eltwise support multi dimensions

mace/kernels/cwise.h

@@ -35,6 +35,7 @@ enum CWiseType {
   DIV = 5,
   NEG = 6,
   ABS = 7,
+  SQR_DIFF = 8,
 };
 
 struct CWiseFunctorBase {
@@ -92,6 +93,7 @@ struct CWiseFunctor : CWiseFunctorBase {
         }
         break;
       case DIV:
+        MACE_CHECK(fabs(coeff_) > 1e-6, "cannot divided by 0.");
 #pragma omp parallel for
         for (index_t i = 0; i < size; ++i) {
           output_ptr[i] =  input_ptr[i] / coeff_;

mace/kernels/eltwise.h

@@ -32,10 +32,15 @@ enum EltwiseType {
   MAX = 2,
   MIN = 3,
   SUB = 4,
+  DIV = 5,
+  NEG = 6,
+  ABS = 7,
+  SQR_DIFF = 8,
 };
 
 struct EltwiseFunctorBase {
-  EltwiseFunctorBase(const EltwiseType type, const std::vector<float> &coeff)
+  EltwiseFunctorBase(const EltwiseType type,
+                     const std::vector<float> &coeff)
       : type_(type), coeff_(coeff) {}
 
   EltwiseType type_;
@@ -44,74 +49,211 @@ struct EltwiseFunctorBase {
 
 template <DeviceType D, typename T>
 struct EltwiseFunctor : EltwiseFunctorBase {
-  EltwiseFunctor(const EltwiseType type, const std::vector<float> &coeff)
+  EltwiseFunctor(const EltwiseType type,
+                 const std::vector<float> &coeff)
       : EltwiseFunctorBase(type, coeff) {}
 
   void operator()(const Tensor *input0,
                   const Tensor *input1,
+                  const index_t start_axis,
+                  const bool is_scaler,
+                  const float value,
+                  const bool swap,
                   Tensor *output,
                   StatsFuture *future) {
-    Tensor::MappingGuard input0_guard(input0);
-    Tensor::MappingGuard input1_guard(input1);
-    Tensor::MappingGuard output_guard(output);
+    if (is_scaler) {
+      Tensor::MappingGuard input0_guard(input0);
+      Tensor::MappingGuard output_guard(output);
 
-    const T *input0_ptr = input0->data<T>();
-    const T *input1_ptr = input1->data<T>();
-    T *output_ptr = output->mutable_data<T>();
-    const index_t size = input0->size();
-
-    switch (type_) {
-      case PROD:
+      const T *input0_ptr = input0->data<T>();
+      T *output_ptr = output->mutable_data<T>();
+      const index_t num = input0->size();
+      switch (type_) {
+        case PROD:
+#pragma omp parallel for
+          for (index_t i = 0; i < num; ++i) {
+              output_ptr[i] = input0_ptr[i] * value;
+          }
+          break;
+        case SUM:
+          if (coeff_.empty()) {
 #pragma omp parallel for
-        for (index_t i = 0; i < size; ++i) {
-          output_ptr[i] = input0_ptr[i] * input1_ptr[i];
-        }
-        break;
-      case SUM:
-        if (coeff_.empty()) {
+            for (index_t i = 0; i < num; ++i) {
+              output_ptr[i] = input0_ptr[i] + value;
+            }
+          } else {
+            const float coeff_0 = swap ? coeff_[1] : coeff_[0];
+            const float coeff_1 = swap ? coeff_[0] : coeff_[1];
 #pragma omp parallel for
-          for (index_t i = 0; i < size; ++i) {
-            output_ptr[i] = input0_ptr[i] + input1_ptr[i];
+            for (index_t i = 0; i < num; ++i) {
+                output_ptr[i] = coeff_0 * input0_ptr[i] +
+                        coeff_1 * value;
+            }
           }
-        } else {
+          break;
+        case MAX:
 #pragma omp parallel for
-          for (index_t i = 0; i < size; ++i) {
-            output_ptr[i] =
-                coeff_[0] * input0_ptr[i] + coeff_[1] * input1_ptr[i];
+          for (index_t i = 0; i < num; ++i) {
+              output_ptr[i] = std::max<T>(input0_ptr[i], value);
           }
-        }
-        break;
-      case MAX:
+          break;
+        case MIN:
 #pragma omp parallel for
-        for (index_t i = 0; i < size; ++i) {
-          output_ptr[i] = std::max<T>(input0_ptr[i], input1_ptr[i]);
-        }
-        break;
-      case MIN:
+          for (index_t i = 0; i < num; ++i) {
+              output_ptr[i] = std::min<T>(input0_ptr[i], value);
+          }
+          break;
+        case SUB:
 #pragma omp parallel for
-        for (index_t i = 0; i < size; ++i) {
-          output_ptr[i] = std::min<T>(input0_ptr[i], input1_ptr[i]);
-        }
-        break;
-      case SUB:
+          for (index_t i = 0; i < num; ++i) {
+              output_ptr[i] = swap ? value - input0_ptr[i] :
+                              input0_ptr[i] - value;
+          }
+          break;
+        case DIV:
+          if (!swap) {
+            MACE_CHECK(fabs(value) > 1e-6, "cannot divided by 0.");
 #pragma omp parallel for
-        for (index_t i = 0; i < size; ++i) {
-          output_ptr[i] = input0_ptr[i] - input1_ptr[i];
-        }
-        break;
-      default:
-        LOG(FATAL) << "Eltwise op not support type " << type_;
+            for (index_t i = 0; i < num; ++i) {
+              output_ptr[i] = input0_ptr[i] / value;
+            }
+          } else {
+#pragma omp parallel for
+            for (index_t i = 0; i < num; ++i) {
+              MACE_CHECK(fabs(input0_ptr[i]) > 1e-6, "cannot divided by 0.");
+              output_ptr[i] = value / input0_ptr[i];
+            }
+          }
+          break;
+        case SQR_DIFF:
+#pragma omp parallel for
+          for (index_t i = 0; i < num; ++i) {
+              const float tmp = input0_ptr[i] - value;
+              output_ptr[i] = tmp * tmp;
+          }
+          break;
+        default:
+          LOG(FATAL) << "Eltwise op not support type " << type_;
+      }
+    } else {
+      MACE_CHECK_NOTNULL(input0);
+      MACE_CHECK_NOTNULL(input1);
+      Tensor::MappingGuard input0_guard(input0);
+      Tensor::MappingGuard input1_guard(input1);
+      Tensor::MappingGuard output_guard(output);
+
+      const T *input0_ptr = input0->data<T>();
+      const T *input1_ptr = input1->data<T>();
+      T *output_ptr = output->mutable_data<T>();
+      const index_t size0 = input0->size();
+      const index_t size1 = input1->size();
+
+      const index_t num = size0 / size1;
+      switch (type_) {
+        case PROD:
+#pragma omp parallel for collapse(2)
+          for (index_t i = 0; i < num; ++i) {
+            for (index_t j= 0; j < size1; ++j) {
+              output_ptr[i * size1 + j] =
+                  input0_ptr[i * size1 + j] * input1_ptr[j];
+            }
+          }
+          break;
+        case SUM:
+          if (coeff_.empty()) {
+#pragma omp parallel for collapse(2)
+            for (index_t i = 0; i < num; ++i) {
+              for (index_t j = 0; j < size1; ++j) {
+                output_ptr[i * size1 + j] =
+                    input0_ptr[i * size1 + j] + input1_ptr[j];
+              }
+            }
+          } else {
+            const float coeff_0 = swap ? coeff_[1] : coeff_[0];
+            const float coeff_1 = swap ? coeff_[0] : coeff_[1];
+#pragma omp parallel for collapse(2)
+            for (index_t i = 0; i < num; ++i) {
+              for (index_t j = 0; j < size1; ++j) {
+                output_ptr[i * size1 + j] =
+                    coeff_0 * input0_ptr[i * size1 + j] +
+                        coeff_1 * input1_ptr[j];
+              }
+            }
+          }
+          break;
+        case MAX:
+#pragma omp parallel for collapse(2)
+          for (index_t i = 0; i < num; ++i) {
+            for (index_t j = 0; j < size1; ++j) {
+              output_ptr[i * size1 + j] =
+                  std::max<T>(input0_ptr[i * size1 + j], input1_ptr[j]);
+            }
+          }
+          break;
+        case MIN:
+#pragma omp parallel for collapse(2)
+          for (index_t i = 0; i < num; ++i) {
+            for (index_t j = 0; j < size1; ++j) {
+              output_ptr[i * size1 + j] =
+                  std::min<T>(input0_ptr[i * size1 + j], input1_ptr[j]);
+            }
+          }
+          break;
+        case SUB:
+#pragma omp parallel for collapse(2)
+          for (index_t i = 0; i < num; ++i) {
+            for (index_t j = 0; j < size1; ++j) {
+              output_ptr[i * size1 + j] = swap ?
+                  input0_ptr[i * size1 + j] - input1_ptr[j] :
+                  input1_ptr[j] - input0_ptr[i * size1 + j];
+            }
+          }
+          break;
+        case DIV:
+#pragma omp parallel for collapse(2)
+          for (index_t i = 0; i < num; ++i) {
+            for (index_t j = 0; j < size1; ++j) {
+              if (!swap) {
+                MACE_CHECK(fabs(input1_ptr[j]) > 1e-6, "cannot divided by 0.");
+                output_ptr[i * size1 + j] =
+                    input0_ptr[i * size1 + j] / input1_ptr[j];
+              } else {
+                MACE_CHECK(fabs(input0_ptr[i * size1 + j]) > 1e-6,
+                           "cannot divided by 0.");
+                output_ptr[i * size1 + j] =
+                    input1_ptr[j] / input0_ptr[i * size1 + j];
+              }
+            }
+          }
+          break;
+        case SQR_DIFF:
+#pragma omp parallel for collapse(2)
+          for (index_t i = 0; i < num; ++i) {
+            for (index_t j = 0; j < size1; ++j) {
+              const T tmp = input0_ptr[i * size1 + j] - input1_ptr[j];
+              output_ptr[i * size1 + j] = tmp * tmp;
+            }
+          }
+          break;
+        default:
+          LOG(FATAL) << "Eltwise op not support type " << type_;
+      }
     }
   }
 };
 
 template <typename T>
 struct EltwiseFunctor<DeviceType::OPENCL, T> : EltwiseFunctorBase {
-  EltwiseFunctor(const EltwiseType type, const std::vector<float> &coeff)
+  EltwiseFunctor(const EltwiseType type,
+                 const std::vector<float> &coeff)
       : EltwiseFunctorBase(type, coeff) {}
 
   void operator()(const Tensor *input0,
                   const Tensor *input1,
+                  const index_t start_axis,
+                  const bool is_scaler,
+                  const float value,
+                  const bool swap,
                   Tensor *output,
                   StatsFuture *future);
 

mace/kernels/opencl/cl/cwise.cl

@@ -31,7 +31,14 @@ __kernel void cwise(KERNEL_ERROR_PARAMS
 #elif CWISE_TYPE == 4
   out = in0 - in1;
 #elif CWISE_TYPE == 5
-  out = in0 / in1;
+  if (fabs(in1.x) > 0.000001f)
+      out.x = in0.x / in1.x;
+  if (fabs(in1.y) > 0.000001f)
+    out.y = in0.y / in1.y;
+  if (fabs(in1.z) > 0.000001f)
+    out.z = in0.z / in1.z;
+  if (fabs(in1.w) > 0.000001f)
+    out.w = in0.w / in1.w;
 #elif CWISE_TYPE == 6
   in1 = (DATA_TYPE4)(0, 0, 0, 0);
   out = in1 - in0;

mace/kernels/opencl/cl/eltwise.cl

 #include <common.h>
 
 __kernel void eltwise(KERNEL_ERROR_PARAMS
-                      GLOBAL_WORK_GROUP_SIZE_DIM2
-                      __read_only image2d_t input0, /* [c%4 * w * c/4, h * b] */
+                      GLOBAL_WORK_GROUP_SIZE_DIM3
+                      __read_only image2d_t input0,
                       __read_only image2d_t input1,
+                      __private const float value,
+                      __private const int height,
+                      __private const int width,
+                      __private const int channel,
 #ifdef COEFF_SUM
                       __private const float coeff0,
                       __private const float coeff1,
 #endif
                       __write_only image2d_t output) {
-  const int w = get_global_id(0);
-  const int hb = get_global_id(1);
+  const int c = get_global_id(0);
+  const int w = get_global_id(1);
+  const int hb = get_global_id(2);
 
 #ifndef NON_UNIFORM_WORK_GROUP
-  if (w >= global_size_dim0 || hb >= global_size_dim1) return;
+  if (c >= global_size_dim0 || w >= global_size_dim1 || hb >= global_size_dim2)
+    return;
 #endif
 
-  DATA_TYPE4 in0 = READ_IMAGET(input0, SAMPLER, (int2)(w, hb));
-  DATA_TYPE4 in1 = READ_IMAGET(input1, SAMPLER, (int2)(w, hb));
+  int pos_w;
+  int pos_h;
+#if START_AXIS == 0
+  pos_w = mad24(c, width, w);
+  pos_h = hb;
+#elif START_AXIS == 1
+  pos_w = mad24(c, width, w);
+  pos_h = hb % height;
+#elif START_AXIS == 2
+  pos_w = mad24(c, width, w);
+  pos_h = 0;
+#elif START_AXIS == 3
+  pos_w = c;
+  pos_h = 0;
+#endif
+  const int pos = mad24(c, width, w);
+  const int remain_channel = channel - 4 * c;
+  DATA_TYPE4 in0 = READ_IMAGET(input0, SAMPLER, (int2)(pos, hb));
+  DATA_TYPE4 in1 ;
+#if IS_SCALER == 1
+  in1 = (DATA_TYPE4){value, value, value, value};
+#else
+  in1 = READ_IMAGET(input1, SAMPLER, (int2)(pos_w, pos_h));
+#endif
   DATA_TYPE4 out;
 #if ELTWISE_TYPE == 0
   out = in0 * in1;
 #elif ELTWISE_TYPE == 1
 
 #ifdef COEFF_SUM
-  out = mad(coeff0, in0, mad(coeff1, in1, 0));
+  #if NEEDSWAP == 0
+    out = mad(coeff0, in0, mad(coeff1, in1, 0));
+  #else
+    out = mad(coeff1, in0, mad(coeff0, in1, 0));
+  #endif
 #else
   out = in0 + in1;
 #endif
@@ -34,8 +66,49 @@ __kernel void eltwise(KERNEL_ERROR_PARAMS
 #elif ELTWISE_TYPE == 3
   out = fmin(in0, in1);
 #elif ELTWISE_TYPE == 4
-  out = in0 - in1;
+  #if NEED_SWAP == 0
+    out = in0 - in1;
+  #else
+    out = in1 - in0;
+  #endif
+#elif ELTWISE_TYPE == 5
+  #if NEED_SWAP == 0
+    if (fabs(in1.x) > 0.000001f)
+      out.x = in0.x / in1.x;
+    if (fabs(in1.y) > 0.000001f)
+      out.y = in0.y / in1.y;
+    if (fabs(in1.z) > 0.000001f)
+      out.z = in0.z / in1.z;
+    if (fabs(in1.w) > 0.000001f)
+      out.w = in0.w / in1.w;
+  #else
+    if (fabs(in1.x) > 0.000001f)
+      out.x = in1.x / in0.x;
+    if (fabs(in1.y) > 0.000001f)
+      out.y = in1.y / in0.y;
+    if (fabs(in1.z) > 0.000001f)
+      out.z = in1.z / in0.z;
+    if (fabs(in1.w) > 0.000001f)
+      out.w = in1.w / in0.w;
+  #endif
+#elif ELTWISE_TYPE == 8
+  DATA_TYPE4 diff = in0 - in1;
+  out = diff * diff;
+#endif
+
+#if ELTWISE_TYPE == 1 || ELTWISE_TYPE == 2 || ELTWISE_TYPE == 3 \
+   || ELTWISE_TYPE == 4 || ELTWISE_TYPE == 8
+  if (remain_channel < 4) {
+    switch (remain_channel) {
+      case 1:
+        out.y = 0;
+      case 2:
+        out.z = 0;
+      case 3:
+        out.w = 0;
+    }
+  }
 #endif
 
-  WRITE_IMAGET(output, (int2)(w, hb), out);
+  WRITE_IMAGET(output, (int2)(pos, hb), out);
 }

mace/kernels/opencl/eltwise_opencl.cc

@@ -23,6 +23,10 @@ namespace kernels {
 template <typename T>
 void EltwiseFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input0,
                                                        const Tensor *input1,
+                                                       const index_t start_axis,
+                                                       const bool is_scaler,
+                                                       const float value,
+                                                       const bool swap,
                                                        Tensor *output,
                                                        StatsFuture *future) {
   const index_t batch = input0->dim(0);
@@ -31,14 +35,15 @@ void EltwiseFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input0,
   const index_t channels = input0->dim(3);
 
   const index_t channel_blocks = RoundUpDiv4(channels);
-  const index_t width_pixels = channel_blocks * width;
   const index_t batch_height_pixels = batch * height;
 
-  const uint32_t gws[2] = {static_cast<uint32_t>(width_pixels),
+  const uint32_t gws[3] = {static_cast<uint32_t>(channel_blocks),
+                           static_cast<uint32_t>(width),
                            static_cast<uint32_t>(batch_height_pixels)};
 
+  const int scaler = is_scaler ? 1 : 0;
+  const int need_swap = swap ? 1 : 0;
   auto runtime = OpenCLRuntime::Global();
-
   if (kernel_.get() == nullptr) {
     std::set<std::string> built_options;
     auto dt = DataTypeToEnum<T>::value;
@@ -47,6 +52,9 @@ void EltwiseFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input0,
     built_options.emplace("-DDATA_TYPE=" + DtToUpstreamCLDt(dt));
     built_options.emplace("-DCMD_DATA_TYPE=" + DtToUpstreamCLCMDDt(dt));
     built_options.emplace(MakeString("-DELTWISE_TYPE=", type_));
+    built_options.emplace(MakeString("-DSTART_AXIS=", start_axis));
+    built_options.emplace(MakeString("-DIS_SCALER=", scaler));
+    built_options.emplace(MakeString("-DNEEDSWAP=", need_swap));
     if (runtime->IsOutOfRangeCheckEnabled()) {
       built_options.emplace("-DOUT_OF_RANGE_CHECK");
       kernel_error_ = std::move(std::unique_ptr<Buffer>(
@@ -73,9 +81,14 @@ void EltwiseFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input0,
     if (!runtime->IsNonUniformWorkgroupsSupported()) {
       kernel_.setArg(idx++, gws[0]);
       kernel_.setArg(idx++, gws[1]);
+      kernel_.setArg(idx++, gws[2]);
     }
     kernel_.setArg(idx++, *(input0->opencl_image()));
     kernel_.setArg(idx++, *(input1->opencl_image()));
+    kernel_.setArg(idx++, value);
+    kernel_.setArg(idx++, static_cast<int32_t>(height));
+    kernel_.setArg(idx++, static_cast<int32_t>(width));
+    kernel_.setArg(idx++, static_cast<int32_t>(channels));
     if (!coeff_.empty()) {
       kernel_.setArg(idx++, coeff_[0]);
       kernel_.setArg(idx++, coeff_[1]);
@@ -85,11 +98,11 @@ void EltwiseFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input0,
     input_shape_ = input0->shape();
   }
 
-  const std::vector<uint32_t> lws = {kwg_size_ / 16, 16, 0};
+  const std::vector<uint32_t> lws = {8, kwg_size_ / 64, 8, 0};
   std::stringstream ss;
   ss << "eltwise_opencl_kernel_" << output->dim(0) << "_" << output->dim(1)
      << "_" << output->dim(2) << "_" << output->dim(3);
-  TuningOrRun2DKernel(kernel_, ss.str(), gws, lws, future);
+  TuningOrRun3DKernel(kernel_, ss.str(), gws, lws, future);
   if (runtime->IsOutOfRangeCheckEnabled()) {
     kernel_error_->Map(nullptr);
     char *kerror_code = kernel_error_->mutable_data<char>();

mace/ops/eltwise.h

@@ -32,24 +32,53 @@ class EltwiseOp : public Operator<D, T> {
                  OperatorBase::GetRepeatedArgument<float>("coeff")) {}
 
   bool Run(StatsFuture *future) override {
-    const Tensor *input0 = this->Input(0);
-    const Tensor *input1 = this->Input(1);
-    Tensor *output = this->Output(OUTPUT);
-    MACE_CHECK(input0->dim_size() == input1->dim_size())
+    if (this->InputSize() == 1) {
+      const Tensor* input = this->Input(0);
+      Tensor *output = this->Output(OUTPUT);
+      start_axis_ = input->dim_size() - 1;
+      is_scaler_ = true;
+      output->ResizeLike(input);
+      const float x = OperatorBase::GetSingleArgument<float>("x", 1.0);
+      functor_(input, nullptr, start_axis_,
+               is_scaler_, x, false, output, future);
+    } else {
+      const index_t size0 = this->Input(0)->size();
+      const index_t size1 = this->Input(1)->size();
+      const bool swap = (size0 < size1);
+      const Tensor *input0 = swap ? this->Input(1) : this->Input(0);
+      const Tensor *input1 = swap ? this->Input(0) : this->Input(1);
+
+      Tensor *output = this->Output(OUTPUT);
+      MACE_CHECK(input0->dim_size() == input1->dim_size())
         << "Inputs of Eltwise op must be same shape";
-    for (int i = 0; i < input0->dim_size(); ++i) {
-      MACE_CHECK(input0->dim(i) == input1->dim(i))
-          << "Inputs of Eltwise op must be same shape";
+      start_axis_ = input0->dim_size() - 1;
+      is_scaler_ = (input1->size() == 1);
+      uint32_t compared_size = 1;
+      if (!is_scaler_) {
+        while (start_axis_ >= 0) {
+          MACE_CHECK(input0->dim(start_axis_) == input1->dim(start_axis_),
+                     "Invalid inputs dimension at axis: ") << start_axis_
+                     << "input 0: " << input0->dim(start_axis_)
+                     << "input 1: " << input1->dim(start_axis_);
+          compared_size *= input1->dim(start_axis_);
+          if (compared_size == input1->size()) {
+            break;
+          }
+          start_axis_--;
+        }
+      }
+      output->ResizeLike(input0);
+      const float x = OperatorBase::GetSingleArgument<float>("x", 1.0);
+      functor_(input0, input1, start_axis_,
+               is_scaler_, x, swap, output, future);
     }
-
-    output->ResizeLike(input0);
-
-    functor_(input0, input1, output, future);
     return true;
   }
 
  private:
   kernels::EltwiseFunctor<D, T> functor_;
+  index_t start_axis_;
+  bool is_scaler_;
 
  private:
   OP_OUTPUT_TAGS(OUTPUT);

mace/ops/eltwise_test.cc

@@ -25,23 +25,26 @@ class EltwiseOpTest : public OpsTestBase {};
 namespace {
 template <DeviceType D>
 void Simple(const kernels::EltwiseType type,
-            const std::vector<index_t> &shape,
+            const std::vector<index_t> &shape0,
+            const std::vector<index_t> &shape1,
             const std::vector<float> &input0,
             const std::vector<float> &input1,
             const std::vector<float> &output,
+            const float x = 1.f,
             const std::vector<float> coeff = {}) {
   // Construct graph
   OpsTestNet net;
 
   // Add input data
-  net.AddInputFromArray<D, float>("Input1", shape, input0);
-  net.AddInputFromArray<D, float>("Input2", shape, input1);
+  net.AddInputFromArray<D, float>("Input1", shape0, input0);
+  net.AddInputFromArray<D, float>("Input2", shape1, input1);
 
   if (D == DeviceType::CPU) {
     OpDefBuilder("Eltwise", "EltwiseTest")
         .Input("Input1")
         .Input("Input2")
         .AddIntArg("type", static_cast<int>(type))
+        .AddFloatArg("x", x)
         .AddFloatsArg("coeff", coeff)
         .Output("Output")
         .Finalize(net.NewOperatorDef());
@@ -57,6 +60,7 @@ void Simple(const kernels::EltwiseType type,
         .Input("InputImg1")
         .Input("InputImg2")
         .AddIntArg("type", static_cast<int>(type))
+        .AddFloatArg("x", x)
         .AddFloatsArg("coeff", coeff)
         .Output("OutputImg")
         .Finalize(net.NewOperatorDef());
@@ -68,7 +72,7 @@ void Simple(const kernels::EltwiseType type,
                             kernels::BufferType::IN_OUT_CHANNEL);
   }
 
-  auto expected = CreateTensor<float>(shape, output);
+  auto expected = CreateTensor<float>(shape0, output);
 
   ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-5);
 }
@@ -76,53 +80,200 @@ void Simple(const kernels::EltwiseType type,
 
 TEST_F(EltwiseOpTest, CPUSimple) {
   Simple<DeviceType::CPU>(kernels::EltwiseType::PROD, {1, 1, 2, 3},
+                          {1, 1, 2, 3},
                           {1, 2, 3, 4, 5, 6}, {1, 2, 3, 4, 5, 6},
                           {1, 4, 9, 16, 25, 36});
   Simple<DeviceType::CPU>(kernels::EltwiseType::SUM, {1, 1, 2, 3},
+                          {1, 1, 2, 3},
                           {1, 2, 3, 4, 5, 6}, {1, 2, 3, 4, 5, 6},
                           {2, 4, 6, 8, 10, 12});
   Simple<DeviceType::CPU>(kernels::EltwiseType::SUM, {1, 1, 2, 3},
+                          {1, 1, 2, 3},
                           {1, 2, 3, 4, 5, 6}, {1, 2, 3, 4, 5, 6},
-                          {3, 6, 9, 12, 15, 18}, {2, 1});
+                          {3, 6, 9, 12, 15, 18}, 1., {2, 1});
   Simple<DeviceType::CPU>(kernels::EltwiseType::MAX, {1, 1, 2, 3},
+                          {1, 1, 2, 3},
                           {1, 2, 3, 4, 5, 6}, {1, 1, 3, 3, 6, 6},
                           {1, 2, 3, 4, 6, 6});
   Simple<DeviceType::CPU>(kernels::EltwiseType::MIN, {1, 1, 2, 3},
+                          {1, 1, 2, 3},
                           {1, 2, 3, 4, 5, 6}, {1, 1, 3, 3, 6, 6},
                           {1, 1, 3, 3, 5, 6});
+  Simple<DeviceType::CPU>(kernels::EltwiseType::SQR_DIFF, {1, 1, 2, 3},
+                          {1, 1, 2, 3},
+                          {1, 2, 3, 4, 5, 6}, {1, 1, 3, 3, 6, 6},
+                          {0, 1, 0, 1, 1, 0});
+  Simple<DeviceType::CPU>(kernels::EltwiseType::DIV, {1, 1, 2, 3},
+                          {1, 1, 2, 3},
+                          {1, 2, 3, 4, 5, 6}, {1, 1, 3, 2, 10, 24},
+                          {1, 2, 1, 2, 0.5, 0.25});
+
+  Simple<DeviceType::CPU>(kernels::EltwiseType::PROD, {1, 1, 2, 3},
+                          {1, 1, 1, 3},
+                          {1, 2, 3, 4, 5, 6}, {1, 2, 3},
+                          {1, 4, 9, 4, 10, 18});
+  Simple<DeviceType::CPU>(kernels::EltwiseType::SUM, {1, 1, 2, 3},
+                          {1, 1, 1, 3},
+                          {1, 2, 3, 4, 5, 6}, {1, 2, 3},
+                          {2, 4, 6, 5, 7, 9});
+  Simple<DeviceType::CPU>(kernels::EltwiseType::SUM, {1, 1, 2, 3},
+                          {1, 1, 1, 3},
+                          {1, 2, 3, 4, 5, 6}, {1, 2, 3},
+                          {3, 6, 9, 9, 12, 15}, 1., {2, 1});
+  Simple<DeviceType::CPU>(kernels::EltwiseType::MAX, {1, 1, 2, 3},
+                          {1, 1, 1, 3},
+                          {1, 2, 3, 4, 5, 6}, {1, 1, 3},
+                          {1, 2, 3, 4, 5, 6});
+  Simple<DeviceType::CPU>(kernels::EltwiseType::MIN, {1, 1, 2, 3},
+                          {1, 1, 1, 3},
+                          {1, 2, 3, 4, 5, 6}, {1, 1, 3},
+                          {1, 1, 3, 1, 1, 3});
+  Simple<DeviceType::CPU>(kernels::EltwiseType::SQR_DIFF, {1, 1, 2, 3},
+                          {1, 1, 1, 3},
+                          {1, 2, 3, 4, 5, 6}, {1, 1, 3},
+                          {0, 1, 0, 9, 16, 9});
+  Simple<DeviceType::CPU>(kernels::EltwiseType::DIV, {1, 1, 2, 3},
+                          {1, 1, 1, 3},
+                          {1, 2, 3, 4, 5, 6}, {1, 1, 3},
+                          {1, 2, 1, 4, 5, 2});
+
+  Simple<DeviceType::CPU>(kernels::EltwiseType::PROD, {1, 1, 2, 3},
+                          {1, 1, 1, 1},
+                          {1, 2, 3, 4, 5, 6}, {2},
+                          {2, 4, 6, 8, 10, 12}, 2);
+  Simple<DeviceType::CPU>(kernels::EltwiseType::SUM, {1, 1, 2, 3},
+                          {1, 1, 1, 1},
+                          {1, 2, 3, 4, 5, 6}, {2},
+                          {3, 4, 5, 6, 7, 8}, 2);
+  Simple<DeviceType::CPU>(kernels::EltwiseType::SUM, {1, 1, 2, 3},
+                          {1, 1, 1, 1},
+                          {1, 2, 3, 4, 5, 6}, {2},
+                          {4, 6, 8, 10, 12, 14}, 2, {2, 1});
+  Simple<DeviceType::CPU>(kernels::EltwiseType::MAX, {1, 1, 2, 3},
+                          {1, 1, 1, 1},
+                          {1, 2, 3, 4, 5, 6}, {3},
+                          {3, 3, 3, 4, 5, 6}, 3);
+  Simple<DeviceType::CPU>(kernels::EltwiseType::MIN, {1, 1, 2, 3},
+                          {1, 1, 1, 1},
+                          {1, 2, 3, 4, 5, 6}, {3},
+                          {1, 2, 3, 3, 3, 3}, 3);
+  Simple<DeviceType::CPU>(kernels::EltwiseType::DIV, {1, 1, 2, 3},
+                          {1, 1, 1, 1},
+                          {1, 2, 3, 4, 5, 6}, {0.5},
+                          {2, 4, 6, 8, 10, 12}, 0.5);
+  Simple<DeviceType::CPU>(kernels::EltwiseType::SQR_DIFF, {1, 1, 2, 3},
+                          {1, 1, 1, 1},
+                          {1, 2, 3, 4, 5, 6}, {3},
+                          {4, 1, 0, 1, 4, 9}, 3);
 }
 
 TEST_F(EltwiseOpTest, GPUSimple) {
   Simple<DeviceType::OPENCL>(kernels::EltwiseType::PROD, {1, 1, 2, 3},
-                             {1, 2, 3, 4, 5, 6}, {1, 2, 3, 4, 5, 6},
-                             {1, 4, 9, 16, 25, 36});
+                          {1, 1, 2, 3},
+                          {1, 2, 3, 4, 5, 6}, {1, 2, 3, 4, 5, 6},
+                          {1, 4, 9, 16, 25, 36});
+  Simple<DeviceType::OPENCL>(kernels::EltwiseType::SUM, {1, 1, 2, 3},
+                          {1, 1, 2, 3},
+                          {1, 2, 3, 4, 5, 6}, {1, 2, 3, 4, 5, 6},
+                          {2, 4, 6, 8, 10, 12});
+  Simple<DeviceType::OPENCL>(kernels::EltwiseType::SUM, {1, 1, 2, 3},
+                          {1, 1, 2, 3},
+                          {1, 2, 3, 4, 5, 6}, {1, 2, 3, 4, 5, 6},
+                          {3, 6, 9, 12, 15, 18}, 1., {2, 1});
+  Simple<DeviceType::OPENCL>(kernels::EltwiseType::MAX, {1, 1, 2, 3},
+                          {1, 1, 2, 3},
+                          {1, 2, 3, 4, 5, 6}, {1, 1, 3, 3, 6, 6},
+                          {1, 2, 3, 4, 6, 6});
+  Simple<DeviceType::OPENCL>(kernels::EltwiseType::MIN, {1, 1, 2, 3},
+                          {1, 1, 2, 3},
+                          {1, 2, 3, 4, 5, 6}, {1, 1, 3, 3, 6, 6},
+                          {1, 1, 3, 3, 5, 6});
+  Simple<DeviceType::OPENCL>(kernels::EltwiseType::DIV, {1, 1, 2, 3},
+                          {1, 1, 2, 3},
+                          {1, 2, 3, 4, 5, 6}, {1, 1, 3, 2, 10, 24},
+                          {1, 2, 1, 2, 0.5, 0.25});
+  Simple<DeviceType::OPENCL>(kernels::EltwiseType::SQR_DIFF, {1, 1, 2, 3},
+                          {1, 1, 2, 3},
+                          {1, 2, 3, 4, 5, 6}, {1, 1, 3, 3, 6, 6},
+                          {0, 1, 0, 1, 1, 0});
+
+  Simple<DeviceType::OPENCL>(kernels::EltwiseType::PROD, {1, 1, 2, 3},
+                          {1, 1, 1, 3},
+                          {1, 2, 3, 4, 5, 6}, {1, 2, 3},
+                          {1, 4, 9, 4, 10, 18});
   Simple<DeviceType::OPENCL>(kernels::EltwiseType::SUM, {1, 1, 2, 3},
-                             {1, 2, 3, 4, 5, 6}, {1, 2, 3, 4, 5, 6},
-                             {2, 4, 6, 8, 10, 12});
+                          {1, 1, 1, 3},
+                          {1, 2, 3, 4, 5, 6}, {1, 2, 3},
+                          {2, 4, 6, 5, 7, 9});
   Simple<DeviceType::OPENCL>(kernels::EltwiseType::SUM, {1, 1, 2, 3},
-                             {1, 2, 3, 4, 5, 6}, {1, 2, 3, 4, 5, 6},
-                             {3, 6, 9, 12, 15, 18}, {2, 1});
+                          {1, 1, 1, 3},
+                          {1, 2, 3, 4, 5, 6}, {1, 2, 3},
+                          {3, 6, 9, 9, 12, 15}, 1., {2, 1});
   Simple<DeviceType::OPENCL>(kernels::EltwiseType::MAX, {1, 1, 2, 3},
-                             {1, 2, 3, 4, 5, 6}, {1, 1, 3, 3, 6, 6},
-                             {1, 2, 3, 4, 6, 6});
+                          {1, 1, 1, 3},
+                          {1, 2, 3, 4, 5, 6}, {1, 1, 3},
+                          {1, 2, 3, 4, 5, 6});
   Simple<DeviceType::OPENCL>(kernels::EltwiseType::MIN, {1, 1, 2, 3},
-                             {1, 2, 3, 4, 5, 6}, {1, 1, 3, 3, 6, 6},
-                             {1, 1, 3, 3, 5, 6});
+                          {1, 1, 1, 3},
+                          {1, 2, 3, 4, 5, 6}, {1, 1, 3},
+                          {1, 1, 3, 1, 1, 3});
+  Simple<DeviceType::OPENCL>(kernels::EltwiseType::SQR_DIFF, {1, 1, 2, 3},
+                          {1, 1, 1, 3},
+                          {1, 2, 3, 4, 5, 6}, {1, 1, 3},
+                          {0, 1, 0, 9, 16, 9});
+  Simple<DeviceType::OPENCL>(kernels::EltwiseType::DIV, {1, 1, 2, 3},
+                          {1, 1, 1, 3},
+                          {1, 2, 3, 4, 5, 6}, {1, 1, 3},
+                          {1, 2, 1, 4, 5, 2});
+
+  Simple<DeviceType::OPENCL>(kernels::EltwiseType::PROD, {1, 1, 2, 3},
+                          {1, 1, 1, 1},
+                          {1, 2, 3, 4, 5, 6}, {2},
+                          {2, 4, 6, 8, 10, 12}, 2);
+  Simple<DeviceType::OPENCL>(kernels::EltwiseType::SUM, {1, 1, 2, 3},
+                          {1, 1, 1, 1},
+                          {1, 2, 3, 4, 5, 6}, {2},
+                          {3, 4, 5, 6, 7, 8}, 2);
+  Simple<DeviceType::OPENCL>(kernels::EltwiseType::SUM, {1, 1, 2, 3},
+                          {1, 1, 1, 1},
+                          {1, 2, 3, 4, 5, 6}, {2},
+                          {4, 6, 8, 10, 12, 14}, 2, {2, 1});
+  Simple<DeviceType::OPENCL>(kernels::EltwiseType::MAX, {1, 1, 2, 3},
+                          {1, 1, 1, 1},
+                          {1, 2, 3, 4, 5, 6}, {3},
+                          {3, 3, 3, 4, 5, 6}, 3);
+  Simple<DeviceType::OPENCL>(kernels::EltwiseType::MIN, {1, 1, 2, 3},
+                          {1, 1, 1, 1},
+                          {1, 2, 3, 4, 5, 6}, {3},
+                          {1, 2, 3, 3, 3, 3}, 3);
+  Simple<DeviceType::OPENCL>(kernels::EltwiseType::SQR_DIFF, {1, 1, 2, 3},
+                             {1, 1, 1, 1},
+                             {1, 2, 3, 4, 5, 6}, {3},
+                             {4, 1, 0, 1, 4, 9}, 3);
+  Simple<DeviceType::OPENCL>(kernels::EltwiseType::DIV, {1, 1, 2, 3},
+                          {1, 1, 1, 1},
+                          {1, 2, 3, 4, 5, 6}, {0.5},
+                          {2, 4, 6, 8, 10, 12}, 0.5);
 }
 
 namespace {
 template <DeviceType D, typename T>
 void RandomTest(const kernels::EltwiseType type,
-                const std::vector<index_t> &shape) {
+                const std::vector<index_t> &shape1,
+                const std::vector<index_t> &shape2) {
   testing::internal::LogToStderr();
   srand(time(NULL));
 
   // Construct graph
   OpsTestNet net;
 
+  bool is_divide = (type == kernels::EltwiseType::DIV);
+
   // Add input data
-  net.AddRandomInput<D, float>("Input1", shape);
-  net.AddRandomInput<D, float>("Input2", shape);
+  net.AddRandomInput<D, float>("Input1", shape1, true, is_divide);
+  net.AddRandomInput<D, float>("Input2", shape2, true, is_divide);
+
+
 
   OpDefBuilder("Eltwise", "EltwiseTest")
       .Input("Input1")
@@ -166,24 +317,110 @@ void RandomTest(const kernels::EltwiseType type,
 
 TEST_F(EltwiseOpTest, OPENCLRandomFloat) {
   RandomTest<DeviceType::OPENCL, float>(kernels::EltwiseType::PROD,
+                                        {3, 23, 37, 19},
                                         {3, 23, 37, 19});
   RandomTest<DeviceType::OPENCL, float>(kernels::EltwiseType::SUM,
+                                        {13, 32, 32, 64},
                                         {13, 32, 32, 64});
   RandomTest<DeviceType::OPENCL, float>(kernels::EltwiseType::MAX,
+                                        {3, 32, 32, 64},
                                         {3, 32, 32, 64});
   RandomTest<DeviceType::OPENCL, float>(kernels::EltwiseType::MIN,
+                                        {13, 32, 32, 64},
+                                        {13, 32, 32, 64});
+  RandomTest<DeviceType::OPENCL, float>(kernels::EltwiseType::DIV,
+                                        {13, 32, 32, 64},
+                                        {13, 32, 32, 64});
+  RandomTest<DeviceType::OPENCL, float>(kernels::EltwiseType::SQR_DIFF,
+                                        {13, 32, 32, 64},
                                         {13, 32, 32, 64});
+  RandomTest<DeviceType::OPENCL, float>(kernels::EltwiseType::PROD,
+                                        {3, 23, 37, 19},
+                                        {1, 1, 37, 19});
+  RandomTest<DeviceType::OPENCL, float>(kernels::EltwiseType::SUM,
+                                        {13, 32, 32, 64},
+                                        {1, 1, 32, 64});
+  RandomTest<DeviceType::OPENCL, float>(kernels::EltwiseType::MAX,
+                                        {3, 32, 32, 64},
+                                        {1, 1, 32, 64});
+  RandomTest<DeviceType::OPENCL, float>(kernels::EltwiseType::MIN,
+                                        {13, 32, 32, 64},
+                                        {1, 1, 32, 64});
+  RandomTest<DeviceType::OPENCL, float>(kernels::EltwiseType::DIV,
+                                        {13, 32, 32, 63},
+                                        {1, 1, 32, 63});
+  RandomTest<DeviceType::OPENCL, float>(kernels::EltwiseType::SQR_DIFF,
+                                        {13, 32, 32, 64},
+                                        {1, 1, 32, 64});
+  RandomTest<DeviceType::OPENCL, float>(kernels::EltwiseType::PROD,
+                                        {3, 23, 37, 19},
+                                        {1, 1, 1, 19});
+  RandomTest<DeviceType::OPENCL, float>(kernels::EltwiseType::SUM,
+                                        {13, 32, 32, 64},
+                                        {1, 1, 1, 64});
+  RandomTest<DeviceType::OPENCL, float>(kernels::EltwiseType::MAX,
+                                        {3, 32, 32, 64},
+                                        {1, 1, 1, 64});
+  RandomTest<DeviceType::OPENCL, float>(kernels::EltwiseType::MIN,
+                                        {13, 32, 32, 64},
+                                        {1, 1, 1, 64});
+  RandomTest<DeviceType::OPENCL, float>(kernels::EltwiseType::DIV,
+                                        {13, 32, 32, 64},
+                                        {1, 1, 1, 64});
+  RandomTest<DeviceType::OPENCL, float>(kernels::EltwiseType::SQR_DIFF,
+                                        {13, 32, 32, 64},
+                                        {1, 1, 1, 64});
 }
 
 TEST_F(EltwiseOpTest, OPENCLRandomHalf) {
   RandomTest<DeviceType::OPENCL, half>(kernels::EltwiseType::PROD,
+                                       {3, 23, 37, 19},
                                        {3, 23, 37, 19});
+  RandomTest<DeviceType::OPENCL, half>(kernels::EltwiseType::PROD,
+                                       {3, 23, 37, 19},
+                                       {1, 23, 37, 19});
+  RandomTest<DeviceType::OPENCL, half>(kernels::EltwiseType::PROD,
+                                       {3, 23, 37, 19},
+                                       {1, 1, 37, 19});
+  RandomTest<DeviceType::OPENCL, half>(kernels::EltwiseType::PROD,
+                                       {3, 23, 37, 19},
+                                       {1, 1, 1, 19});
   RandomTest<DeviceType::OPENCL, half>(kernels::EltwiseType::SUM,
-                                       {13, 32, 32, 64});
+                                       {13, 32, 32, 64},
+                                       {1, 1, 1, 1});
+  RandomTest<DeviceType::OPENCL, half>(kernels::EltwiseType::SUM,
+                                       {13, 32, 32, 64},
+                                       {1, 1, 1, 64});
+  RandomTest<DeviceType::OPENCL, half>(kernels::EltwiseType::SUM,
+                                       {13, 32, 32, 64},
+                                       {1, 1, 32, 64});
   RandomTest<DeviceType::OPENCL, half>(kernels::EltwiseType::MAX,
+                                       {3, 32, 32, 64},
                                        {3, 32, 32, 64});
+  RandomTest<DeviceType::OPENCL, half>(kernels::EltwiseType::MAX,
+                                       {3, 32, 32, 64},
+                                       {1, 1, 32, 64});
   RandomTest<DeviceType::OPENCL, half>(kernels::EltwiseType::MIN,
+                                       {13, 32, 32, 64},
+                                       {13, 32, 32, 64});
+  RandomTest<DeviceType::OPENCL, half>(kernels::EltwiseType::SQR_DIFF,
+                                       {13, 32, 32, 64},
+                                       {13, 32, 32, 64});
+  RandomTest<DeviceType::OPENCL, half>(kernels::EltwiseType::SQR_DIFF,
+                                       {13, 32, 32, 64},
+                                       {1, 1, 1, 64});
+  RandomTest<DeviceType::OPENCL, half>(kernels::EltwiseType::SQR_DIFF,
+                                       {13, 32, 32, 64},
+                                       {1, 1, 32, 64});
+  RandomTest<DeviceType::OPENCL, half>(kernels::EltwiseType::DIV,
+                                       {13, 32, 32, 64},
                                        {13, 32, 32, 64});
+  RandomTest<DeviceType::OPENCL, half>(kernels::EltwiseType::DIV,
+                                       {13, 32, 32, 64},
+                                       {1, 1, 1, 64});
+  RandomTest<DeviceType::OPENCL, half>(kernels::EltwiseType::DIV,
+                                       {13, 32, 32, 64},
+                                       {1, 1, 32, 64});
 }
 
 }  // namespace test

mace/ops/ops_test_util.h

@@ -150,7 +150,8 @@ class OpsTestNet {
   template<DeviceType D, typename T>
   void AddRandomInput(const std::string &name,
                       const std::vector<index_t> &shape,
-                      bool positive = true) {
+                      bool positive = true,
+                      bool truncate = false) {
     Tensor *input =
       ws_.CreateTensor(name, GetDeviceAllocator(D), DataTypeToEnum<T>::v());
     input->Resize(shape);
@@ -162,14 +163,24 @@ class OpsTestNet {
     std::normal_distribution<float> nd(0, 1);
     if (DataTypeToEnum<T>::value == DT_HALF) {
       std::generate(
-        input_data, input_data + input->size(), [&gen, &nd, positive] {
-          return half_float::half_cast<half>(positive ? std::abs(nd(gen))
-                                                      : nd(gen));
+        input_data, input_data + input->size(),
+        [&gen, &nd, positive, truncate] {
+          float d = nd(gen);
+          if (truncate) {
+            if (std::abs(d) > 100.f) d = 100.f;
+            if (std::abs(d) < 0.001f) d = 0.001f;
+          }
+          return half_float::half_cast<half>(positive ?std::abs(d) : d);
         });
     } else {
       std::generate(input_data, input_data + input->size(),
-                    [&gen, &nd, positive] {
-                      return positive ? std::abs(nd(gen)) : nd(gen);
+                    [&gen, &nd, positive, truncate] {
+                      float d = nd(gen);
+                      if (truncate) {
+                        if (std::abs(d) > 100.f) d = 100.f;
+                        if (std::abs(d) < 0.001f) d = 0.001f;
+                      }
+                      return (positive ?std::abs(d) : d);
                     });
     }
   }

mace/python/tools/tf_converter_lib.py

@@ -835,31 +835,19 @@ class TFConverter(object):
         arg.name = 'T'
         arg.i = self.dt
         op_def.name = op.name
-
-        if len(op.inputs) == 1:
-            op_def.type = "CWise"
-            op_def.input.extend([input.name for input in op.inputs])
-            x_arg = op_def.arg.add()
-            x_arg.name = 'x'
-            x_arg.f = 0
-        elif len(op.inputs) >= 2:
+        op_def.type = "Eltwise"
+        op_def.input.extend([input.name for input in op.inputs])
+        x_value = op.get_attr('x')
+        if len(op.inputs) >= 2:
             input_tensor0 = get_input_tensor(op, 0)
             input_tensor1 = get_input_tensor(op, 1)
-            if input_tensor0.shape == input_tensor1.shape:
-                op_def.type = "Eltwise"
-                op_def.input.extend([input.name for input in op.inputs])
-            else:
-                op_def.type = "CWise"
-                x_value = 0
-                if len(input_tensor1.shape) == 4:
-                    op_def.input.extend([op.inputs[1].name])
-                    x_value = get_input_tensor(op, 0).eval().astype(np.float32)
-                else:
-                    op_def.input.extend([op.inputs[0].name])
-                    x_value = get_input_tensor(op, 1).eval().astype(np.float32)
-                x_arg = op_def.arg.add()
-                x_arg.name = 'x'
-                x_arg.f = x_value
+            if len(input_tensor0) == 1:
+                x_value = input_tensor0.eval().astype(np.float32)
+            elif len(input_tensor1) == 1:
+                x_value = input_tensor1.eval().astype(np.float32)
+        x_arg = op_def.arg.add()
+        x_arg.name = 'x'
+        x_arg.f = x_value
         type_arg = op_def.arg.add()
         type_arg.name = 'type'
         type_arg.i = math_type_mode[math_type]