suport v2.1.0

csrc/macros.h

+#pragma once
+
+#ifdef _WIN32
+#if defined(torchscatter_EXPORTS)
+#define SCATTER_API __declspec(dllexport)
+#else
+#define SCATTER_API __declspec(dllimport)
+#endif
+#else
+#define SCATTER_API
+#endif
+
+#if (defined __cpp_inline_variables) || __cplusplus >= 201703L
+#define SCATTER_INLINE_VARIABLE inline
+#else
+#ifdef _MSC_VER
+#define SCATTER_INLINE_VARIABLE __declspec(selectany)
+#else
+#define SCATTER_INLINE_VARIABLE __attribute__((weak))
+#endif
+#endif

csrc/scatter.cpp

+#ifdef WITH_PYTHON
 #include <Python.h>
+#endif
+
 #include <torch/script.h>
 
 #include "cpu/scatter_cpu.h"
+#include "macros.h"
 #include "utils.h"
 
-#ifdef WITH_HIP
-#include "hip/scatter_hip.h"
+#ifdef WITH_CUDA
+#include "cuda/scatter_cuda.h"
 #endif
 
 #ifdef _WIN32
-#ifdef WITH_HIP
+#ifdef WITH_PYTHON
+#ifdef WITH_CUDA
 PyMODINIT_FUNC PyInit__scatter_cuda(void) { return NULL; }
 #else
 PyMODINIT_FUNC PyInit__scatter_cpu(void) { return NULL; }
 #endif
 #endif
+#endif
 
 torch::Tensor broadcast(torch::Tensor src, torch::Tensor other, int64_t dim) {
   if (src.dim() == 1)
@@ -31,7 +37,7 @@ scatter_fw(torch::Tensor src, torch::Tensor index, int64_t dim,
            torch::optional<torch::Tensor> optional_out,
            torch::optional<int64_t> dim_size, std::string reduce) {
   if (src.device().is_cuda()) {
-#ifdef WITH_HIP
+#ifdef WITH_CUDA
     return scatter_cuda(src, index, dim, optional_out, dim_size, reduce);
 #else
     AT_ERROR("Not compiled with CUDA support");
@@ -226,25 +232,28 @@ public:
   }
 };
 
-torch::Tensor scatter_sum(torch::Tensor src, torch::Tensor index, int64_t dim,
+SCATTER_API torch::Tensor
+scatter_sum(torch::Tensor src, torch::Tensor index, int64_t dim,
             torch::optional<torch::Tensor> optional_out,
             torch::optional<int64_t> dim_size) {
   return ScatterSum::apply(src, index, dim, optional_out, dim_size)[0];
 }
 
-torch::Tensor scatter_mul(torch::Tensor src, torch::Tensor index, int64_t dim,
+SCATTER_API torch::Tensor
+scatter_mul(torch::Tensor src, torch::Tensor index, int64_t dim,
             torch::optional<torch::Tensor> optional_out,
             torch::optional<int64_t> dim_size) {
   return ScatterMul::apply(src, index, dim, optional_out, dim_size)[0];
 }
 
-torch::Tensor scatter_mean(torch::Tensor src, torch::Tensor index, int64_t dim,
+SCATTER_API torch::Tensor
+scatter_mean(torch::Tensor src, torch::Tensor index, int64_t dim,
              torch::optional<torch::Tensor> optional_out,
              torch::optional<int64_t> dim_size) {
   return ScatterMean::apply(src, index, dim, optional_out, dim_size)[0];
 }
 
-std::tuple<torch::Tensor, torch::Tensor>
+SCATTER_API std::tuple<torch::Tensor, torch::Tensor>
 scatter_min(torch::Tensor src, torch::Tensor index, int64_t dim,
             torch::optional<torch::Tensor> optional_out,
             torch::optional<int64_t> dim_size) {
@@ -252,7 +261,7 @@ scatter_min(torch::Tensor src, torch::Tensor index, int64_t dim,
   return std::make_tuple(result[0], result[1]);
 }
 
-std::tuple<torch::Tensor, torch::Tensor>
+SCATTER_API std::tuple<torch::Tensor, torch::Tensor>
 scatter_max(torch::Tensor src, torch::Tensor index, int64_t dim,
             torch::optional<torch::Tensor> optional_out,
             torch::optional<int64_t> dim_size) {

csrc/scatter.h

 #pragma once
 
-#include <torch/extension.h>
+#include "extensions.h"
 
-int64_t cuda_version();
+namespace scatter {
+SCATTER_API int64_t cuda_version() noexcept;
 
-torch::Tensor scatter_sum(torch::Tensor src, torch::Tensor index, int64_t dim,
+namespace detail {
+SCATTER_INLINE_VARIABLE int64_t _cuda_version = cuda_version();
+} // namespace detail
+} // namespace scatter
+
+SCATTER_API torch::Tensor
+scatter_sum(torch::Tensor src, torch::Tensor index, int64_t dim,
+            torch::optional<torch::Tensor> optional_out,
+            torch::optional<int64_t> dim_size);
+
+SCATTER_API torch::Tensor
+scatter_mul(torch::Tensor src, torch::Tensor index, int64_t dim,
             torch::optional<torch::Tensor> optional_out,
             torch::optional<int64_t> dim_size);
 
-torch::Tensor scatter_mean(torch::Tensor src, torch::Tensor index, int64_t dim,
+SCATTER_API torch::Tensor
+scatter_mean(torch::Tensor src, torch::Tensor index, int64_t dim,
              torch::optional<torch::Tensor> optional_out,
              torch::optional<int64_t> dim_size);
 
-std::tuple<torch::Tensor, torch::Tensor>
+SCATTER_API std::tuple<torch::Tensor, torch::Tensor>
 scatter_min(torch::Tensor src, torch::Tensor index, int64_t dim,
             torch::optional<torch::Tensor> optional_out,
             torch::optional<int64_t> dim_size);
 
-std::tuple<torch::Tensor, torch::Tensor>
+SCATTER_API std::tuple<torch::Tensor, torch::Tensor>
 scatter_max(torch::Tensor src, torch::Tensor index, int64_t dim,
             torch::optional<torch::Tensor> optional_out,
             torch::optional<int64_t> dim_size);
 
-torch::Tensor segment_sum_coo(torch::Tensor src, torch::Tensor index,
+SCATTER_API torch::Tensor
+segment_sum_coo(torch::Tensor src, torch::Tensor index,
                 torch::optional<torch::Tensor> optional_out,
                 torch::optional<int64_t> dim_size);
 
-torch::Tensor segment_mean_coo(torch::Tensor src, torch::Tensor index,
+SCATTER_API torch::Tensor
+segment_mean_coo(torch::Tensor src, torch::Tensor index,
                  torch::optional<torch::Tensor> optional_out,
                  torch::optional<int64_t> dim_size);
 
-std::tuple<torch::Tensor, torch::Tensor>
+SCATTER_API std::tuple<torch::Tensor, torch::Tensor>
 segment_min_coo(torch::Tensor src, torch::Tensor index,
                 torch::optional<torch::Tensor> optional_out,
                 torch::optional<int64_t> dim_size);
 
-std::tuple<torch::Tensor, torch::Tensor>
+SCATTER_API std::tuple<torch::Tensor, torch::Tensor>
 segment_max_coo(torch::Tensor src, torch::Tensor index,
                 torch::optional<torch::Tensor> optional_out,
                 torch::optional<int64_t> dim_size);
 
-torch::Tensor gather_coo(torch::Tensor src, torch::Tensor index,
+SCATTER_API torch::Tensor
+gather_coo(torch::Tensor src, torch::Tensor index,
            torch::optional<torch::Tensor> optional_out);
 
-torch::Tensor segment_sum_csr(torch::Tensor src, torch::Tensor indptr,
+SCATTER_API torch::Tensor
+segment_sum_csr(torch::Tensor src, torch::Tensor indptr,
                 torch::optional<torch::Tensor> optional_out);
 
-torch::Tensor segment_mean_csr(torch::Tensor src, torch::Tensor indptr,
+SCATTER_API torch::Tensor
+segment_mean_csr(torch::Tensor src, torch::Tensor indptr,
                  torch::optional<torch::Tensor> optional_out);
 
-std::tuple<torch::Tensor, torch::Tensor>
+SCATTER_API std::tuple<torch::Tensor, torch::Tensor>
 segment_min_csr(torch::Tensor src, torch::Tensor indptr,
                 torch::optional<torch::Tensor> optional_out);
 
-std::tuple<torch::Tensor, torch::Tensor>
+SCATTER_API std::tuple<torch::Tensor, torch::Tensor>
 segment_max_csr(torch::Tensor src, torch::Tensor indptr,
                 torch::optional<torch::Tensor> optional_out);
 
-torch::Tensor gather_csr(torch::Tensor src, torch::Tensor indptr,
+SCATTER_API torch::Tensor
+gather_csr(torch::Tensor src, torch::Tensor indptr,
            torch::optional<torch::Tensor> optional_out);

csrc/scatter_hip.cpp

+// !!! This is a file automatically generated by hipify!!!
+#include <ATen/dtk_macros.h>
+#ifdef WITH_PYTHON
+#include <Python.h>
+#endif
+
+#include <torch/script.h>
+
+#include "cpu/scatter_cpu.h"
+#include "macros.h"
+#include "utils.h"
+
+#ifdef WITH_CUDA
+#include "hip/scatter_cuda.h"
+#endif
+
+#ifdef _WIN32
+#ifdef WITH_PYTHON
+#ifdef WITH_CUDA
+PyMODINIT_FUNC PyInit__scatter_cuda(void) { return NULL; }
+#else
+PyMODINIT_FUNC PyInit__scatter_cpu(void) { return NULL; }
+#endif
+#endif
+#endif
+
+torch::Tensor broadcast(torch::Tensor src, torch::Tensor other, int64_t dim) {
+  if (src.dim() == 1)
+    for (auto i = 0; i < dim; i++)
+      src = src.unsqueeze(0);
+  for (auto i = src.dim(); i < other.dim(); i++)
+    src = src.unsqueeze(-1);
+  src = src.expand(other.sizes().vec());
+  return src;
+}
+
+std::tuple<torch::Tensor, torch::optional<torch::Tensor>>
+scatter_fw(torch::Tensor src, torch::Tensor index, int64_t dim,
+           torch::optional<torch::Tensor> optional_out,
+           torch::optional<int64_t> dim_size, std::string reduce) {
+  if (src.device().is_cuda()) {
+#ifdef WITH_CUDA
+    return scatter_cuda(src, index, dim, optional_out, dim_size, reduce);
+#else
+    AT_ERROR("Not compiled with CUDA support");
+#endif
+  } else {
+    return scatter_cpu(src, index, dim, optional_out, dim_size, reduce);
+  }
+}
+
+using torch::autograd::AutogradContext;
+using torch::autograd::Variable;
+using torch::autograd::variable_list;
+
+class ScatterSum : public torch::autograd::Function<ScatterSum> {
+public:
+  static variable_list forward(AutogradContext *ctx, Variable src,
+                               Variable index, int64_t dim,
+                               torch::optional<Variable> optional_out,
+                               torch::optional<int64_t> dim_size) {
+    dim = dim < 0 ? src.dim() + dim : dim;
+    ctx->saved_data["dim"] = dim;
+    ctx->saved_data["src_shape"] = src.sizes();
+    index = broadcast(index, src, dim);
+    auto result = scatter_fw(src, index, dim, optional_out, dim_size, "sum");
+    auto out = std::get<0>(result);
+    ctx->save_for_backward({index});
+    if (optional_out.has_value())
+      ctx->mark_dirty({optional_out.value()});
+    return {out};
+  }
+
+  static variable_list backward(AutogradContext *ctx, variable_list grad_outs) {
+    auto grad_out = grad_outs[0];
+    auto saved = ctx->get_saved_variables();
+    auto index = saved[0];
+    auto dim = ctx->saved_data["dim"].toInt();
+    auto src_shape = list2vec(ctx->saved_data["src_shape"].toIntList());
+    auto grad_in = torch::gather(grad_out, dim, index, false);
+    return {grad_in, Variable(), Variable(), Variable(), Variable()};
+  }
+};
+
+class ScatterMul : public torch::autograd::Function<ScatterMul> {
+public:
+  static variable_list forward(AutogradContext *ctx, Variable src,
+                               Variable index, int64_t dim,
+                               torch::optional<Variable> optional_out,
+                               torch::optional<int64_t> dim_size) {
+    dim = dim < 0 ? src.dim() + dim : dim;
+    ctx->saved_data["dim"] = dim;
+    ctx->saved_data["src_shape"] = src.sizes();
+    index = broadcast(index, src, dim);
+    auto result = scatter_fw(src, index, dim, optional_out, dim_size, "mul");
+    auto out = std::get<0>(result);
+    ctx->save_for_backward({src, index, out});
+    if (optional_out.has_value())
+      ctx->mark_dirty({optional_out.value()});
+    return {out};
+  }
+
+  static variable_list backward(AutogradContext *ctx, variable_list grad_outs) {
+    auto grad_out = grad_outs[0];
+    auto saved = ctx->get_saved_variables();
+    auto src = saved[0];
+    auto index = saved[1];
+    auto out = saved[2];
+    auto dim = ctx->saved_data["dim"].toInt();
+    auto src_shape = list2vec(ctx->saved_data["src_shape"].toIntList());
+    auto grad_in = torch::gather(grad_out * out, dim, index, false).div_(src);
+    grad_in.masked_fill_(grad_in.isnan(), 0);
+    return {grad_in, Variable(), Variable(), Variable(), Variable()};
+  }
+};
+
+class ScatterMean : public torch::autograd::Function<ScatterMean> {
+public:
+  static variable_list forward(AutogradContext *ctx, Variable src,
+                               Variable index, int64_t dim,
+                               torch::optional<Variable> optional_out,
+                               torch::optional<int64_t> dim_size) {
+    dim = dim < 0 ? src.dim() + dim : dim;
+    ctx->saved_data["dim"] = dim;
+    ctx->saved_data["src_shape"] = src.sizes();
+
+    auto old_index = index;
+
+    index = broadcast(index, src, dim);
+    auto result = scatter_fw(src, index, dim, optional_out, dim_size, "sum");
+    auto out = std::get<0>(result);
+
+    auto ones = torch::ones(old_index.sizes(), src.options());
+    result = scatter_fw(ones, old_index,
+                        old_index.dim() <= dim ? old_index.dim() - 1 : dim,
+                        torch::nullopt, out.size(dim), "sum");
+    auto count = std::get<0>(result);
+    count.masked_fill_(count < 1, 1);
+    count = broadcast(count, out, dim);
+    if (out.is_floating_point())
+      out.true_divide_(count);
+    else
+      out.div_(count, "floor");
+
+    ctx->save_for_backward({index, count});
+    if (optional_out.has_value())
+      ctx->mark_dirty({optional_out.value()});
+    return {out};
+  }
+
+  static variable_list backward(AutogradContext *ctx, variable_list grad_outs) {
+    auto grad_out = grad_outs[0];
+    auto saved = ctx->get_saved_variables();
+    auto index = saved[0];
+    auto count = saved[1];
+    auto dim = ctx->saved_data["dim"].toInt();
+    auto src_shape = list2vec(ctx->saved_data["src_shape"].toIntList());
+    count = torch::gather(count, dim, index, false);
+    auto grad_in = torch::gather(grad_out, dim, index, false);
+    grad_in.true_divide_(count);
+    return {grad_in, Variable(), Variable(), Variable(), Variable()};
+  }
+};
+
+class ScatterMin : public torch::autograd::Function<ScatterMin> {
+public:
+  static variable_list forward(AutogradContext *ctx, Variable src,
+                               Variable index, int64_t dim,
+                               torch::optional<Variable> optional_out,
+                               torch::optional<int64_t> dim_size) {
+    dim = dim < 0 ? src.dim() + dim : dim;
+    ctx->saved_data["dim"] = dim;
+    ctx->saved_data["src_shape"] = src.sizes();
+
+    index = broadcast(index, src, dim);
+    auto result = scatter_fw(src, index, dim, optional_out, dim_size, "min");
+    auto out = std::get<0>(result);
+    auto arg_out = std::get<1>(result).value();
+    ctx->save_for_backward({index, arg_out});
+    ctx->mark_non_differentiable({arg_out});
+    if (optional_out.has_value())
+      ctx->mark_dirty({optional_out.value()});
+    return {out, arg_out};
+  }
+
+  static variable_list backward(AutogradContext *ctx, variable_list grad_outs) {
+    auto grad_out = grad_outs[0];
+    auto saved = ctx->get_saved_variables();
+    auto index = saved[0];
+    auto arg_out = saved[1];
+    auto dim = ctx->saved_data["dim"].toInt();
+    auto src_shape = list2vec(ctx->saved_data["src_shape"].toIntList());
+    src_shape[dim] += 1;
+    auto grad_in = torch::zeros(src_shape, grad_out.options());
+    grad_in.scatter_(dim, arg_out, grad_out);
+    grad_in = grad_in.narrow(dim, 0, src_shape[dim] - 1);
+    return {grad_in, Variable(), Variable(), Variable(), Variable()};
+  }
+};
+
+class ScatterMax : public torch::autograd::Function<ScatterMax> {
+public:
+  static variable_list forward(AutogradContext *ctx, Variable src,
+                               Variable index, int64_t dim,
+                               torch::optional<Variable> optional_out,
+                               torch::optional<int64_t> dim_size) {
+    dim = dim < 0 ? src.dim() + dim : dim;
+    ctx->saved_data["dim"] = dim;
+    ctx->saved_data["src_shape"] = src.sizes();
+
+    index = broadcast(index, src, dim);
+    auto result = scatter_fw(src, index, dim, optional_out, dim_size, "max");
+    auto out = std::get<0>(result);
+    auto arg_out = std::get<1>(result).value();
+    ctx->save_for_backward({index, arg_out});
+    ctx->mark_non_differentiable({arg_out});
+    if (optional_out.has_value())
+      ctx->mark_dirty({optional_out.value()});
+    return {out, arg_out};
+  }
+
+  static variable_list backward(AutogradContext *ctx, variable_list grad_outs) {
+    auto grad_out = grad_outs[0];
+    auto saved = ctx->get_saved_variables();
+    auto index = saved[0];
+    auto arg_out = saved[1];
+    auto dim = ctx->saved_data["dim"].toInt();
+    auto src_shape = list2vec(ctx->saved_data["src_shape"].toIntList());
+    src_shape[dim] += 1;
+    auto grad_in = torch::zeros(src_shape, grad_out.options());
+    grad_in.scatter_(dim, arg_out, grad_out);
+    grad_in = grad_in.narrow(dim, 0, src_shape[dim] - 1);
+    return {grad_in, Variable(), Variable(), Variable(), Variable()};
+  }
+};
+
+SCATTER_API torch::Tensor
+scatter_sum(torch::Tensor src, torch::Tensor index, int64_t dim,
+            torch::optional<torch::Tensor> optional_out,
+            torch::optional<int64_t> dim_size) {
+  return ScatterSum::apply(src, index, dim, optional_out, dim_size)[0];
+}
+
+SCATTER_API torch::Tensor
+scatter_mul(torch::Tensor src, torch::Tensor index, int64_t dim,
+            torch::optional<torch::Tensor> optional_out,
+            torch::optional<int64_t> dim_size) {
+  return ScatterMul::apply(src, index, dim, optional_out, dim_size)[0];
+}
+
+SCATTER_API torch::Tensor
+scatter_mean(torch::Tensor src, torch::Tensor index, int64_t dim,
+             torch::optional<torch::Tensor> optional_out,
+             torch::optional<int64_t> dim_size) {
+  return ScatterMean::apply(src, index, dim, optional_out, dim_size)[0];
+}
+
+SCATTER_API std::tuple<torch::Tensor, torch::Tensor>
+scatter_min(torch::Tensor src, torch::Tensor index, int64_t dim,
+            torch::optional<torch::Tensor> optional_out,
+            torch::optional<int64_t> dim_size) {
+  auto result = ScatterMin::apply(src, index, dim, optional_out, dim_size);
+  return std::make_tuple(result[0], result[1]);
+}
+
+SCATTER_API std::tuple<torch::Tensor, torch::Tensor>
+scatter_max(torch::Tensor src, torch::Tensor index, int64_t dim,
+            torch::optional<torch::Tensor> optional_out,
+            torch::optional<int64_t> dim_size) {
+  auto result = ScatterMax::apply(src, index, dim, optional_out, dim_size);
+  return std::make_tuple(result[0], result[1]);
+}
+
+static auto registry = torch::RegisterOperators()
+                           .op("torch_scatter::scatter_sum", &scatter_sum)
+                           .op("torch_scatter::scatter_mul", &scatter_mul)
+                           .op("torch_scatter::scatter_mean", &scatter_mean)
+                           .op("torch_scatter::scatter_min", &scatter_min)
+                           .op("torch_scatter::scatter_max", &scatter_max);

csrc/segment_coo.cpp

+#ifdef WITH_PYTHON
 #include <Python.h>
+#endif
+
 #include <torch/script.h>
 
 #include "cpu/segment_coo_cpu.h"
+#include "macros.h"
 #include "utils.h"
 
-#ifdef WITH_HIP
-#include "hip/segment_coo_hip.h"
+#ifdef WITH_CUDA
+#include "cuda/segment_coo_cuda.h"
 #endif
 
 #ifdef _WIN32
-#ifdef WITH_HIP
+#ifdef WITH_PYTHON
+#ifdef WITH_CUDA
 PyMODINIT_FUNC PyInit__segment_coo_cuda(void) { return NULL; }
 #else
 PyMODINIT_FUNC PyInit__segment_coo_cpu(void) { return NULL; }
 #endif
 #endif
+#endif
 
 std::tuple<torch::Tensor, torch::optional<torch::Tensor>>
 segment_coo_fw(torch::Tensor src, torch::Tensor index,
                torch::optional<torch::Tensor> optional_out,
                torch::optional<int64_t> dim_size, std::string reduce) {
   if (src.device().is_cuda()) {
-#ifdef WITH_HIP
+#ifdef WITH_CUDA
     return segment_coo_cuda(src, index, optional_out, dim_size, reduce);
 #else
     AT_ERROR("Not compiled with CUDA support");
@@ -34,7 +40,7 @@ segment_coo_fw(torch::Tensor src, torch::Tensor index,
 torch::Tensor gather_coo_fw(torch::Tensor src, torch::Tensor index,
                             torch::optional<torch::Tensor> optional_out) {
   if (src.device().is_cuda()) {
-#ifdef WITH_HIP
+#ifdef WITH_CUDA
     return gather_coo_cuda(src, index, optional_out);
 #else
     AT_ERROR("Not compiled with CUDA support");
@@ -195,19 +201,21 @@ public:
   }
 };
 
-torch::Tensor segment_sum_coo(torch::Tensor src, torch::Tensor index,
+SCATTER_API torch::Tensor
+segment_sum_coo(torch::Tensor src, torch::Tensor index,
                 torch::optional<torch::Tensor> optional_out,
                 torch::optional<int64_t> dim_size) {
   return SegmentSumCOO::apply(src, index, optional_out, dim_size)[0];
 }
 
-torch::Tensor segment_mean_coo(torch::Tensor src, torch::Tensor index,
+SCATTER_API torch::Tensor
+segment_mean_coo(torch::Tensor src, torch::Tensor index,
                  torch::optional<torch::Tensor> optional_out,
                  torch::optional<int64_t> dim_size) {
   return SegmentMeanCOO::apply(src, index, optional_out, dim_size)[0];
 }
 
-std::tuple<torch::Tensor, torch::Tensor>
+SCATTER_API std::tuple<torch::Tensor, torch::Tensor>
 segment_min_coo(torch::Tensor src, torch::Tensor index,
                 torch::optional<torch::Tensor> optional_out,
                 torch::optional<int64_t> dim_size) {
@@ -215,7 +223,7 @@ segment_min_coo(torch::Tensor src, torch::Tensor index,
   return std::make_tuple(result[0], result[1]);
 }
 
-std::tuple<torch::Tensor, torch::Tensor>
+SCATTER_API std::tuple<torch::Tensor, torch::Tensor>
 segment_max_coo(torch::Tensor src, torch::Tensor index,
                 torch::optional<torch::Tensor> optional_out,
                 torch::optional<int64_t> dim_size) {
@@ -223,7 +231,8 @@ segment_max_coo(torch::Tensor src, torch::Tensor index,
   return std::make_tuple(result[0], result[1]);
 }
 
-torch::Tensor gather_coo(torch::Tensor src, torch::Tensor index,
+SCATTER_API torch::Tensor
+gather_coo(torch::Tensor src, torch::Tensor index,
            torch::optional<torch::Tensor> optional_out) {
   return GatherCOO::apply(src, index, optional_out)[0];
 }

csrc/segment_coo_hip.cpp

+// !!! This is a file automatically generated by hipify!!!
+#include <ATen/dtk_macros.h>
+#ifdef WITH_PYTHON
+#include <Python.h>
+#endif
+
+#include <torch/script.h>
+
+#include "cpu/segment_coo_cpu.h"
+#include "macros.h"
+#include "utils.h"
+
+#ifdef WITH_CUDA
+#include "hip/segment_coo_cuda.h"
+#endif
+
+#ifdef _WIN32
+#ifdef WITH_PYTHON
+#ifdef WITH_CUDA
+PyMODINIT_FUNC PyInit__segment_coo_cuda(void) { return NULL; }
+#else
+PyMODINIT_FUNC PyInit__segment_coo_cpu(void) { return NULL; }
+#endif
+#endif
+#endif
+
+std::tuple<torch::Tensor, torch::optional<torch::Tensor>>
+segment_coo_fw(torch::Tensor src, torch::Tensor index,
+               torch::optional<torch::Tensor> optional_out,
+               torch::optional<int64_t> dim_size, std::string reduce) {
+  if (src.device().is_cuda()) {
+#ifdef WITH_CUDA
+    return segment_coo_cuda(src, index, optional_out, dim_size, reduce);
+#else
+    AT_ERROR("Not compiled with CUDA support");
+#endif
+  } else {
+    return segment_coo_cpu(src, index, optional_out, dim_size, reduce);
+  }
+}
+
+torch::Tensor gather_coo_fw(torch::Tensor src, torch::Tensor index,
+                            torch::optional<torch::Tensor> optional_out) {
+  if (src.device().is_cuda()) {
+#ifdef WITH_CUDA
+    return gather_coo_cuda(src, index, optional_out);
+#else
+    AT_ERROR("Not compiled with CUDA support");
+#endif
+  } else {
+    return gather_coo_cpu(src, index, optional_out);
+  }
+}
+
+using torch::autograd::AutogradContext;
+using torch::autograd::Variable;
+using torch::autograd::variable_list;
+
+class SegmentSumCOO : public torch::autograd::Function<SegmentSumCOO> {
+public:
+  static variable_list forward(AutogradContext *ctx, Variable src,
+                               Variable index,
+                               torch::optional<Variable> optional_out,
+                               torch::optional<int64_t> dim_size) {
+    ctx->saved_data["src_shape"] = src.sizes();
+    auto result = segment_coo_fw(src, index, optional_out, dim_size, "sum");
+    auto out = std::get<0>(result);
+    ctx->save_for_backward({index});
+    if (optional_out.has_value())
+      ctx->mark_dirty({optional_out.value()});
+    return {out};
+  }
+
+  static variable_list backward(AutogradContext *ctx, variable_list grad_outs) {
+    auto grad_out = grad_outs[0];
+    auto saved = ctx->get_saved_variables();
+    auto index = saved[0];
+    auto src_shape = list2vec(ctx->saved_data["src_shape"].toIntList());
+    auto grad_in = torch::empty(src_shape, grad_out.options());
+    gather_coo_fw(grad_out, index, grad_in);
+    return {grad_in, Variable(), Variable(), Variable()};
+  }
+};
+
+class SegmentMeanCOO : public torch::autograd::Function<SegmentMeanCOO> {
+public:
+  static variable_list forward(AutogradContext *ctx, Variable src,
+                               Variable index,
+                               torch::optional<Variable> optional_out,
+                               torch::optional<int64_t> dim_size) {
+    ctx->saved_data["src_shape"] = src.sizes();
+    auto result = segment_coo_fw(src, index, optional_out, dim_size, "mean");
+    auto out = std::get<0>(result);
+    auto count = std::get<1>(result).value();
+    ctx->save_for_backward({index, count});
+    if (optional_out.has_value())
+      ctx->mark_dirty({optional_out.value()});
+    return {out};
+  }
+
+  static variable_list backward(AutogradContext *ctx, variable_list grad_outs) {
+    auto grad_out = grad_outs[0];
+    auto saved = ctx->get_saved_variables();
+    auto index = saved[0];
+    auto count = saved[1];
+    auto src_shape = list2vec(ctx->saved_data["src_shape"].toIntList());
+    auto grad_in = torch::empty(src_shape, grad_out.options());
+    gather_coo_fw(grad_out, index, grad_in);
+    count = gather_coo_fw(count, index, torch::nullopt);
+    for (auto i = 0; i < grad_out.dim() - index.dim(); i++)
+      count = count.unsqueeze(-1);
+    grad_in.true_divide_(count);
+    return {grad_in, Variable(), Variable(), Variable()};
+  }
+};
+
+class SegmentMinCOO : public torch::autograd::Function<SegmentMinCOO> {
+public:
+  static variable_list forward(AutogradContext *ctx, Variable src,
+                               Variable index,
+                               torch::optional<Variable> optional_out,
+                               torch::optional<int64_t> dim_size) {
+    ctx->saved_data["src_shape"] = src.sizes();
+    auto result = segment_coo_fw(src, index, optional_out, dim_size, "min");
+    auto out = std::get<0>(result);
+    auto arg_out = std::get<1>(result).value();
+    ctx->save_for_backward({index, arg_out});
+    ctx->mark_non_differentiable({arg_out});
+    if (optional_out.has_value())
+      ctx->mark_dirty({optional_out.value()});
+    return {out, arg_out};
+  }
+
+  static variable_list backward(AutogradContext *ctx, variable_list grad_outs) {
+    auto grad_out = grad_outs[0];
+    auto saved = ctx->get_saved_variables();
+    auto index = saved[0];
+    auto arg_out = saved[1];
+    auto src_shape = list2vec(ctx->saved_data["src_shape"].toIntList());
+    src_shape[index.dim() - 1] += 1;
+    auto grad_in = torch::zeros(src_shape, grad_out.options());
+    grad_in.scatter_(index.dim() - 1, arg_out, grad_out);
+    grad_in =
+        grad_in.narrow(index.dim() - 1, 0, src_shape[index.dim() - 1] - 1);
+    return {grad_in, Variable(), Variable(), Variable()};
+  }
+};
+
+class SegmentMaxCOO : public torch::autograd::Function<SegmentMaxCOO> {
+public:
+  static variable_list forward(AutogradContext *ctx, Variable src,
+                               Variable index,
+                               torch::optional<Variable> optional_out,
+                               torch::optional<int64_t> dim_size) {
+    ctx->saved_data["src_shape"] = src.sizes();
+    auto result = segment_coo_fw(src, index, optional_out, dim_size, "max");
+    auto out = std::get<0>(result);
+    auto arg_out = std::get<1>(result).value();
+    ctx->save_for_backward({index, arg_out});
+    ctx->mark_non_differentiable({arg_out});
+    if (optional_out.has_value())
+      ctx->mark_dirty({optional_out.value()});
+    return {out, arg_out};
+  }
+
+  static variable_list backward(AutogradContext *ctx, variable_list grad_outs) {
+    auto grad_out = grad_outs[0];
+    auto saved = ctx->get_saved_variables();
+    auto index = saved[0];
+    auto arg_out = saved[1];
+    auto src_shape = list2vec(ctx->saved_data["src_shape"].toIntList());
+    src_shape[index.dim() - 1] += 1;
+    auto grad_in = torch::zeros(src_shape, grad_out.options());
+    grad_in.scatter_(index.dim() - 1, arg_out, grad_out);
+    grad_in =
+        grad_in.narrow(index.dim() - 1, 0, src_shape[index.dim() - 1] - 1);
+    return {grad_in, Variable(), Variable(), Variable()};
+  }
+};
+
+class GatherCOO : public torch::autograd::Function<GatherCOO> {
+public:
+  static variable_list forward(AutogradContext *ctx, Variable src,
+                               Variable index,
+                               torch::optional<Variable> optional_out) {
+    ctx->saved_data["src_shape"] = src.sizes();
+    auto out = gather_coo_fw(src, index, optional_out);
+    ctx->save_for_backward({index});
+    if (optional_out.has_value())
+      ctx->mark_dirty({optional_out.value()});
+    return {out};
+  }
+
+  static variable_list backward(AutogradContext *ctx, variable_list grad_outs) {
+    auto grad_out = grad_outs[0];
+    auto saved = ctx->get_saved_variables();
+    auto index = saved[0];
+    auto src_shape = list2vec(ctx->saved_data["src_shape"].toIntList());
+
+    auto grad_in = torch::zeros(src_shape, grad_out.options());
+    segment_coo_fw(grad_out, index, grad_in, torch::nullopt, "sum");
+    return {grad_in, Variable(), Variable()};
+  }
+};
+
+SCATTER_API torch::Tensor
+segment_sum_coo(torch::Tensor src, torch::Tensor index,
+                torch::optional<torch::Tensor> optional_out,
+                torch::optional<int64_t> dim_size) {
+  return SegmentSumCOO::apply(src, index, optional_out, dim_size)[0];
+}
+
+SCATTER_API torch::Tensor
+segment_mean_coo(torch::Tensor src, torch::Tensor index,
+                 torch::optional<torch::Tensor> optional_out,
+                 torch::optional<int64_t> dim_size) {
+  return SegmentMeanCOO::apply(src, index, optional_out, dim_size)[0];
+}
+
+SCATTER_API std::tuple<torch::Tensor, torch::Tensor>
+segment_min_coo(torch::Tensor src, torch::Tensor index,
+                torch::optional<torch::Tensor> optional_out,
+                torch::optional<int64_t> dim_size) {
+  auto result = SegmentMinCOO::apply(src, index, optional_out, dim_size);
+  return std::make_tuple(result[0], result[1]);
+}
+
+SCATTER_API std::tuple<torch::Tensor, torch::Tensor>
+segment_max_coo(torch::Tensor src, torch::Tensor index,
+                torch::optional<torch::Tensor> optional_out,
+                torch::optional<int64_t> dim_size) {
+  auto result = SegmentMaxCOO::apply(src, index, optional_out, dim_size);
+  return std::make_tuple(result[0], result[1]);
+}
+
+SCATTER_API torch::Tensor
+gather_coo(torch::Tensor src, torch::Tensor index,
+           torch::optional<torch::Tensor> optional_out) {
+  return GatherCOO::apply(src, index, optional_out)[0];
+}
+
+static auto registry =
+    torch::RegisterOperators()
+        .op("torch_scatter::segment_sum_coo", &segment_sum_coo)
+        .op("torch_scatter::segment_mean_coo", &segment_mean_coo)
+        .op("torch_scatter::segment_min_coo", &segment_min_coo)
+        .op("torch_scatter::segment_max_coo", &segment_max_coo)
+        .op("torch_scatter::gather_coo", &gather_coo);

csrc/segment_csr.cpp

+#ifdef WITH_PYTHON
 #include <Python.h>
+#endif
+
 #include <torch/script.h>
 
 #include "cpu/segment_csr_cpu.h"
+#include "macros.h"
 #include "utils.h"
 
-#ifdef WITH_HIP
-#include "hip/segment_csr_hip.h"
+#ifdef WITH_CUDA
+#include "cuda/segment_csr_cuda.h"
 #endif
 
 #ifdef _WIN32
-#ifdef WITH_HIP
+#ifdef WITH_PYTHON
+#ifdef WITH_CUDA
 PyMODINIT_FUNC PyInit__segment_csr_cuda(void) { return NULL; }
 #else
 PyMODINIT_FUNC PyInit__segment_csr_cpu(void) { return NULL; }
 #endif
 #endif
+#endif
 
 std::tuple<torch::Tensor, torch::optional<torch::Tensor>>
 segment_csr_fw(torch::Tensor src, torch::Tensor indptr,
                torch::optional<torch::Tensor> optional_out,
                std::string reduce) {
   if (src.device().is_cuda()) {
-#ifdef WITH_HIP
+#ifdef WITH_CUDA
     return segment_csr_cuda(src, indptr, optional_out, reduce);
 #else
     AT_ERROR("Not compiled with CUDA support");
@@ -34,7 +40,7 @@ segment_csr_fw(torch::Tensor src, torch::Tensor indptr,
 torch::Tensor gather_csr_fw(torch::Tensor src, torch::Tensor indptr,
                             torch::optional<torch::Tensor> optional_out) {
   if (src.device().is_cuda()) {
-#ifdef WITH_HIP
+#ifdef WITH_CUDA
     return gather_csr_cuda(src, indptr, optional_out);
 #else
     AT_ERROR("Not compiled with CUDA support");
@@ -192,31 +198,34 @@ public:
   }
 };
 
-torch::Tensor segment_sum_csr(torch::Tensor src, torch::Tensor indptr,
+SCATTER_API torch::Tensor
+segment_sum_csr(torch::Tensor src, torch::Tensor indptr,
                 torch::optional<torch::Tensor> optional_out) {
   return SegmentSumCSR::apply(src, indptr, optional_out)[0];
 }
 
-torch::Tensor segment_mean_csr(torch::Tensor src, torch::Tensor indptr,
+SCATTER_API torch::Tensor
+segment_mean_csr(torch::Tensor src, torch::Tensor indptr,
                  torch::optional<torch::Tensor> optional_out) {
   return SegmentMeanCSR::apply(src, indptr, optional_out)[0];
 }
 
-std::tuple<torch::Tensor, torch::Tensor>
+SCATTER_API std::tuple<torch::Tensor, torch::Tensor>
 segment_min_csr(torch::Tensor src, torch::Tensor indptr,
                 torch::optional<torch::Tensor> optional_out) {
   auto result = SegmentMinCSR::apply(src, indptr, optional_out);
   return std::make_tuple(result[0], result[1]);
 }
 
-std::tuple<torch::Tensor, torch::Tensor>
+SCATTER_API std::tuple<torch::Tensor, torch::Tensor>
 segment_max_csr(torch::Tensor src, torch::Tensor indptr,
                 torch::optional<torch::Tensor> optional_out) {
   auto result = SegmentMaxCSR::apply(src, indptr, optional_out);
   return std::make_tuple(result[0], result[1]);
 }
 
-torch::Tensor gather_csr(torch::Tensor src, torch::Tensor indptr,
+SCATTER_API torch::Tensor
+gather_csr(torch::Tensor src, torch::Tensor indptr,
            torch::optional<torch::Tensor> optional_out) {
   return GatherCSR::apply(src, indptr, optional_out)[0];
 }

csrc/segment_csr_hip.cpp

+// !!! This is a file automatically generated by hipify!!!
+#include <ATen/dtk_macros.h>
+#ifdef WITH_PYTHON
+#include <Python.h>
+#endif
+
+#include <torch/script.h>
+
+#include "cpu/segment_csr_cpu.h"
+#include "macros.h"
+#include "utils.h"
+
+#ifdef WITH_CUDA
+#include "hip/segment_csr_cuda.h"
+#endif
+
+#ifdef _WIN32
+#ifdef WITH_PYTHON
+#ifdef WITH_CUDA
+PyMODINIT_FUNC PyInit__segment_csr_cuda(void) { return NULL; }
+#else
+PyMODINIT_FUNC PyInit__segment_csr_cpu(void) { return NULL; }
+#endif
+#endif
+#endif
+
+std::tuple<torch::Tensor, torch::optional<torch::Tensor>>
+segment_csr_fw(torch::Tensor src, torch::Tensor indptr,
+               torch::optional<torch::Tensor> optional_out,
+               std::string reduce) {
+  if (src.device().is_cuda()) {
+#ifdef WITH_CUDA
+    return segment_csr_cuda(src, indptr, optional_out, reduce);
+#else
+    AT_ERROR("Not compiled with CUDA support");
+#endif
+  } else {
+    return segment_csr_cpu(src, indptr, optional_out, reduce);
+  }
+}
+
+torch::Tensor gather_csr_fw(torch::Tensor src, torch::Tensor indptr,
+                            torch::optional<torch::Tensor> optional_out) {
+  if (src.device().is_cuda()) {
+#ifdef WITH_CUDA
+    return gather_csr_cuda(src, indptr, optional_out);
+#else
+    AT_ERROR("Not compiled with CUDA support");
+#endif
+  } else {
+    return gather_csr_cpu(src, indptr, optional_out);
+  }
+}
+
+using torch::autograd::AutogradContext;
+using torch::autograd::Variable;
+using torch::autograd::variable_list;
+
+class SegmentSumCSR : public torch::autograd::Function<SegmentSumCSR> {
+public:
+  static variable_list forward(AutogradContext *ctx, Variable src,
+                               Variable indptr,
+                               torch::optional<Variable> optional_out) {
+    ctx->saved_data["src_shape"] = src.sizes();
+    auto out = std::get<0>(segment_csr_fw(src, indptr, optional_out, "sum"));
+    ctx->save_for_backward({indptr});
+    if (optional_out.has_value())
+      ctx->mark_dirty({optional_out.value()});
+    return {out};
+  }
+
+  static variable_list backward(AutogradContext *ctx, variable_list grad_outs) {
+    auto grad_out = grad_outs[0];
+    auto saved = ctx->get_saved_variables();
+    auto indptr = saved[0];
+    auto src_shape = list2vec(ctx->saved_data["src_shape"].toIntList());
+    auto grad_in = torch::empty(src_shape, grad_out.options());
+    gather_csr_fw(grad_out, indptr, grad_in);
+    return {grad_in, Variable(), Variable()};
+  }
+};
+
+class SegmentMeanCSR : public torch::autograd::Function<SegmentMeanCSR> {
+public:
+  static variable_list forward(AutogradContext *ctx, Variable src,
+                               Variable indptr,
+                               torch::optional<Variable> optional_out) {
+    ctx->saved_data["src_shape"] = src.sizes();
+    auto out = std::get<0>(segment_csr_fw(src, indptr, optional_out, "mean"));
+    ctx->save_for_backward({indptr});
+    if (optional_out.has_value())
+      ctx->mark_dirty({optional_out.value()});
+    return {out};
+  }
+
+  static variable_list backward(AutogradContext *ctx, variable_list grad_outs) {
+    auto grad_out = grad_outs[0];
+    auto saved = ctx->get_saved_variables();
+    auto indptr = saved[0];
+    auto src_shape = list2vec(ctx->saved_data["src_shape"].toIntList());
+    auto grad_in = torch::empty(src_shape, grad_out.options());
+    if (grad_in.numel() > 0) {
+      gather_csr_fw(grad_out, indptr, grad_in);
+      auto indptr1 = indptr.narrow(-1, 0, indptr.size(-1) - 1);
+      auto indptr2 = indptr.narrow(-1, 1, indptr.size(-1) - 1);
+      auto count = (indptr2 - indptr1).to(grad_in.options());
+      count = gather_csr_fw(count, indptr, torch::nullopt);
+      for (auto i = 0; i < grad_out.dim() - indptr.dim(); i++)
+        count = count.unsqueeze(-1);
+      grad_in.true_divide_(count);
+    }
+    return {grad_in, Variable(), Variable()};
+  }
+};
+
+class SegmentMinCSR : public torch::autograd::Function<SegmentMinCSR> {
+public:
+  static variable_list forward(AutogradContext *ctx, Variable src,
+                               Variable indptr,
+                               torch::optional<Variable> optional_out) {
+    ctx->saved_data["src_shape"] = src.sizes();
+    auto result = segment_csr_fw(src, indptr, optional_out, "min");
+    auto out = std::get<0>(result);
+    auto arg_out = std::get<1>(result).value();
+    ctx->save_for_backward({indptr, arg_out});
+    ctx->mark_non_differentiable({arg_out});
+    if (optional_out.has_value())
+      ctx->mark_dirty({optional_out.value()});
+    return {out, arg_out};
+  }
+
+  static variable_list backward(AutogradContext *ctx, variable_list grad_outs) {
+    auto grad_out = grad_outs[0];
+    auto saved = ctx->get_saved_variables();
+    auto indptr = saved[0];
+    auto arg_out = saved[1];
+    auto src_shape = list2vec(ctx->saved_data["src_shape"].toIntList());
+    src_shape[indptr.dim() - 1] += 1;
+    auto grad_in = torch::zeros(src_shape, grad_out.options());
+    grad_in.scatter_(indptr.dim() - 1, arg_out, grad_out);
+    grad_in =
+        grad_in.narrow(indptr.dim() - 1, 0, src_shape[indptr.dim() - 1] - 1);
+    return {grad_in, Variable(), Variable()};
+  }
+};
+
+class SegmentMaxCSR : public torch::autograd::Function<SegmentMaxCSR> {
+public:
+  static variable_list forward(AutogradContext *ctx, Variable src,
+                               Variable indptr,
+                               torch::optional<Variable> optional_out) {
+    ctx->saved_data["src_shape"] = src.sizes();
+    auto result = segment_csr_fw(src, indptr, optional_out, "max");
+    auto out = std::get<0>(result);
+    auto arg_out = std::get<1>(result).value();
+    ctx->save_for_backward({indptr, arg_out});
+    ctx->mark_non_differentiable({arg_out});
+    if (optional_out.has_value())
+      ctx->mark_dirty({optional_out.value()});
+    return {out, arg_out};
+  }
+
+  static variable_list backward(AutogradContext *ctx, variable_list grad_outs) {
+    auto grad_out = grad_outs[0];
+    auto saved = ctx->get_saved_variables();
+    auto indptr = saved[0];
+    auto arg_out = saved[1];
+    auto src_shape = list2vec(ctx->saved_data["src_shape"].toIntList());
+    src_shape[indptr.dim() - 1] += 1;
+    auto grad_in = torch::zeros(src_shape, grad_out.options());
+    grad_in.scatter_(indptr.dim() - 1, arg_out, grad_out);
+    grad_in =
+        grad_in.narrow(indptr.dim() - 1, 0, src_shape[indptr.dim() - 1] - 1);
+    return {grad_in, Variable(), Variable()};
+  }
+};
+
+class GatherCSR : public torch::autograd::Function<GatherCSR> {
+public:
+  static variable_list forward(AutogradContext *ctx, Variable src,
+                               Variable indptr,
+                               torch::optional<Variable> optional_out) {
+    ctx->saved_data["src_shape"] = src.sizes();
+    auto out = gather_csr_fw(src, indptr, optional_out);
+    ctx->save_for_backward({indptr});
+    if (optional_out.has_value())
+      ctx->mark_dirty({optional_out.value()});
+    return {out};
+  }
+
+  static variable_list backward(AutogradContext *ctx, variable_list grad_outs) {
+    auto grad_out = grad_outs[0];
+    auto saved = ctx->get_saved_variables();
+    auto indptr = saved[0];
+    auto src_shape = list2vec(ctx->saved_data["src_shape"].toIntList());
+
+    auto grad_in = torch::empty(src_shape, grad_out.options());
+    segment_csr_fw(grad_out, indptr, grad_in, "sum");
+    return {grad_in, Variable(), Variable()};
+  }
+};
+
+SCATTER_API torch::Tensor
+segment_sum_csr(torch::Tensor src, torch::Tensor indptr,
+                torch::optional<torch::Tensor> optional_out) {
+  return SegmentSumCSR::apply(src, indptr, optional_out)[0];
+}
+
+SCATTER_API torch::Tensor
+segment_mean_csr(torch::Tensor src, torch::Tensor indptr,
+                 torch::optional<torch::Tensor> optional_out) {
+  return SegmentMeanCSR::apply(src, indptr, optional_out)[0];
+}
+
+SCATTER_API std::tuple<torch::Tensor, torch::Tensor>
+segment_min_csr(torch::Tensor src, torch::Tensor indptr,
+                torch::optional<torch::Tensor> optional_out) {
+  auto result = SegmentMinCSR::apply(src, indptr, optional_out);
+  return std::make_tuple(result[0], result[1]);
+}
+
+SCATTER_API std::tuple<torch::Tensor, torch::Tensor>
+segment_max_csr(torch::Tensor src, torch::Tensor indptr,
+                torch::optional<torch::Tensor> optional_out) {
+  auto result = SegmentMaxCSR::apply(src, indptr, optional_out);
+  return std::make_tuple(result[0], result[1]);
+}
+
+SCATTER_API torch::Tensor
+gather_csr(torch::Tensor src, torch::Tensor indptr,
+           torch::optional<torch::Tensor> optional_out) {
+  return GatherCSR::apply(src, indptr, optional_out)[0];
+}
+
+static auto registry =
+    torch::RegisterOperators()
+        .op("torch_scatter::segment_sum_csr", &segment_sum_csr)
+        .op("torch_scatter::segment_mean_csr", &segment_mean_csr)
+        .op("torch_scatter::segment_min_csr", &segment_min_csr)
+        .op("torch_scatter::segment_max_csr", &segment_max_csr)
+        .op("torch_scatter::gather_csr", &gather_csr);

csrc/version.cpp

+#ifdef WITH_PYTHON
 #include <Python.h>
+#endif
+
 #include <torch/script.h>
+#include "scatter.h"
+#include "macros.h"
 
-#ifdef WITH_HIP
-#include <hip/hip_runtime.h>
+#ifdef WITH_CUDA
+#ifdef USE_ROCM
+#include <hip/hip_version.h>
+#else
+#include <cuda.h>
+#endif
 #endif
 
 #ifdef _WIN32
-#ifdef WITH_HIP
+#ifdef WITH_PYTHON
+#ifdef WITH_CUDA
 PyMODINIT_FUNC PyInit__version_cuda(void) { return NULL; }
 #else
 PyMODINIT_FUNC PyInit__version_cpu(void) { return NULL; }
 #endif
 #endif
+#endif
 
-int64_t cuda_version() {
-#ifdef WITH_HIP
-  return TORCH_HIP_VERSION;
+namespace scatter {
+SCATTER_API int64_t cuda_version() noexcept {
+#ifdef WITH_CUDA
+#ifdef USE_ROCM
+  return HIP_VERSION;
+#else
+  return CUDA_VERSION;
+#endif
 #else
   return -1;
 #endif
 }
+} // namespace scatter
 
-static auto registry =
-    torch::RegisterOperators().op("torch_scatter::cuda_version", &cuda_version);
+static auto registry = torch::RegisterOperators().op(
+    "torch_scatter::cuda_version", [] { return scatter::cuda_version(); });

csrc/version_hip.cpp

+// !!! This is a file automatically generated by hipify!!!
+#include <ATen/dtk_macros.h>
+#ifdef WITH_PYTHON
+#include <Python.h>
+#endif
+
+#include <torch/script.h>
+#include "scatter.h"
+#include "macros.h"
+
+#ifdef WITH_CUDA
+#ifdef USE_ROCM
+#include <hip/hip_version.h>
+#else
+#include <hip/hip_runtime.h>
+#endif
+#endif
+
+#ifdef _WIN32
+#ifdef WITH_PYTHON
+#ifdef WITH_CUDA
+PyMODINIT_FUNC PyInit__version_cuda(void) { return NULL; }
+#else
+PyMODINIT_FUNC PyInit__version_cpu(void) { return NULL; }
+#endif
+#endif
+#endif
+
+namespace scatter {
+SCATTER_API int64_t cuda_version() noexcept {
+#ifdef WITH_CUDA
+#ifdef USE_ROCM
+  return HIP_VERSION;
+#else
+  return DTK_VERSION;
+#endif
+#else
+  return -1;
+#endif
+}
+} // namespace scatter
+
+static auto registry = torch::RegisterOperators().op(
+    "torch_scatter::cuda_version", [] { return scatter::cuda_version(); });

docs/Makefile

+SPHINXBUILD := sphinx-build
+SPHINXPROJ := pytorch_scatter
+SOURCEDIR := source
+BUILDDIR := build
+
+.PHONY: help Makefile
+
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)"

docs/index.html

+<!DOCTYPE html>
+
+<html>
+<head>
+  <title>Redirect</title>
+  <meta http-equiv="refresh" content="0; url=https://pytorch-scatter.readthedocs.io" />
+</head>
+</html>

docs/requirements.txt

+https://download.pytorch.org/whl/cpu/torch-1.11.0%2Bcpu-cp38-cp38-linux_x86_64.whl
+sphinx>=3
+sphinx_rtd_theme

docs/source/_figures/add.svg

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docs/source/_figures/add.tex

+\def\indices{{0, 0, 1, 0, 2, 2, 3, 3}}
+\def\inputs{{5, 1, 7, 2, 3, 2, 1, 3}}
+\def\outputs{{8, 7, 5, 4}}
+\def\colors{{"cyan", "orange", "olive", "magenta"}}
+\def\numberInputs{7}
+\def\numberOutputs{3}
+\def\operation{add}
+\input{template}

docs/source/_figures/build.sh

+#!/bin/bash
+
+files=(add sub mul div mean max min std)
+
+for name in "${files[@]}"; do
+  pdflatex "$name"
+  pdf2svg "$name.pdf" "$name.svg"
+done

docs/source/_figures/div.svg

+<?xml version="1.0" encoding="UTF-8"?>
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="144.766pt" height="76.934pt" viewBox="0 0 144.766 76.934" version="1.1">
+<defs>
+<g>
+<symbol overflow="visible" id="glyph0-0">
+<path style="stroke:none;" d=""/>
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docs/source/_figures/div.tex

+\def\indices{{0, 0, 1, 0, 2, 2, 3, 3}}
+\def\inputs{{5, 1, 7, 2, 3, 2, 1, 3}}
+\def\outputs{{"$\frac{1}{10}$", "$\frac{1}{7}$", "$\frac{1}{6}$", "$\frac{1}{3}$"}}
+\def\colors{{"cyan", "orange", "olive", "magenta"}}
+\def\numberInputs{7}
+\def\numberOutputs{3}
+\def\operation{div}
+\input{template}

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