【PaddlePaddle Hackathon No.78】add gather, gather_nd, scatter and scat…

…ter_nd op (#897)

【PaddlePaddle Hackathon No.78】add gather, gather_nd, scatter and scatter_nd op

cinn/frontend/net_builder.cc

@@ -349,6 +349,41 @@ Variable NetBuilder::ReluGrad(const Variable& lhs, const Variable& rhs) {
   return CustomInstr("relu_grad", {lhs, rhs}, {}).front();
 }
 
+Variable NetBuilder::Gather(const Variable& x, const Variable& index, const int& axis) {
+  return CustomInstr("gather", {x, index}, {{"axis", axis}}).front();
+}
+
+Variable NetBuilder::GatherNd(const Variable& x, const Variable& index, const std::vector<int>& axes) {
+  return CustomInstr("gather_nd", {x, index}, {{"axes", axes}}).front();
+}
+
+Variable NetBuilder::Scatter(const Variable& src, const Variable& index, const Variable& out, const int& axis) {
+  return CustomInstr("scatter", {src, index, out}, {{"axis", axis}}).front();
+}
+Variable NetBuilder::Scatter(const Variable& src,
+                             const Variable& index,
+                             const std::vector<int>& shape,
+                             const float& default_value,
+                             const int& axis) {
+  auto out = FillConstant(shape, default_value, UniqName("fill_constant"), "float", false);
+  return Scatter(src, index, out, axis);
+}
+
+Variable NetBuilder::ScatterNd(const Variable& src,
+                               const Variable& index,
+                               const Variable& out,
+                               const std::vector<int>& axes) {
+  return CustomInstr("scatter_nd", {src, index, out}, {{"axes", axes}}).front();
+}
+Variable NetBuilder::ScatterNd(const Variable& src,
+                               const Variable& index,
+                               const std::vector<int>& shape,
+                               const float& default_value,
+                               const std::vector<int>& axes) {
+  auto out = FillConstant(shape, default_value, UniqName("fill_constant"), "float", false);
+  return ScatterNd(src, index, out, axes);
+}
+
 Variable NetBuilder::Cast(const Variable& operand, const std::string& dtype) {
   if (operand->type == common::Str2Type(dtype)) {
     return Identity(operand);

cinn/frontend/net_builder.h

@@ -262,6 +262,27 @@ class NetBuilder {
    */
   Variable Clip(const std::vector<Variable>& x, const float& max, const float& min);
 
+  Variable Gather(const Variable& x, const Variable& index, const int& axis = 0);
+
+  Variable GatherNd(const Variable& x, const Variable& index, const std::vector<int>& axes = {});
+
+  Variable Scatter(const Variable& src, const Variable& index, const Variable& out, const int& axis = 0);
+  Variable Scatter(const Variable& src,
+                   const Variable& index,
+                   const std::vector<int>& shape,
+                   const float& default_value = 0,
+                   const int& axis            = 0);
+
+  Variable ScatterNd(const Variable& src,
+                     const Variable& index,
+                     const Variable& out,
+                     const std::vector<int>& axes = {});
+  Variable ScatterNd(const Variable& src,
+                     const Variable& index,
+                     const std::vector<int>& shape,
+                     const float& default_value   = 0,
+                     const std::vector<int>& axes = {});
+
   /**
    * @brief This operator checks if all `x` and `y` satisfy the condition: `|x - y| <= atol + rtol * |y|`
    * @param x The first variable.
@@ -793,7 +814,7 @@ class NetBuilder {
                                       const std::string& data_layout = "NCHW");
 
   /**
-   * @brief Sort Variable x along the given axis. The original Variable x will not be changed.
+   * @brief Sort Variable x along the given axis and return sorted index. The original Variable x will not be changed.
    * @param operand The variable that will be sorted.
    * @param axis Specify the axis to operate on the input. Default: 0.
    * @param is_ascend Sort mode.
@@ -803,7 +824,8 @@ class NetBuilder {
   Variable ArgSort(const Variable& operand, const int& axis, const bool& is_ascend = true);
 
   /**
-   * @brief Sort Variable x along the given axis. The original Variable x will not be changed.
+   * @brief Sort Variable x along the given axis and return sorted variable. The original Variable x will not be
+   * changed.
    * @param operand The variable that will be sorted.
    * @param axis Specify the axis to operate on the input. Default: 0.
    * @param is_ascend Sort mode.

cinn/frontend/net_builder_test.cc

@@ -246,6 +246,258 @@ TEST(net_build, program_execute_clip) {
   }
 }
 
+TEST(net_build, program_execute_gather) {
+  const int B     = 4;
+  const int H_IN1 = 11;
+  const int H_IN2 = 14;
+
+  NetBuilder builder("net_builder");
+  Placeholder input1 = builder.CreateInput(Float(32), {B, H_IN1}, "In1");
+  Placeholder input2 = builder.CreateInput(Int(32), {B, H_IN2}, "In2");
+  Variable output    = builder.Gather(input1, input2, 1);
+  auto program       = builder.Build();
+
+  Target target = common::DefaultHostTarget();
+
+  auto graph = std::make_shared<hlir::framework::Graph>(program, target);
+  auto scope = BuildScope(target, graph);
+  hlir::framework::GraphCompiler gc(target, scope, graph);
+  auto runtime_program = gc.Build();
+
+  scope->Var<hlir::framework::Tensor>(std::string(input1.id()));
+  scope->Var<hlir::framework::Tensor>(std::string(input2.id()));
+  scope->Var<hlir::framework::Tensor>(std::string(output->id));
+
+  auto input1_tensor = scope->GetTensor(std::string(input1.id()));
+  SetRandData<float>(input1_tensor, target);
+  float* input1_data = input1_tensor->mutable_data<float>(target);
+
+  auto input2_tensor = scope->GetTensor(std::string(input2.id()));
+  SetRandInt(input2_tensor, target);
+  int* input2_data = input2_tensor->mutable_data<int>(target);
+  memset(input2_data, 0, sizeof(int) * B * H_IN2);
+
+  runtime_program->Execute();
+
+  auto output_tensor                   = scope->GetTensor(std::string(output->id));
+  const std::vector<int>& output_shape = output_tensor->shape().data();
+  EXPECT_EQ(output_tensor->type(), Float(32));
+  EXPECT_EQ(output_shape.size(), 2UL);
+  EXPECT_EQ(output_shape[0], B);
+  EXPECT_EQ(output_shape[1], H_IN2);
+
+  float* output_data = output_tensor->mutable_data<float>(target);
+  VLOG(6) << "Visualize output_data";
+  for (int b = 0; b < B; ++b) {
+    for (int h = 0; h < H_IN2; ++h) {
+      std::string line;
+      int index      = h + H_IN2 * b;
+      float in_data  = input1_data[input2_data[index] + H_IN1 * b];
+      float out_data = output_data[index];
+      line += (std::to_string(out_data) + ", ");
+      EXPECT_EQ(in_data, out_data);
+      VLOG(6) << line;
+    }
+  }
+}
+
+TEST(net_build, program_execute_gather_nd) {
+  const int B     = 4;
+  const int H_IN1 = 11;
+  const int H_IN2 = 14;
+
+  NetBuilder builder("net_builder");
+  Placeholder input1 = builder.CreateInput(Float(32), {B, H_IN1}, "In1");
+  Placeholder input2 = builder.CreateInput(Int(32), {B, H_IN2, 1}, "In2");
+  Variable output    = builder.GatherNd(input1, input2, {1});
+  auto program       = builder.Build();
+
+  Target target = common::DefaultHostTarget();
+
+  auto graph = std::make_shared<hlir::framework::Graph>(program, target);
+  auto scope = BuildScope(target, graph);
+  hlir::framework::GraphCompiler gc(target, scope, graph);
+  auto runtime_program = gc.Build();
+
+  scope->Var<hlir::framework::Tensor>(std::string(input1.id()));
+  scope->Var<hlir::framework::Tensor>(std::string(input2.id()));
+  scope->Var<hlir::framework::Tensor>(std::string(output->id));
+
+  auto input1_tensor = scope->GetTensor(std::string(input1.id()));
+  SetRandData<float>(input1_tensor, target);
+  float* input1_data = input1_tensor->mutable_data<float>(target);
+
+  auto input2_tensor = scope->GetTensor(std::string(input2.id()));
+  SetRandInt(input2_tensor, target);
+  int* input2_data = input2_tensor->mutable_data<int>(target);
+
+  runtime_program->Execute();
+
+  auto output_tensor                   = scope->GetTensor(std::string(output->id));
+  const std::vector<int>& output_shape = output_tensor->shape().data();
+  EXPECT_EQ(output_tensor->type(), Float(32));
+  EXPECT_EQ(output_shape.size(), 2UL);
+  EXPECT_EQ(output_shape[0], B);
+  EXPECT_EQ(output_shape[1], H_IN2);
+
+  float* output_data = output_tensor->mutable_data<float>(target);
+  VLOG(6) << "Visualize output_data";
+  for (int b = 0; b < B; ++b) {
+    for (int h = 0; h < H_IN2; ++h) {
+      std::string line;
+      int index      = h + H_IN2 * b;
+      float in_data  = input1_data[input2_data[index] + H_IN1 * b];
+      float out_data = output_data[index];
+      line += (std::to_string(out_data) + ", ");
+      EXPECT_EQ(in_data, out_data);
+      VLOG(6) << line;
+    }
+  }
+}
+
+TEST(net_build, program_execute_scatter) {
+  const float default_value = 3.14;
+  const int B               = 3;
+  const int H_IN            = 4;
+  const int H_OUT           = 11;
+
+  NetBuilder builder("net_builder");
+  Placeholder input1 = builder.CreateInput(Float(32), {B, H_IN}, "In1");
+  Placeholder input2 = builder.CreateInput(Int(32), {B, H_IN}, "In2");
+  Variable output    = builder.Scatter(input1, input2, {B, H_OUT}, default_value, 1);
+  auto program       = builder.Build();
+
+  Target target = common::DefaultHostTarget();
+
+  auto graph = std::make_shared<hlir::framework::Graph>(program, target);
+  auto scope = BuildScope(target, graph);
+  hlir::framework::GraphCompiler gc(target, scope, graph);
+  auto runtime_program = gc.Build();
+
+  scope->Var<hlir::framework::Tensor>(std::string(input1.id()));
+  scope->Var<hlir::framework::Tensor>(std::string(input2.id()));
+  scope->Var<hlir::framework::Tensor>(std::string(output->id));
+
+  auto input1_tensor = scope->GetTensor(std::string(input1.id()));
+  SetRandData<float>(input1_tensor, target);
+  float* input1_data = input1_tensor->mutable_data<float>(target);
+
+  auto input2_tensor = scope->GetTensor(std::string(input2.id()));
+  SetRandInt(input2_tensor, target);
+  int* input2_data = input2_tensor->mutable_data<int>(target);
+  memset(input2_data, 0, sizeof(int) * B * H_IN);
+
+  runtime_program->Execute();
+
+  auto output_tensor                   = scope->GetTensor(std::string(output->id));
+  const std::vector<int>& output_shape = output_tensor->shape().data();
+  EXPECT_EQ(output_tensor->type(), Float(32));
+  EXPECT_EQ(output_shape.size(), 2UL);
+  EXPECT_EQ(output_shape[0], B);
+  EXPECT_EQ(output_shape[1], H_OUT);
+
+  float true_data[B * H_OUT];
+  for (int b = 0; b < B; ++b) {
+    for (int h = 0; h < H_OUT; ++h) {
+      int index        = h + H_OUT * b;
+      true_data[index] = default_value;
+    }
+  }
+  for (int b = 0; b < B; ++b) {
+    for (int h = 0; h < H_IN; ++h) {
+      int index                                 = h + H_IN * b;
+      true_data[input2_data[index] + H_OUT * b] = input1_data[index];
+      std::cout << index << " " << input2_data[index] + H_OUT * b << " " << true_data[input2_data[index] + H_OUT * b];
+    }
+  }
+
+  float* output_data = output_tensor->mutable_data<float>(target);
+  VLOG(6) << "Visualize output_data";
+  for (int b = 0; b < B; ++b) {
+    for (int h = 0; h < H_OUT; ++h) {
+      std::string line;
+      int index      = h + H_OUT * b;
+      float t_data   = true_data[index];
+      float out_data = output_data[index];
+      line += (std::to_string(out_data) + ", ");
+      EXPECT_EQ(t_data, out_data);
+      VLOG(6) << line;
+    }
+  }
+}
+
+TEST(net_build, program_execute_scatter_nd) {
+  const float default_value = 3.14;
+  const int B               = 3;
+  const int H_IN            = 4;
+  const int H_OUT           = 11;
+
+  NetBuilder builder("net_builder");
+  Placeholder input1 = builder.CreateInput(Float(32), {B, H_IN}, "In1");
+  Placeholder input2 = builder.CreateInput(Int(32), {B, H_IN, 1}, "In2");
+  Variable output    = builder.ScatterNd(input1, input2, {B, H_OUT}, default_value, {1});
+  auto program       = builder.Build();
+
+  Target target = common::DefaultHostTarget();
+
+  auto graph = std::make_shared<hlir::framework::Graph>(program, target);
+  auto scope = BuildScope(target, graph);
+  hlir::framework::GraphCompiler gc(target, scope, graph);
+  auto runtime_program = gc.Build();
+
+  scope->Var<hlir::framework::Tensor>(std::string(input1.id()));
+  scope->Var<hlir::framework::Tensor>(std::string(input2.id()));
+  scope->Var<hlir::framework::Tensor>(std::string(output->id));
+
+  auto input1_tensor = scope->GetTensor(std::string(input1.id()));
+  SetRandData<float>(input1_tensor, target);
+
+  auto input2_tensor = scope->GetTensor(std::string(input2.id()));
+  SetRandInt(input2_tensor, target);
+
+  runtime_program->Execute();
+
+  int* input2_data;
+  float* input1_data;
+  input2_data = input2_tensor->mutable_data<int>(target);
+  input1_data = input1_tensor->mutable_data<float>(target);
+
+  auto output_tensor                   = scope->GetTensor(std::string(output->id));
+  const std::vector<int>& output_shape = output_tensor->shape().data();
+  EXPECT_EQ(output_tensor->type(), Float(32));
+  EXPECT_EQ(output_shape.size(), 2UL);
+  EXPECT_EQ(output_shape[0], B);
+  EXPECT_EQ(output_shape[1], H_OUT);
+
+  float true_data[B * H_OUT];
+  for (int b = 0; b < B; ++b) {
+    for (int h = 0; h < H_OUT; ++h) {
+      int index        = h + H_OUT * b;
+      true_data[index] = default_value;
+    }
+  }
+  for (int b = 0; b < B; ++b) {
+    for (int h = 0; h < H_IN; ++h) {
+      int index                                 = h + H_IN * b;
+      true_data[input2_data[index] + H_OUT * b] = input1_data[index];
+    }
+  }
+
+  float* output_data = output_tensor->mutable_data<float>(target);
+  VLOG(6) << "Visualize output_data";
+  for (int b = 0; b < B; ++b) {
+    for (int h = 0; h < H_OUT; ++h) {
+      std::string line;
+      int index      = h + H_OUT * b;
+      float t_data   = true_data[index];
+      float out_data = output_data[index];
+      line += (std::to_string(out_data) + ", ");
+      EXPECT_EQ(t_data, out_data);
+      VLOG(6) << line;
+    }
+  }
+}
+
 TEST(net_build, program_execute_cast) {
   const int B = 4;
   const int H = 7;
@@ -266,7 +518,7 @@ TEST(net_build, program_execute_cast) {
   scope->Var<hlir::framework::Tensor>(std::string(output->id));
 
   auto input_tensor = scope->GetTensor(std::string(input.id()));
-  SetRandData<int>(input_tensor, target);
+  SetRandInt(input_tensor, target);
   int* input_data = input_tensor->mutable_data<int>(target);
 
   runtime_program->Execute();

cinn/hlir/op/contrib/CMakeLists.txt

@@ -1,6 +1,8 @@
 core_gather_headers()
 
 gather_srcs(cinnapi_src SRCS
+        gather.cc
+        scatter.cc
         cast.cc
         squeeze.cc
         clip.cc
@@ -11,6 +13,8 @@ gather_srcs(cinnapi_src SRCS
 
 cc_test(test_cast SRCS cast_test.cc DEPS cinncore)
 cc_test(test_squeeze SRCS squeeze_test.cc DEPS cinncore)
+cc_test(test_gather SRCS gather_test.cc DEPS cinncore)
+cc_test(test_scatter SRCS scatter_test.cc DEPS cinncore)
 cc_test(test_clip SRCS clip_test.cc DEPS cinncore)
 cc_test(test_sort SRCS sort_test.cc DEPS cinncore)
 cc_test(test_arange SRCS arange_test.cc DEPS cinncore)