT(
CombineExecutorAudioData(executor_data),
StatusIs(absl::StatusCode::kInvalidArgument, "Executor data is empty."));
}
TEST(ExecutorDataUtilTest, CombineExecutorAudioDataSingleSuccess) {
std::vector<ExecutorAudioData> executor_data;
ExecutorAudioData executor_audio_data;
LITERT_ASSERT_OK_AND_ASSIGN(
auto audio_buffer,
CopyToTensorBuffer<float>({4.0, 3.0, 2.0, 1.0}, {1, 2, 2}));
executor_audio_data.SetEmbeddings(std::move(audio_buffer));
executor_data.push_back(std::move(executor_audio_data));
ASSERT_OK_AND_ASSIGN(auto combined_executor_data,
CombineExecutorAudioData(executor_data));
ASSERT_OK_AND_ASSIGN(auto combined_embeddings_ptr,
combined_executor_data.GetEmbeddingsPtr());
LITERT_ASSERT_OK_AND_ASSIGN(
auto combined_embeddings_span,
ReferTensorBufferAsSpan<float>(*combined_embeddings_ptr));
EXPECT_THAT(std::vector<float>(combined_embeddings_span.begin(),
combined_embeddings_span.end()),
ElementsAre(4.0, 3.0, 2.0, 1.0));
}
TEST(ExecutorDataUtilTest, CombineExecutorAudioDataMultiSuccess) {
std::vector<ExecutorAudioData> executor_data;
ExecutorAudioData executor_audio_data_1;
LITERT_ASSERT_OK_AND_ASSIGN(
auto audio_buffer_1,
CopyToTensorBuffer<float>({6.0, 5.0, 4.0, 3.0, 2.0, 1.0}, {1, 3, 2}));
executor_audio_data_1.SetEmbeddings(std::move(audio_buffer_1));
executor_audio_data_1.SetValidTokens(3);
executor_data.push_back(std::move(executor_audio_data_1));
ExecutorAudioData executor_audio_data_2;
LITERT_ASSERT_OK_AND_ASSIGN(
auto audio_buffer_2,
CopyToTensorBuffer<float>({5.0, 6.0, 7.0, 8.0}, {1, 2, 2}));
executor_audio_data_2.SetEmbeddings(std::move(audio_buffer_2));
executor_audio_data_2.SetValidTokens(2);
executor_data.push_back(std::move(executor_audio_data_2));
ExecutorAudioData executor_audio_data_3;
LITERT_ASSERT_OK_AND_ASSIGN(
auto audio_buffer_3, CopyToTensorBuffer<float>({11.0, 12.0}, {1, 1, 2}));
executor_audio_data_3.SetEmbeddings(std::move(audio_buffer_3));
executor_audio_data_3.SetValidTokens(1);
executor_data.push_back(std::move(executor_audio_data_3));
ASSERT_OK_AND_ASSIGN(auto combined_executor_data,
CombineExecutorAudioData(executor_data));
ASSERT_OK_AND_ASSIGN(auto combined_embeddings_ptr,
combined_executor_data.GetEmbeddingsPtr());
LITERT_ASSERT_OK_AND_ASSIGN(
auto combined_embeddings_span,
ReferTensorBufferAsSpan<float>(*combined_embeddings_ptr));
const auto& dimensions = TensorBufferDims(*combined_embeddings_ptr);
EXPECT_THAT(dimensions, ElementsAre(1, 6, 2));
EXPECT_THAT(std::vector<float>(combined_embeddings_span.begin(),
combined_embeddings_span.end()),
ElementsAre(6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 5.0, 6.0, 7.0, 8.0,
11.0, 12.0));
}
TEST(ExecutorDataUtilTest, CombineExecutorVisionDataEmptyFails) {
std::vector<ExecutorVisionData> executor_data;
EXPECT_THAT(
CombineExecutorVisionData(executor_data),
StatusIs(absl::StatusCode::kInvalidArgument, "Executor data is empty."));
}
TEST(ExecutorDataUtilTest, CombineExecutorVisionDataSingleSuccess) {
std::vector<ExecutorVisionData> executor_data;
ExecutorVisionData executor_vision_data;
LITERT_ASSERT_OK_AND_ASSIGN(
auto vision_buffer,
CopyToTensorBuffer<float>({1.0, 2.0, 3.0, 4.0}, {1, 2, 2}));
executor_vision_data.SetEmbeddings(std::move(vision_buffer));
executor_data.push_back(std::move(executor_vision_data));
ASSERT_OK_AND_ASSIGN(auto combined_executor_data,
CombineExecutorVisionData(executor_data));
ASSERT_OK_AND_ASSIGN(auto combined_embeddings_ptr,
combined_executor_data.GetEmbeddingsPtr());
LITERT_ASSERT_OK_AND_ASSIGN(
auto combined_embeddings_span,
ReferTensorBufferAsSpan<float>(*combined_embeddings_ptr));
EXPECT_THAT(std::vector<float>(combined_embeddings_span.begin(),
combined_embeddings_span.end()),
ElementsAre(1.0, 2.0, 3.0, 4.0));
}
TEST(ExecutorDataUtilTest, CombineExecutorVisionDataMultiSuccess) {
std::vector<ExecutorVisionData> executor_data;
ExecutorVisionData executor_vision_data_1;
LITERT_ASSERT_OK_AND_ASSIGN(
auto vision_buffer,
CopyToTensorBuffer<float>({1.0, 2.0, 3.0, 4.0}, {1, 2, 2}));
executor_vision_data_1.SetEmbeddings(std::move(vision_buffer));
executor_data.push_back(std::move(executor_vision_data_1));
ExecutorVisionData executor_vision_data_2;
LITERT_ASSERT_OK_AND_ASSIGN(
auto vision_buffer_2,
CopyToTensorBuffer<float>({5.0, 6.0, 7.0, 8.0}, {1, 2, 2}));
executor_vision_data_2.SetEmbeddings(std::move(vision_buffer_2));
executor_data.push_back(std::move(executor_vision_data_2));
ExecutorVisionData executor_vision_data_3;
LITERT_ASSERT_OK_AND_ASSIGN(
auto vision_buffer_3,
CopyToTensorBuffer<float>({9.0, 10.0, 11.0, 12.0}, {1, 2, 2}));
executor_vision_data_3.SetEmbeddings(std::move(vision_buffer_3));
executor_data.push_back(std::move(executor_vision_data_3));
ASSERT_OK_AND_ASSIGN(auto combined_executor_data,
CombineExecutorVisionData(executor_data));
ASSERT_OK_AND_ASSIGN(auto combined_embeddings_ptr,
combined_executor_data.GetEmbeddingsPtr());
LITERT_ASSERT_OK_AND_ASSIGN(
auto combined_embeddings_span,
ReferTensorBufferAsSpan<float>(*combined_embeddings_ptr));
const auto& dimensions = TensorBufferDims(*combined_embeddings_ptr);
EXPECT_THAT(dimensions, ElementsAre(1, 1, 6, 2));
EXPECT_THAT(std::vector<float>(combined_embeddings_span.begin(),
combined_embeddings_span.end()),
ElementsAre(1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0,
11.0, 12.0));
}
} // namespace
} // namespace litert::lm
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