[DL] Add AlexNet benchmark.

.gitattributes

@@ -1,3 +1,4 @@
 benchmarks/DeepLearning/Models/MiniLM-L6/MiniLM-L6-12.mlir filter=lfs diff=lfs merge=lfs -text
 benchmarks/DeepLearning/Models/MiniLM-L6/MiniLM-L6-200.mlir filter=lfs diff=lfs merge=lfs -text
 benchmarks/DeepLearning/Models/VGG-13/VGG-13.mlir filter=lfs diff=lfs merge=lfs -text
+benchmarks/DeepLearning/Models/AlexNet/alexnet_tosa.mlir filter=lfs diff=lfs merge=lfs -text

benchmarks/DeepLearning/Models/AlexNet/AlexNetDefaultBenchmark.cpp

@@ -0,0 +1,124 @@
+//===- AlexNetDefaultBenchmark.cpp ---------------------------------------===//
+//
+// 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.
+//
+//===----------------------------------------------------------------------===//
+//
+// This file implements the benchmark for e2e alexnet.
+// The alexnet_tosa.mlir is generated from torch-mlir project.
+//
+//===----------------------------------------------------------------------===//
+
+#include <benchmark/benchmark.h>
+#include <buddy/Core/Container.h>
+#include <buddy/DIP/ImageContainer.h>
+#include <fstream>
+#include <iostream>
+#include <opencv2/opencv.hpp>
+#include <string>
+
+namespace {
+
+// Declare the alexnet C interface.
+extern "C" {
+void _mlir_ciface_alexnet(MemRef<float, 2> *output, Img<float, 4> *input);
+}
+
+const cv::Mat imagePreprocessing() {
+  cv::Mat inputImage =
+      cv::imread("../../benchmarks/DeepLearning/Models/AlexNet/Images/"
+                 "traffic-light.png");
+  assert(!inputImage.empty() && "Could not read the image.");
+  cv::Mat resizedImage;
+  int imageWidth = 224;
+  int imageHeight = 224;
+  cv::resize(inputImage, resizedImage, cv::Size(imageWidth, imageHeight),
+             cv::INTER_LINEAR);
+  return resizedImage;
+}
+
+cv::Mat image = imagePreprocessing();
+
+intptr_t sizesInput[4] = {1, 3, image.rows, image.cols};
+intptr_t sizesOutput[2] = {1, 1000};
+
+Img<float, 4> input(image, sizesInput, true);
+MemRef<float, 2> output(sizesOutput);
+
+// Define benchmark function.
+void BM_alexnet(benchmark::State &state) {
+  for (auto _ : state) {
+    for (int i = 0; i < state.range(0); ++i) {
+      _mlir_ciface_alexnet(&output, &input);
+    }
+  }
+}
+
+// Softmax function.
+void softmax(float *input, size_t size) {
+  assert(0 <= size <= sizeof(input) / sizeof(float));
+  int i;
+  float m, sum, constant;
+  m = -INFINITY;
+  for (i = 0; i < size; ++i) {
+    if (m < input[i]) {
+      m = input[i];
+    }
+  }
+
+  sum = 0.0;
+  for (i = 0; i < size; ++i) {
+    sum += exp(input[i] - m);
+  }
+
+  constant = m + log(sum);
+  for (i = 0; i < size; ++i) {
+    input[i] = exp(input[i] - constant);
+  }
+}
+
+std::string getLabel(int idx) {
+  std::ifstream in("../../benchmarks/DeepLearning/Models/AlexNet/Labels.txt");
+  assert(in.is_open() && "Could not read the label file.");
+  std::string label;
+  for (int i = 0; i < idx; ++i)
+    std::getline(in, label);
+  std::getline(in, label);
+  in.close();
+  return label;
+}
+
+} // namespace
+
+// Register benchmarking function with different arguments.
+BENCHMARK(BM_alexnet)->Arg(1)->Unit(benchmark::kMillisecond);
+
+// Print result function.
+void printResult() {
+  // Run the model and activation function.
+  _mlir_ciface_alexnet(&output, &input);
+  auto out = output.getData();
+  softmax(out, 1000);
+  // Find the classification and print the result.
+  float maxVal = 0;
+  float maxIdx = 0;
+  for (int i = 0; i < 1000; ++i) {
+    if (out[i] > maxVal) {
+      maxVal = out[i];
+      maxIdx = i;
+    }
+  }
+  std::cout << "Classification Index: " << maxIdx << std::endl;
+  std::cout << "Classification: " << getLabel(maxIdx) << std::endl;
+  std::cout << "Probability: " << maxVal << std::endl;
+}

benchmarks/DeepLearning/Models/AlexNet/CMakeLists.txt

@@ -0,0 +1,45 @@
+set(alexnet_TOSA_PIPELINE "builtin.module(func.func(tosa-to-linalg-named),func.func(tosa-to-linalg),func.func(tosa-to-tensor),func.func(tosa-to-arith))")
+
+# Compile MLIR file to object file.
+add_custom_command(OUTPUT alexnet-default.o
+COMMAND 
+  ${LLVM_MLIR_BINARY_DIR}/mlir-opt ${CMAKE_CURRENT_SOURCE_DIR}/alexnet_tosa.mlir
+    --pass-pipeline="${alexnet_TOSA_PIPELINE}" | 
+  ${LLVM_MLIR_BINARY_DIR}/mlir-opt
+    --test-linalg-transform-patterns="test-generalize-pad-tensor"
+    --linalg-bufferize
+    --convert-linalg-to-loops
+    --func-bufferize
+    --arith-bufferize
+    --tensor-bufferize 
+    --finalizing-bufferize
+    --convert-vector-to-scf
+    --convert-scf-to-cf
+    --expand-strided-metadata
+    --lower-affine
+    --convert-vector-to-llvm
+    --memref-expand
+    --arith-expand
+    --convert-arith-to-llvm
+    --finalize-memref-to-llvm
+    --convert-math-to-llvm
+    --llvm-request-c-wrappers
+    --convert-func-to-llvm
+    --reconcile-unrealized-casts |
+  ${LLVM_MLIR_BINARY_DIR}/mlir-translate --mlir-to-llvmir |
+  ${LLVM_MLIR_BINARY_DIR}/llc -mtriple=${BUDDY_OPT_TRIPLE} -mattr=${BUDDY_OPT_ATTR} 
+    --filetype=obj -o ${CMAKE_CURRENT_BINARY_DIR}/alexnet-default.o
+)
+
+add_library(alexnetDefault STATIC alexnet-default.o)
+set_target_properties(alexnetDefault PROPERTIES LINKER_LANGUAGE CXX)
+
+add_executable(alexnet-benchmark Main.cpp AlexNetDefaultBenchmark.cpp)
+# Link libraries
+target_link_directories(alexnet-benchmark PRIVATE ${LLVM_MLIR_LIBRARY_DIR})
+target_link_libraries(alexnet-benchmark
+  alexnetDefault
+  GoogleBenchmark
+  mlir_c_runner_utils
+  ${OpenCV_LIBS}
+)