[geometric]Move graph-related incubate api to geometric

paddle/phi/kernels/cpu/segment_pool_grad_kernel.cc

@@ -25,4 +25,5 @@ PD_REGISTER_KERNEL(segment_pool_grad,
                    float,
                    double,
                    int,
-                   int64_t) {}
+                   int64_t,
+                   phi::dtype::float16) {}

paddle/phi/kernels/cpu/segment_pool_kernel.cc

@@ -25,4 +25,5 @@ PD_REGISTER_KERNEL(segment_pool,
                    float,
                    double,
                    int,
-                   int64_t) {}
+                   int64_t,
+                   phi::dtype::float16) {}

paddle/phi/kernels/funcs/segment_pooling.cc

@@ -145,6 +145,7 @@ class SegmentPoolGradFunctor<phi::CPUContext, T, IndexT> {
 };
 
 using CPU = phi::CPUContext;
+using float16 = phi::dtype::float16;
 template class SegmentPoolFunctor<CPU, float, int>;
 template class SegmentPoolFunctor<CPU, float, int64_t>;
 template class SegmentPoolFunctor<CPU, double, int>;
@@ -153,6 +154,8 @@ template class SegmentPoolFunctor<CPU, int, int>;
 template class SegmentPoolFunctor<CPU, int, int64_t>;
 template class SegmentPoolFunctor<CPU, int64_t, int>;
 template class SegmentPoolFunctor<CPU, int64_t, int64_t>;
+template class SegmentPoolFunctor<CPU, float16, int>;
+template class SegmentPoolFunctor<CPU, float16, int64_t>;
 
 template class SegmentPoolGradFunctor<CPU, float, int>;
 template class SegmentPoolGradFunctor<CPU, float, int64_t>;
@@ -162,6 +165,8 @@ template class SegmentPoolGradFunctor<CPU, int, int>;
 template class SegmentPoolGradFunctor<CPU, int, int64_t>;
 template class SegmentPoolGradFunctor<CPU, int64_t, int>;
 template class SegmentPoolGradFunctor<CPU, int64_t, int64_t>;
+template class SegmentPoolGradFunctor<CPU, float16, int>;
+template class SegmentPoolGradFunctor<CPU, float16, int64_t>;
 
 }  // namespace funcs
 }  // namespace phi

paddle/phi/kernels/funcs/segment_pooling.cu

@@ -324,7 +324,7 @@ class SegmentPoolFunctor<phi::GPUContext, T, IndexT> {
                   const std::string pooltype = "SUM") {
     if (pooltype == "MEAN") {
       // Sum the segment id num first
-      T DimTileSize = 8;
+      IndexT DimTileSize = 8;
       auto input_length_size = segment_ids.numel();
       auto total_stripe_count =
           (input_length_size + DimTileSize - 1) / DimTileSize;
@@ -440,6 +440,7 @@ class SegmentPoolGradFunctor<phi::GPUContext, T, IndexT> {
 };
 
 using GPU = phi::GPUContext;
+using float16 = phi::dtype::float16;
 template class SegmentPoolFunctor<GPU, float, int>;
 template class SegmentPoolFunctor<GPU, float, int64_t>;
 template class SegmentPoolFunctor<GPU, double, int>;
@@ -448,6 +449,8 @@ template class SegmentPoolFunctor<GPU, int, int>;
 template class SegmentPoolFunctor<GPU, int, int64_t>;
 template class SegmentPoolFunctor<GPU, int64_t, int>;
 template class SegmentPoolFunctor<GPU, int64_t, int64_t>;
+template class SegmentPoolFunctor<GPU, float16, int>;
+template class SegmentPoolFunctor<GPU, float16, int64_t>;
 
 template class SegmentPoolGradFunctor<GPU, float, int>;
 template class SegmentPoolGradFunctor<GPU, float, int64_t>;
@@ -457,6 +460,8 @@ template class SegmentPoolGradFunctor<GPU, int, int>;
 template class SegmentPoolGradFunctor<GPU, int, int64_t>;
 template class SegmentPoolGradFunctor<GPU, int64_t, int>;
 template class SegmentPoolGradFunctor<GPU, int64_t, int64_t>;
+template class SegmentPoolGradFunctor<GPU, float16, int>;
+template class SegmentPoolGradFunctor<GPU, float16, int64_t>;
 
 }  // namespace funcs
 }  // namespace phi

paddle/phi/kernels/gpu/segment_pool_grad_kernel.cu

@@ -26,4 +26,5 @@ PD_REGISTER_KERNEL(segment_pool_grad,
                    float,
                    double,
                    int,
-                   int64_t) {}
+                   int64_t,
+                   phi::dtype::float16) {}

paddle/phi/kernels/gpu/segment_pool_kernel.cu

@@ -26,4 +26,5 @@ PD_REGISTER_KERNEL(segment_pool,
                    float,
                    double,
                    int,
-                   int64_t) {}
+                   int64_t,
+                   phi::dtype::float16) {}

paddle/phi/kernels/impl/segment_pool_kernel_impl.h

@@ -97,7 +97,7 @@ void SegmentKernelLaunchHelper(const Context& dev_ctx,
     out->Resize({dims});
     dev_ctx.template Alloc<T>(out);
 
-    T init_value = 0;
+    T init_value = static_cast<T>(0);
     if (pooltype == "MAX") {
       init_value = static_cast<T>(-FLT_MAX);
     } else if (pooltype == "MIN") {

python/paddle/fluid/tests/unittests/test_graph_reindex.py

@@ -181,5 +181,276 @@ def test_reindex_result_static(self):
             np.testing.assert_allclose(self.out_nodes, out_nodes_2, rtol=1e-05)
 
 
+class TestGeometricGraphReindex(unittest.TestCase):
+
+    def setUp(self):
+        self.x = np.arange(5).astype("int64")
+        self.neighbors = np.random.randint(100, size=20).astype("int64")
+        self.count = np.array([2, 8, 4, 3, 3], dtype="int32")
+
+        # Get numpy result.
+        out_nodes = list(self.x)
+        for neighbor in self.neighbors:
+            if neighbor not in out_nodes:
+                out_nodes.append(neighbor)
+        self.out_nodes = np.array(out_nodes, dtype="int64")
+        reindex_dict = {node: ind for ind, node in enumerate(self.out_nodes)}
+        self.reindex_src = np.array(
+            [reindex_dict[node] for node in self.neighbors])
+        reindex_dst = []
+        for node, c in zip(self.x, self.count):
+            for i in range(c):
+                reindex_dst.append(reindex_dict[node])
+        self.reindex_dst = np.array(reindex_dst, dtype="int64")
+        self.num_nodes = np.max(np.concatenate([self.x, self.neighbors])) + 1
+
+    def test_reindex_result(self):
+        paddle.disable_static()
+        x = paddle.to_tensor(self.x)
+        neighbors = paddle.to_tensor(self.neighbors)
+        count = paddle.to_tensor(self.count)
+        value_buffer = paddle.full([self.num_nodes], -1, dtype="int32")
+        index_buffer = paddle.full([self.num_nodes], -1, dtype="int32")
+
+        reindex_src, reindex_dst, out_nodes = \
+            paddle.geometric.reindex_graph(x, neighbors, count)
+        np.testing.assert_allclose(self.reindex_src, reindex_src, rtol=1e-05)
+        np.testing.assert_allclose(self.reindex_dst, reindex_dst, rtol=1e-05)
+        np.testing.assert_allclose(self.out_nodes, out_nodes, rtol=1e-05)
+
+        reindex_src, reindex_dst, out_nodes = \
+            paddle.geometric.reindex_graph(x, neighbors, count,
+                                          value_buffer, index_buffer)
+        np.testing.assert_allclose(self.reindex_src, reindex_src, rtol=1e-05)
+        np.testing.assert_allclose(self.reindex_dst, reindex_dst, rtol=1e-05)
+        np.testing.assert_allclose(self.out_nodes, out_nodes, rtol=1e-05)
+
+    def test_heter_reindex_result(self):
+        paddle.disable_static()
+        x = paddle.to_tensor(self.x)
+        neighbors = paddle.to_tensor(self.neighbors)
+        neighbors = paddle.concat([neighbors, neighbors])
+        count = paddle.to_tensor(self.count)
+        count = paddle.concat([count, count])
+
+        reindex_src, reindex_dst, out_nodes = \
+            paddle.geometric.reindex_graph(x, neighbors, count)
+        np.testing.assert_allclose(self.reindex_src,
+                                   reindex_src[:self.neighbors.shape[0]],
+                                   rtol=1e-05)
+        np.testing.assert_allclose(self.reindex_src,
+                                   reindex_src[self.neighbors.shape[0]:],
+                                   rtol=1e-05)
+        np.testing.assert_allclose(self.reindex_dst,
+                                   reindex_dst[:self.neighbors.shape[0]],
+                                   rtol=1e-05)
+        np.testing.assert_allclose(self.reindex_dst,
+                                   reindex_dst[self.neighbors.shape[0]:],
+                                   rtol=1e-05)
+        np.testing.assert_allclose(self.out_nodes, out_nodes, rtol=1e-05)
+
+    def test_heter_reindex_result_v2(self):
+        paddle.disable_static()
+        x = np.arange(5).astype("int64")
+        neighbors1 = np.random.randint(100, size=20).astype("int64")
+        count1 = np.array([2, 8, 4, 3, 3], dtype="int32")
+        neighbors2 = np.random.randint(100, size=20).astype("int64")
+        count2 = np.array([4, 5, 1, 6, 4], dtype="int32")
+        neighbors = np.concatenate([neighbors1, neighbors2])
+        counts = np.concatenate([count1, count2])
+
+        # Get numpy result.
+        out_nodes = list(x)
+        for neighbor in neighbors:
+            if neighbor not in out_nodes:
+                out_nodes.append(neighbor)
+        out_nodes = np.array(out_nodes, dtype="int64")
+        reindex_dict = {node: ind for ind, node in enumerate(out_nodes)}
+        reindex_src = np.array([reindex_dict[node] for node in neighbors])
+        reindex_dst = []
+        for count in [count1, count2]:
+            for node, c in zip(x, count):
+                for i in range(c):
+                    reindex_dst.append(reindex_dict[node])
+        reindex_dst = np.array(reindex_dst, dtype="int64")
+
+        reindex_src_, reindex_dst_, out_nodes_ = \
+            paddle.geometric.reindex_graph(paddle.to_tensor(x),
+                                          paddle.to_tensor(neighbors),
+                                          paddle.to_tensor(counts))
+        np.testing.assert_allclose(reindex_src, reindex_src_, rtol=1e-05)
+        np.testing.assert_allclose(reindex_dst, reindex_dst_, rtol=1e-05)
+        np.testing.assert_allclose(out_nodes, out_nodes_, rtol=1e-05)
+
+    def test_heter_reindex_result_v3(self):
+        paddle.disable_static()
+        x = np.arange(5).astype("int64")
+        neighbors1 = np.random.randint(100, size=20).astype("int64")
+        count1 = np.array([2, 8, 4, 3, 3], dtype="int32")
+        neighbors2 = np.random.randint(100, size=20).astype("int64")
+        count2 = np.array([4, 5, 1, 6, 4], dtype="int32")
+        neighbors = np.concatenate([neighbors1, neighbors2])
+        count = np.concatenate([count1, count2])
+
+        # Get numpy result.
+        out_nodes = list(x)
+        for neighbor in neighbors:
+            if neighbor not in out_nodes:
+                out_nodes.append(neighbor)
+        out_nodes = np.array(out_nodes, dtype="int64")
+        reindex_dict = {node: ind for ind, node in enumerate(out_nodes)}
+        reindex_src = np.array([reindex_dict[node] for node in neighbors])
+        reindex_dst = []
+        for count in [count1, count2]:
+            for node, c in zip(x, count):
+                for i in range(c):
+                    reindex_dst.append(reindex_dict[node])
+        reindex_dst = np.array(reindex_dst, dtype="int64")
+
+        neighbors = [paddle.to_tensor(neighbors1), paddle.to_tensor(neighbors2)]
+        count = [paddle.to_tensor(count1), paddle.to_tensor(count2)]
+        reindex_src_, reindex_dst_, out_nodes_ = \
+            paddle.geometric.reindex_heter_graph(paddle.to_tensor(x),
+                                                 neighbors, count)
+        np.testing.assert_allclose(reindex_src, reindex_src_, rtol=1e-05)
+        np.testing.assert_allclose(reindex_dst, reindex_dst_, rtol=1e-05)
+        np.testing.assert_allclose(out_nodes, out_nodes_, rtol=1e-05)
+
+    def test_reindex_result_static(self):
+        paddle.enable_static()
+        with paddle.static.program_guard(paddle.static.Program()):
+            x = paddle.static.data(name="x",
+                                   shape=self.x.shape,
+                                   dtype=self.x.dtype)
+            neighbors = paddle.static.data(name="neighbors",
+                                           shape=self.neighbors.shape,
+                                           dtype=self.neighbors.dtype)
+            count = paddle.static.data(name="count",
+                                       shape=self.count.shape,
+                                       dtype=self.count.dtype)
+            value_buffer = paddle.static.data(name="value_buffer",
+                                              shape=[self.num_nodes],
+                                              dtype="int32")
+            index_buffer = paddle.static.data(name="index_buffer",
+                                              shape=[self.num_nodes],
+                                              dtype="int32")
+
+            reindex_src_1, reindex_dst_1, out_nodes_1 = \
+                paddle.geometric.reindex_graph(x, neighbors, count)
+            reindex_src_2, reindex_dst_2, out_nodes_2 = \
+                paddle.geometric.reindex_graph(x, neighbors, count,
+                                              value_buffer, index_buffer)
+
+            exe = paddle.static.Executor(paddle.CPUPlace())
+            ret = exe.run(feed={
+                'x':
+                self.x,
+                'neighbors':
+                self.neighbors,
+                'count':
+                self.count,
+                'value_buffer':
+                np.full([self.num_nodes], -1, dtype="int32"),
+                'index_buffer':
+                np.full([self.num_nodes], -1, dtype="int32")
+            },
+                          fetch_list=[
+                              reindex_src_1, reindex_dst_1, out_nodes_1,
+                              reindex_src_2, reindex_dst_2, out_nodes_2
+                          ])
+            reindex_src_1, reindex_dst_1, out_nodes_1, reindex_src_2, \
+                reindex_dst_2, out_nodes_2 = ret
+            np.testing.assert_allclose(self.reindex_src,
+                                       reindex_src_1,
+                                       rtol=1e-05)
+            np.testing.assert_allclose(self.reindex_dst,
+                                       reindex_dst_1,
+                                       rtol=1e-05)
+            np.testing.assert_allclose(self.out_nodes, out_nodes_1, rtol=1e-05)
+            np.testing.assert_allclose(self.reindex_src,
+                                       reindex_src_2,
+                                       rtol=1e-05)
+            np.testing.assert_allclose(self.reindex_dst,
+                                       reindex_dst_2,
+                                       rtol=1e-05)
+            np.testing.assert_allclose(self.out_nodes, out_nodes_2, rtol=1e-05)
+
+    def test_heter_reindex_result_static(self):
+        paddle.enable_static()
+        np_x = np.arange(5).astype("int64")
+        np_neighbors1 = np.random.randint(100, size=20).astype("int64")
+        np_count1 = np.array([2, 8, 4, 3, 3], dtype="int32")
+        np_neighbors2 = np.random.randint(100, size=20).astype("int64")
+        np_count2 = np.array([4, 5, 1, 6, 4], dtype="int32")
+        np_neighbors = np.concatenate([np_neighbors1, np_neighbors2])
+        np_count = np.concatenate([np_count1, np_count2])
+
+        # Get numpy result.
+        out_nodes = list(np_x)
+        for neighbor in np_neighbors:
+            if neighbor not in out_nodes:
+                out_nodes.append(neighbor)
+        out_nodes = np.array(out_nodes, dtype="int64")
+        reindex_dict = {node: ind for ind, node in enumerate(out_nodes)}
+        reindex_src = np.array([reindex_dict[node] for node in np_neighbors])
+        reindex_dst = []
+        for count in [np_count1, np_count2]:
+            for node, c in zip(np_x, count):
+                for i in range(c):
+                    reindex_dst.append(reindex_dict[node])
+        reindex_dst = np.array(reindex_dst, dtype="int64")
+
+        with paddle.static.program_guard(paddle.static.Program()):
+            x = paddle.static.data(name="x", shape=[5], dtype="int64")
+            neighbors1 = paddle.static.data(name="neighbors1",
+                                            shape=[20],
+                                            dtype="int64")
+            count1 = paddle.static.data(name="count1", shape=[5], dtype="int32")
+            neighbors2 = paddle.static.data(name="neighbors2",
+                                            shape=[20],
+                                            dtype="int64")
+            count2 = paddle.static.data(name="count2", shape=[5], dtype="int32")
+            value_buffer = paddle.static.data(name="value_buffer",
+                                              shape=[5],
+                                              dtype="int32")
+            index_buffer = paddle.static.data(name="index_buffer",
+                                              shape=[5],
+                                              dtype="int32")
+
+            reindex_src_1, reindex_dst_1, out_nodes_1 = \
+                paddle.geometric.reindex_heter_graph(x,
+                                                     [neighbors1, neighbors2],
+                                                     [count1, count2])
+            reindex_src_2, reindex_dst_2, out_nodes_2 = \
+                paddle.geometric.reindex_heter_graph(x,
+                                                     [neighbors1, neighbors2],
+                                                     [count1, count2],
+                                                     value_buffer, index_buffer)
+
+            exe = paddle.static.Executor(paddle.CPUPlace())
+            ret = exe.run(feed={
+                'x': np_x,
+                'neighbors1': np_neighbors1,
+                'count1': np_count1,
+                'neighbors2': np_neighbors2,
+                'count2': np_count2,
+                'value_buffer': np.full([5], -1, dtype="int32"),
+                'index_buffer': np.full([5], -1, dtype="int32")
+            },
+                          fetch_list=[
+                              reindex_src_1, reindex_dst_1, out_nodes_1,
+                              reindex_src_2, reindex_dst_2, out_nodes_2
+                          ])
+
+            reindex_src_1, reindex_dst_1, out_nodes_1, reindex_src_2, \
+                reindex_dst_2, out_nodes_2 = ret
+            np.testing.assert_allclose(reindex_src, reindex_src_1, rtol=1e-05)
+            np.testing.assert_allclose(reindex_dst, reindex_dst_1, rtol=1e-05)
+            np.testing.assert_allclose(out_nodes, out_nodes_1, rtol=1e-05)
+            np.testing.assert_allclose(reindex_src, reindex_src_2, rtol=1e-05)
+            np.testing.assert_allclose(reindex_dst, reindex_dst_2, rtol=1e-05)
+            np.testing.assert_allclose(out_nodes, out_nodes_2, rtol=1e-05)
+
+
 if __name__ == "__main__":
     unittest.main()