Merge branch 'master' into 'master'

Implement AddN Op

See merge request !9

mace/core/BUILD

@@ -23,7 +23,7 @@ cc_library(
 
 # Main program for tests
 cc_library(
-    name = "test_main",
+    name = "test_benchmark_main",
     testonly = 1,
     srcs = glob([
         "testing/*.cc",

mace/core/common.h

@@ -33,4 +33,6 @@ private:                                                                       \
 
 #define MACE_NOT_IMPLEMENTED REQUIRE(false, "not implemented")
 
+#define kCostPerGroup 8192
+
 #endif // MACE_CORE_COMMON_H_

mace/examples/BUILD

@@ -28,6 +28,6 @@ cc_test(
     linkstatic = 1,
     deps = [
         "//mace/core",
-        "//mace/core:test_main",
+        "//mace/core:test_benchmark_main",
     ],
 )

mace/kernels/BUILD

@@ -17,9 +17,41 @@ cc_library(
     deps = [
         "//mace/core:core",
     ],
-    copts = ['-std=c++11'] + if_android([
-        "-mfpu=neon", # TODO recheck the flags
-        "-mfloat-abi=hard",
-        ]),
+    copts = ['-std=c++11'],
 )
 
+cc_test(
+    name = "kernel_test",
+    srcs = glob(["test/*.cc"]),
+    deps = [
+        "@gtest//:gtest_main",
+        ":kernels",
+        "//mace/core:core",
+    ],
+    copts = ['-std=c++11'],
+    linkopts = ["-fopenmp"] + if_android([
+        "-pie",
+        "-llog",
+        "-lm",
+    ]),
+    linkstatic = 1,
+    testonly = 1,
+)
+
+cc_test(
+    name = "benchmark",
+    srcs = glob(["benchmark/*.cc"]),
+    deps = [
+        ":kernels",
+        "//mace/core:core",
+        "//mace/core:test_benchmark_main",
+    ],
+    copts = ['-std=c++11'],
+    linkopts = ["-fopenmp"] + if_android([
+        "-pie",
+        "-llog",
+        "-lm",
+    ]),
+    linkstatic = 1,
+    testonly = 1,
+)

mace/kernels/addn.h

+//
+// Copyright (c) 2017 XiaoMi All rights reserved.
+//
+
+#ifndef MACE_KERNELS_ADDN_H_
+#define MACE_KERNELS_ADDN_H_
+
+#include "mace/core/tensor.h"
+
+namespace mace {
+namespace kernels {
+
+template<typename T>
+void AddNFuntion(const vector<const Tensor*>& input_tensor, Tensor *output_tensor) {
+  int n = input_tensor.size();
+  CHECK(n > 1);
+  CHECK_NOTNULL(input_tensor[0]);
+  int64 size = input_tensor[0]->size();
+  vector<const T*> inputs(n);
+  for (int i = 0; i < n; ++i) {
+    inputs[i] = input_tensor[i]->data<float>();
+  }
+  output_tensor->ResizeLike(input_tensor[0]);
+  float* output = output_tensor->mutable_data<T>();
+
+  for (int i = 0; i < n; ++i) {
+    for (int64 j = 0; j < size; ++j) {
+      output[j] += inputs[i][j];
+    }
+  }
+}
+
+} //  namespace kernels
+} //  namespace mace
+
+#endif // MACE_KERNELS_ADDN_H_
\ No newline at end of file

mace/kernels/benchmark/addn_benchmark.cc

+//
+// Copyright (c) 2017 XiaoMi All rights reserved.
+//
+
+#include "mace/core/testing/test_benchmark.h"
+#include "mace/core/tensor.h"
+#include "mace/kernels/addn.h"
+#include "mace/kernels/neon/addn_neon.h"
+
+using namespace mace;
+using namespace mace::kernels;
+
+static void AddNBenchmark(int iters, int n, int type) {
+  const int64 tot = static_cast<int64>(iters) * n * 3;
+  mace::testing::ItemsProcessed(tot);
+  mace::testing::BytesProcessed(tot * (sizeof(float)));
+
+  std::random_device rd;
+  std::mt19937 gen(rd());
+  std::normal_distribution<float> nd(0, 1);
+
+  Tensor input_tensor1(cpu_allocator(), DataType::DT_FLOAT);
+  input_tensor1.Resize({n});
+  Tensor input_tensor2(cpu_allocator(), DataType::DT_FLOAT);
+  input_tensor2.Resize({n});
+  Tensor input_tensor3(cpu_allocator(), DataType::DT_FLOAT);
+  input_tensor3.Resize({n});
+  vector<const Tensor*> input_tensors {&input_tensor1,
+                                       &input_tensor2,
+                                       &input_tensor3};
+  Tensor output_tensor(cpu_allocator(), DataType::DT_FLOAT);
+  output_tensor.ResizeLike(input_tensor1);
+  float *input1 = input_tensor1.mutable_data<float>();
+  float *input2 = input_tensor2.mutable_data<float>();
+  float *input3 = input_tensor3.mutable_data<float>();
+  float *output = output_tensor.mutable_data<float>();
+
+  for (int64 i = 0; i < n; ++i) {
+    input1[i] = nd(gen);
+    input2[i] = nd(gen);
+    input3[i] = nd(gen);
+  }
+
+  if (type == DeviceType::CPU) {
+    AddNFuntion<float>(input_tensors, &output_tensor);
+  } else if (type == DeviceType::NEON) {
+    NeonAddNFuntion_float(input_tensors, &output_tensor);
+  }
+}
+
+static const int kBenchmarkSize = 10000000;
+
+BENCHMARK(AddNBenchmark)
+    ->ArgPair(kBenchmarkSize, DeviceType::CPU)
+    ->ArgPair(kBenchmarkSize, DeviceType::NEON);

mace/kernels/benchmark/relu_benchmark.cc

+//
+// Copyright (c) 2017 XiaoMi All rights reserved.
+//
+
+#include "mace/core/testing/test_benchmark.h"
+#include "mace/core/tensor.h"
+#include "mace/kernels/relu.h"
+#include "mace/kernels/neon/relu_neon.h"
+
+using namespace mace;
+using namespace mace::kernels;
+
+static void ReluBenchmark(int iters, int n, int type) {
+  const int64 tot = static_cast<int64>(iters) * n;
+  mace::testing::ItemsProcessed(tot);
+  mace::testing::BytesProcessed(tot * (sizeof(float)));
+
+  std::random_device rd;
+  std::mt19937 gen(rd());
+  std::normal_distribution<float> nd(0, 1);
+
+  Tensor input_tensor(cpu_allocator(), DataType::DT_FLOAT);
+  input_tensor.Resize({n});
+  Tensor output_tensor(cpu_allocator(), DataType::DT_FLOAT);
+  output_tensor.ResizeLike(input_tensor);
+  float *input = input_tensor.mutable_data<float>();
+  float *output = output_tensor.mutable_data<float>();
+  for (int64 i = 0; i < n; ++i) {
+    input[i] = nd(gen);
+  }
+
+  if (type == DeviceType::CPU) {
+    ReluFuntion<float>(&input_tensor, &output_tensor);
+  } else if (type == DeviceType::NEON) {
+    NeonReluFuntion_float(&input_tensor, &output_tensor);
+  }
+}
+
+static const int kBenchmarkSize = 10000000;
+
+BENCHMARK(ReluBenchmark)
+    ->ArgPair(kBenchmarkSize, DeviceType::CPU)
+    ->ArgPair(kBenchmarkSize, DeviceType::NEON);

mace/kernels/neon/addn_neon.cc

+//
+// Copyright (c) 2017 XiaoMi All rights reserved.
+//
+
+#include <arm_neon.h>
+#include "mace/kernels/neon/addn_neon.h"
+#include "mace/core/common.h"
+
+namespace mace {
+namespace kernels {
+
+void NeonAddNFuntion_float(const vector<const Tensor *> &input_tensor,
+                           Tensor *output_tensor) {
+  int n = input_tensor.size();
+  CHECK(n > 1);
+  CHECK_NOTNULL(input_tensor[0]);
+  int64 size = input_tensor[0]->size();
+  output_tensor->ResizeLike(input_tensor[0]);
+  float *output = output_tensor->mutable_data<float>();
+  vector<const float *> inputs(n);
+  for (int i = 0; i < n; ++i) {
+    inputs[i] = input_tensor[i]->data<float>();
+  }
+
+  int64 cost = size * n;
+  int64 groups = 1;
+  if (cost > kCostPerGroup) {
+    groups = cost / kCostPerGroup;
+  }
+  int64 element_per_group = size / groups;
+
+#pragma omp parallel for num_threads(1) // no significant performance improve
+  for (int64 i = 0; i < size; i += element_per_group) {
+    int64 count = std::min(element_per_group, size - i);
+    int nn = count >> 2;
+    int remain = count - (nn << 2);
+    for (int64 j = 0; j < n; ++j) {
+      const float *inptr = inputs[j] + i;
+      float *outptr = output + i;
+      for (int k = 0; k < nn; ++k) {
+        float32x4_t _inptr = vld1q_f32(inptr);
+        float32x4_t _outptr = vld1q_f32(outptr);
+        _outptr = vaddq_f32(_outptr, _inptr);
+        vst1q_f32(outptr, _outptr);
+
+        inptr += 4;
+        outptr += 4;
+      }
+      for (int k = 0; k < remain; ++k) {
+        *outptr += *inptr;
+        ++inptr;
+        ++outptr;
+      }
+    }
+  }
+}
+
+} // namespace kernels
+} // namespace mace
\ No newline at end of file

mace/kernels/neon/addn_neon.h

+//
+// Copyright (c) 2017 XiaoMi All rights reserved.
+//
+
+#ifndef MACE_KERNELS_ADDN_NEON_H_
+#define MACE_KERNELS_ADDN_NEON_H_
+
+#include "mace/core/tensor.h"
+
+namespace mace {
+namespace kernels {
+
+void NeonAddNFuntion_float(const vector<const Tensor *> &input_tensor,
+                           Tensor *output_tensor);
+
+} // namespace kernels
+} // namespace mace
+
+#endif // MACE_KERNELS_ADDN_NEON_H_

mace/kernels/neon/relu_neon.cc

@@ -6,23 +6,36 @@
 #include "mace/kernels/neon/relu_neon.h"
 
 namespace mace {
-namespace kernels{
+namespace kernels {
 
 void NeonReluFuntion_float(const Tensor *input_tensor,
                            Tensor *output_tensor) {
   int64 size = input_tensor->size();
   output_tensor->ResizeLike(input_tensor);
-  const float* input = input_tensor->data<float>();
-  float* output = output_tensor->mutable_data<float>();
+  const float *input = input_tensor->data<float>();
+  float *output = output_tensor->mutable_data<float>();
 
-  float32x4_t _zero = vdupq_n_f32(0.f);
-  for (; size > 0; size--) {
-    float32x4_t _inp = vld1q_f32(input);
-    float32x4_t _outp = vmaxq_f32(_inp, _zero);
-    vst1q_f32(output, _outp);
+#pragma omp parallel for num_threads(1) // no significant performance improve
+  for (int64 i = 0; i < size; i += kCostPerGroup) {
+    int64 count = std::min(static_cast<int64>(kCostPerGroup), size - i);
+    int nn = count >> 2;
+    int remain = count - (nn << 2);
+    const float *inptr = input + i;
+    float *outptr = output + i;
+    float32x4_t _zero = vdupq_n_f32(0.f);
+    for (; nn > 0; --nn) {
+      float32x4_t _inptr = vld1q_f32(inptr);
+      float32x4_t _outptr = vmaxq_f32(_inptr, _zero);
+      vst1q_f32(outptr, _outptr);
 
-    input += 4;
-    output += 4;
+      inptr += 4;
+      outptr += 4;
+    }
+    for (; remain > 0; --remain) {
+      *outptr = std::max(*inptr, 0.f);
+      ++inptr;
+      ++outptr;
+    }
   }
 }
 

mace/kernels/relu.h

@@ -14,8 +14,8 @@ template<typename T>
 void ReluFuntion(const Tensor *input_tensor, Tensor *output_tensor) {
   int64 size = input_tensor->size();
   output_tensor->ResizeLike(input_tensor);
-  const float* input = input_tensor->data<float>();
-  float* output = output_tensor->mutable_data<float>();
+  const float *input = input_tensor->data<float>();
+  float *output = output_tensor->mutable_data<float>();
 
   for (int64 i = 0; i < size; ++i) {
     output[i] = std::max(input[i], static_cast<T>(0));

mace/kernels/test/addn_neon_test.cc

+//
+// Copyright (c) 2017 XiaoMi All rights reserved.
+//
+#include <random>
+#include <cmath>
+#include "gtest/gtest.h"
+#include "mace/kernels/neon/addn_neon.h"
+#include "mace/kernels/addn.h"
+
+using namespace mace;
+using namespace mace::kernels;
+
+TEST(NeonTest, AddN) {
+  std::random_device rd;
+  std::mt19937 gen(rd());
+  std::normal_distribution<float> nd(0, 1);
+
+  int64 count = 100000;
+  Tensor input_tensor1(cpu_allocator(), DataType::DT_FLOAT);
+  input_tensor1.Resize({100, 1000});
+  Tensor input_tensor2(cpu_allocator(), DataType::DT_FLOAT);
+  input_tensor2.ResizeLike(input_tensor1);
+  Tensor input_tensor3(cpu_allocator(), DataType::DT_FLOAT);
+  input_tensor3.ResizeLike(input_tensor1);
+  vector<const Tensor*> input_tensors {&input_tensor1,
+                                       &input_tensor2,
+                                       &input_tensor3};
+
+  Tensor output_tensor(cpu_allocator(), DataType::DT_FLOAT);
+  output_tensor.ResizeLike(input_tensors[0]);
+  Tensor output_tensor_neon(cpu_allocator(), DataType::DT_FLOAT);
+  output_tensor_neon.ResizeLike(input_tensors[0]);
+
+  float *input1 = input_tensor1.mutable_data<float>();
+  float *input2 = input_tensor2.mutable_data<float>();
+  float *input3 = input_tensor3.mutable_data<float>();
+  float *output = output_tensor.mutable_data<float>();
+  float *output_neon = output_tensor_neon.mutable_data<float>();
+
+  for (int64 i = 0; i < count; ++i) {
+    input1[i] = nd(gen);
+    input2[i] = nd(gen);
+    input3[i] = nd(gen);
+  }
+
+  AddNFuntion<float>(input_tensors, &output_tensor);
+  NeonAddNFuntion_float(input_tensors, &output_tensor_neon);
+
+  ASSERT_EQ(count, output_tensor.size());
+  ASSERT_EQ(count, output_tensor_neon.size());
+  for (int64 i = 0; i < count; ++i) {
+    ASSERT_FLOAT_EQ(output[i], output_neon[i]);
+  }
+}
+

mace/kernels/test/relu_neon_test.cc

+//
+// Copyright (c) 2017 XiaoMi All rights reserved.
+//
+#include <random>
+#include <cmath>
+#include "gtest/gtest.h"
+#include "mace/kernels/neon/relu_neon.h"
+#include "mace/kernels/relu.h"
+
+using namespace mace;
+using namespace mace::kernels;
+
+TEST(NeonTest, Relu) {
+  std::random_device rd;
+  std::mt19937 gen(rd());
+  std::normal_distribution<float> nd(0, 1);
+
+  int64 count = 100000;
+  Tensor input_tensor(cpu_allocator(), DataType::DT_FLOAT);
+  input_tensor.Resize({100, 1000});
+  Tensor output_tensor(cpu_allocator(), DataType::DT_FLOAT);
+  output_tensor.ResizeLike(input_tensor);
+  Tensor output_tensor_neon(cpu_allocator(), DataType::DT_FLOAT);
+  output_tensor_neon.ResizeLike(input_tensor);
+
+  float *input = input_tensor.mutable_data<float>();
+  float *output = output_tensor.mutable_data<float>();
+  float *output_neon = output_tensor_neon.mutable_data<float>();
+
+  for (int64 i = 0; i < count; ++i) {
+    input[i] = nd(gen);
+  }
+
+  ReluFuntion<float>(&input_tensor, &output_tensor);
+  NeonReluFuntion_float(&input_tensor, &output_tensor_neon);
+
+  ASSERT_EQ(count, output_tensor.size());
+  ASSERT_EQ(count, output_tensor_neon.size());
+  for (int64 i = 0; i < count; ++i) {
+    ASSERT_FLOAT_EQ(output[i], output_neon[i]);
+  }
+}
+