[API] paddle.slogdet 返回值规范化

paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.cc

@@ -3325,15 +3325,17 @@ bool SlogdetOpInferSymbolicShape(
                                       "greater than or equal to 2."));
   infer_context->AddEqualCstr(x_shape[x_shape_size - 1],
                               x_shape[x_shape_size - 2]);
-  std::vector<symbol::DimExpr> out_shape = {2};
-  size_t additional_dims = x_shape.size() - 2;
-  for (size_t i = 0; i < additional_dims; i++) {
-    out_shape.push_back(x_shape[i]);
+  std::vector<symbol::DimExpr> out_dims;
+  if (x_shape_size > 2) {
+    out_dims.assign(x_shape.begin(), x_shape.end() - 2);
   }
   infer_context->SetShapeOrDataForValue(
       op->result(0),
-      symbol::ShapeOrDataDimExprs{
-          symbol::TensorShapeOrDataDimExprs(out_shape)});
+      symbol::ShapeOrDataDimExprs{symbol::TensorShapeOrDataDimExprs(out_dims)});
+  infer_context->SetShapeOrDataForValue(
+      op->result(1),
+      symbol::ShapeOrDataDimExprs{symbol::TensorShapeOrDataDimExprs(out_dims)});
+
   return true;
 }
 

paddle/fluid/pir/serialize_deserialize/patch/2.yaml

@@ -57,3 +57,9 @@ op_patches:
         type : pir::ArrayAttribute
         data :
           - type: pir::Int64Attribute
+  - op_name : pd_op.slogdet
+    actions:
+      - action : add_output
+        object : 1
+        type : pir::DenseTensorType
+        data : [pir::Float32Type,[-1],"NCHW",[],0]

paddle/phi/infermeta/unary.cc

@@ -6339,6 +6339,32 @@ void WeightQuantizeInferMeta(const MetaTensor& x,
   scale->set_dtype(x.dtype());
 }
 
+void SlogdetInferMeta(const MetaTensor& x,
+                      MetaTensor* sign,
+                      MetaTensor* logdet) {
+  DDim x_dims = x.dims();
+  int rank = x_dims.size();
+  PADDLE_ENFORCE_GE(rank,
+                    2,
+                    errors::InvalidArgument(
+                        "Input(X) should be at least a 2-D tensor, but got %u.",
+                        x_dims.size()));
+  PADDLE_ENFORCE_EQ(
+      x_dims[rank - 1],
+      x_dims[rank - 2],
+      errors::InvalidArgument("the input matrix should be square matrix."));
+  auto x_dtype = x.dtype();
+  auto x_layout = x.layout();
+  DDim out_dims = slice_ddim(x_dims, 0, rank - 2);
+  sign->set_dtype(x_dtype);
+  sign->set_layout(x_layout);
+  sign->set_dims(out_dims);
+
+  logdet->set_dtype(dtype::ToReal(x_dtype));
+  logdet->set_layout(x_layout);
+  logdet->set_dims(out_dims);
+}
+
 void ChannelShuffleInferMeta(const MetaTensor& x,
                              int groups,
                              const std::string& data_format,

paddle/phi/infermeta/unary.h

@@ -862,6 +862,10 @@ void SvdInferMeta(const MetaTensor& x,
                   MetaTensor* s,
                   MetaTensor* vh);
 
+void SlogdetInferMeta(const MetaTensor& x,
+                      MetaTensor* sign,
+                      MetaTensor* logdet);
+
 void TemporalShiftInferMeta(const MetaTensor& x,
                             int seg_num,
                             float shift_ratio,

paddle/phi/kernels/cpu/slogdeterminant_grad_kernel.cc

@@ -24,4 +24,8 @@ PD_REGISTER_KERNEL(slogdet_grad,
                    float,
                    double,
                    phi::dtype::complex<float>,
-                   phi::dtype::complex<double>) {}
+                   phi::dtype::complex<double>) {
+  phi::DataType real_dtype = phi::dtype::ToReal(kernel_key.dtype());
+  kernel->InputAt(2).SetDataType(real_dtype);
+  kernel->InputAt(4).SetDataType(real_dtype);
+}

paddle/phi/kernels/elementwise_multiply_kernel.h

@@ -36,4 +36,14 @@ DenseTensor Multiply(const Context& dev_ctx,
   return dense_out;
 }
 
+template <typename T, typename Context>
+void Multiply(const Context& dev_ctx,
+              const DenseTensor& x,
+              const DenseTensor& y,
+              DenseTensor* out) {
+  MetaTensor meta_out(out);
+  ElementwiseInferMeta(x, y, &meta_out);
+  MultiplyKernel<T, Context>(dev_ctx, x, y, out);
+}
+
 }  // namespace phi

paddle/phi/kernels/gpu/slogdeterminant_grad_kernel.cu

@@ -24,4 +24,8 @@ PD_REGISTER_KERNEL(slogdet_grad,
                    float,
                    double,
                    phi::dtype::complex<float>,
-                   phi::dtype::complex<double>) {}
+                   phi::dtype::complex<double>) {
+  phi::DataType real_dtype = phi::dtype::ToReal(kernel_key.dtype());
+  kernel->InputAt(2).SetDataType(real_dtype);
+  kernel->InputAt(4).SetDataType(real_dtype);
+}