Move one hot to phi

paddle/fluid/framework/infershape_utils.cc

@@ -500,8 +500,22 @@ phi::InferMetaContext BuildInferMetaContext(InferShapeContext* ctx,
             "Unsupported attribute type is received when call "
             "InferShapeFunctor."));
       }
-    } else {
-      // do nothing
+    } else if (ctx->HasInput(attr_name)) {
+      // convert from data
+      if (attr_defs[i].type_index == std::type_index(typeid(int32_t))) {
+        if (ctx->IsRuntime()) {
+          const auto& infershape_inputs = ctx->GetInputVarPtrs(attr_name);
+          auto var_temp = BOOST_GET_CONST(Variable*, infershape_inputs[i]);
+          auto val = experimental::MakePhiScalarFromVar(*var_temp);
+          int32_t val_int = val.template to<int32_t>();
+          infer_meta_context.EmplaceBackAttr(val_int);
+        } else {
+          infer_meta_context.EmplaceBackAttr(-1);
+        }
+      } else {
+        PADDLE_THROW(platform::errors::Unimplemented(
+            "Get value from variable only support int yet"));
+      }
     }
   }
 

paddle/fluid/framework/operator.cc

@@ -2250,41 +2250,62 @@ void OperatorWithKernel::BuildPhiKernelContext(
       }
     } else {
       // TODO(chenweihang): support other attrs later
-      auto& attr = Attrs().at(attr_names[i]);
+      auto attr_it = attrs_.find(attr_names[i]);
       if (attr_defs[i].type_index == std::type_index(typeid(int))) {
-        pt_kernel_context->EmplaceBackAttr(BOOST_GET_CONST(int, attr));
+        if (attr_it == attrs_.end()) {
+          auto in_it = ctx.inputs.find(attr_names[i]);
+          if (in_it != ctx.inputs.end()) {
+            // get data from input
+            auto val = experimental::MakePhiScalarFromVar(*(in_it->second[0]));
+            int32_t val_int = val.template to<int32_t>();
+            pt_kernel_context->EmplaceBackAttr(val_int);
+          } else {
+            PADDLE_THROW(platform::errors::NotFound(
+                "can not find attribute `%s` both in attribute and input ",
+                attr_names[i]));
+          }
+        } else {
+          pt_kernel_context->EmplaceBackAttr(
+              BOOST_GET_CONST(int, attr_it->second));
+        }
       } else if (attr_defs[i].type_index == std::type_index(typeid(float))) {
-        pt_kernel_context->EmplaceBackAttr(BOOST_GET_CONST(float, attr));
+        pt_kernel_context->EmplaceBackAttr(
+            BOOST_GET_CONST(float, attr_it->second));
       } else if (attr_defs[i].type_index == std::type_index(typeid(bool))) {
-        pt_kernel_context->EmplaceBackAttr(BOOST_GET_CONST(bool, attr));
+        pt_kernel_context->EmplaceBackAttr(
+            BOOST_GET_CONST(bool, attr_it->second));
       } else if (attr_defs[i].type_index == std::type_index(typeid(int64_t))) {
-        pt_kernel_context->EmplaceBackAttr(BOOST_GET_CONST(int64_t, attr));
+        pt_kernel_context->EmplaceBackAttr(
+            BOOST_GET_CONST(int64_t, attr_it->second));
       } else if (attr_defs[i].type_index ==
                  std::type_index(typeid(std::string))) {
-        pt_kernel_context->EmplaceBackAttr(BOOST_GET_CONST(std::string, attr));
+        pt_kernel_context->EmplaceBackAttr(
+            BOOST_GET_CONST(std::string, attr_it->second));
       } else if (attr_defs[i].type_index ==
                  std::type_index(typeid(phi::DataType))) {
         auto data_type = paddle::framework::TransToPhiDataType(
             static_cast<framework::proto::VarType::Type>(
-                BOOST_GET_CONST(int, attr)));
+                BOOST_GET_CONST(int, attr_it->second)));
         pt_kernel_context->EmplaceBackAttr(data_type);
       } else if (attr_defs[i].type_index ==
                  std::type_index(typeid(std::vector<int64_t>))) {
-        if (std::type_index(attr.type()) ==
+        if (std::type_index(attr_it->second.type()) ==
             std::type_index(typeid(std::vector<int64_t>))) {
           pt_kernel_context->EmplaceBackAttr(
-              BOOST_GET_CONST(std::vector<int64_t>, attr));
-        } else if (std::type_index(attr.type()) ==
+              BOOST_GET_CONST(std::vector<int64_t>, attr_it->second));
+        } else if (std::type_index(attr_it->second.type()) ==
                    std::type_index(typeid(std::vector<int>))) {
           // Emplace Back Attr according to the type of Phi_Kernel args.
-          const auto& vector_int_attr = BOOST_GET_CONST(std::vector<int>, attr);
+          const auto& vector_int_attr =
+              BOOST_GET_CONST(std::vector<int>, attr_it->second);
           const std::vector<int64_t> vector_int64_attr(vector_int_attr.begin(),
                                                        vector_int_attr.end());
           pt_kernel_context->EmplaceBackAttr(vector_int64_attr);
         }
       } else if (attr_defs[i].type_index ==
                  std::type_index(typeid(std::vector<int32_t>))) {
-        const auto& vector_int_attr = BOOST_GET_CONST(std::vector<int>, attr);
+        const auto& vector_int_attr =
+            BOOST_GET_CONST(std::vector<int>, attr_it->second);
         pt_kernel_context->EmplaceBackAttr(vector_int_attr);
       } else {
         PADDLE_THROW(platform::errors::Unimplemented(

paddle/fluid/imperative/prepared_operator.h

@@ -419,6 +419,17 @@ void BuildDygraphPhiKernelContext(
             experimental::MakePhiScalarFromVar(ins_vector[0]->Var())));
       }
 
+    } else if (ins.find(attr_names[i]) != ins.end()) {
+      // deal tensor attr here
+      auto& ins_vector = ins.at(attr_names[i]);
+      auto tensor_attr =
+          experimental::MakePhiScalarFromVar(ins_vector[0]->Var());
+      if (attr_defs[i].type_index == std::type_index(typeid(int))) {
+        int val = tensor_attr.template to<int>();
+        kernel_ctx->EmplaceBackAttr(val);
+      } else {
+        PADDLE_THROW(platform::errors::Unimplemented("only support int here"));
+      }
     } else if (attr_defs[i].type_index ==
                std::type_index(typeid(std::vector<phi::Scalar>))) {
       auto& attr = GetAttr(attrs, default_attrs, attr_names[i]);
@@ -475,6 +486,7 @@ void BuildDygraphPhiKernelContext(
       }
     } else {
       // TODO(chenweihang): support other attrs later
+
       auto& attr = GetAttr(attrs, default_attrs, attr_names[i]);
       if (attr_defs[i].type_index == std::type_index(typeid(int))) {
         kernel_ctx->EmplaceBackAttr(BOOST_GET_CONST(int, attr));

paddle/fluid/operators/one_hot_v2_op.cc

@@ -12,36 +12,20 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include "paddle/fluid/operators/one_hot_v2_op.h"
 #include <string>
 #include <vector>
+#include "paddle/fluid/framework/infershape_utils.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/phi/core/infermeta_utils.h"
+#include "paddle/phi/infermeta/backward.h"
+#include "paddle/phi/infermeta/unary.h"
 
 namespace paddle {
 namespace operators {
 
 class OneHotV2Op : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
-  void InferShape(framework::InferShapeContext* ctx) const override {
-    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "one_hot_v2");
-    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "one_hot_v2");
-
-    auto x_dims = ctx->GetInputDim("X");
-    PADDLE_ENFORCE_GE(x_dims.size(), 1,
-                      platform::errors::InvalidArgument(
-                          "Rank of Input(X) should be at least 1."));
-
-    int depth = ctx->Attrs().Get<int>("depth");
-    if (ctx->HasInput("depth_tensor")) {
-      depth = -1;
-    }
-
-    auto out_dims_vec = phi::vectorize(x_dims);
-    out_dims_vec.push_back(depth);
-    auto out_dims = phi::make_ddim(out_dims_vec);
-    ctx->SetOutputDim("Out", out_dims);
-    ctx->ShareLoD("X", /* --> */ "Out");
-  }
 
  protected:
   framework::OpKernelType GetExpectedKernelType(
@@ -52,7 +36,7 @@ class OneHotV2Op : public framework::OperatorWithKernel {
   }
 
   framework::OpKernelType GetKernelTypeForVar(
-      const std::string& var_name, const Tensor& tensor,
+      const std::string& var_name, const framework::Tensor& tensor,
       const framework::OpKernelType& expected_kernel_type) const override {
     if (var_name == "depth_tensor") {
       return expected_kernel_type;
@@ -114,10 +98,12 @@ Out is a LoDTensor:
 }  // namespace paddle
 
 namespace ops = paddle::operators;
+
+DECLARE_INFER_SHAPE_FUNCTOR(one_hot_v2, OneHotInferShapeFunctor,
+                            PD_INFER_META(phi::OneHotRawInferMeta));
+
 REGISTER_OPERATOR(
     one_hot_v2, ops::OneHotV2Op, ops::OneHotV2OpMaker,
     paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
-    paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>);
-REGISTER_OP_CPU_KERNEL(
-    one_hot_v2, ops::OneHotV2Kernel<paddle::platform::CPUDeviceContext, int>,
-    ops::OneHotV2Kernel<paddle::platform::CPUDeviceContext, int64_t>);
+    paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>,
+    OneHotInferShapeFunctor);

paddle/fluid/operators/one_hot_v2_op_npu.cc

@@ -12,13 +12,14 @@ 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 "paddle/fluid/operators/one_hot_v2_op.h"
+#include "paddle/fluid/framework/op_registry.h"
 
 #include "paddle/fluid/platform/device/npu/npu_op_runner.h"
 
 namespace paddle {
 namespace operators {
 using Tensor = framework::Tensor;
+using LoDTensor = framework::LoDTensor;
 
 template <typename T>
 class OneHotV2NPUKernel : public framework::OpKernel<T> {

paddle/phi/core/compat/op_utils.h

@@ -55,6 +55,7 @@ const std::unordered_set<std::string> deprecated_op_names({"diag",
                                                            "expand_grad",
                                                            "expand_as_grad",
                                                            "sum",
+                                                           "one_hot",
                                                            "sum_grad",
                                                            "top_k",
                                                            "top_k_grad"});

paddle/phi/core/meta_tensor.cc

@@ -72,6 +72,10 @@ void MetaTensor::set_layout(DataLayout layout) {
 }
 
 void MetaTensor::share_lod(const MetaTensor& meta_tensor) {
+  if (meta_tensor.lod().size() == 0) {
+    // no need share
+    return;
+  }
   if (phi::DenseTensor::classof(tensor_)) {
     DenseTensorUtils::GetMutableMeta(static_cast<DenseTensor*>(tensor_))->lod =
         meta_tensor.lod();

paddle/phi/infermeta/unary.cc

@@ -1602,6 +1602,43 @@ void UnfoldInferMeta(const MetaTensor& x,
   out->set_dims(phi::make_ddim(out_dims));
 }
 
+void OneHotRawInferMeta(const MetaTensor& x,
+                        int32_t depth,
+                        DataType dtype,
+                        bool allow_out_of_range,
+                        MetaTensor* out) {
+  auto x_dims = x.dims();
+  PADDLE_ENFORCE_GE(
+      x_dims.size(),
+      1,
+      phi::errors::InvalidArgument("Rank of Input(X) should be at least 1."));
+
+  auto out_dims_vec = phi::vectorize(x_dims);
+  out_dims_vec.push_back(depth);
+  auto out_dims = phi::make_ddim(out_dims_vec);
+  out->set_dims(out_dims);
+  out->share_lod(x);
+  out->set_dtype(dtype);
+}
+
+void OneHotInferMeta(const MetaTensor& x,
+                     const Scalar& depth_t,
+                     MetaTensor* out) {
+  auto x_dims = x.dims();
+  PADDLE_ENFORCE_GE(
+      x_dims.size(),
+      1,
+      phi::errors::InvalidArgument("Rank of Input(X) should be at least 1."));
+
+  int depth = depth_t.to<int>();
+  auto out_dims_vec = phi::vectorize(x_dims);
+  out_dims_vec.push_back(depth);
+  auto out_dims = phi::make_ddim(out_dims_vec);
+  out->set_dims(out_dims);
+  out->share_lod(x);
+  out->set_dtype(phi::DataType::FLOAT32);
+}
+
 void WhereIndexInferMeta(const MetaTensor& condition, MetaTensor* out) {
   auto rank = condition.dims().size();
   PADDLE_ENFORCE_GE(

paddle/phi/infermeta/unary.h

@@ -228,6 +228,14 @@ void UnfoldInferMeta(const MetaTensor& x,
                      MetaTensor* out,
                      MetaConfig config = MetaConfig());
 
+void OneHotRawInferMeta(const MetaTensor& x,
+                        int32_t depth,
+                        DataType dtype,
+                        bool allow_out_of_range,
+                        MetaTensor* out);
+
+void OneHotInferMeta(const MetaTensor& x, const Scalar& depth, MetaTensor* out);
+
 void WhereIndexInferMeta(const MetaTensor& condition, MetaTensor* out);
 
 }  // namespace phi