gather_scatter on CPU

Summary: CPU implementation of the graph convolution op.

Reviewed By: nikhilaravi, gkioxari

Differential Revision: D21384361

fbshipit-source-id: bc96730e9727bb9aa1b0a232dcb82f0c0d12fe6b

pytorch3d/csrc/gather_scatter/gather_scatter.cu

@@ -44,8 +44,8 @@ __global__ void GatherScatterCudaKernel(
 }
 
 at::Tensor GatherScatterCuda(
-    const at::Tensor input,
-    const at::Tensor edges,
+    const at::Tensor& input,
+    const at::Tensor& edges,
     bool directed,
     bool backward) {
   // Check inputs are on the same device

pytorch3d/csrc/gather_scatter/gather_scatter.h

@@ -20,17 +20,22 @@
 // Returns:
 //   output: float32 Tensor of same shape as input.
 
-// Cuda implementation.
 at::Tensor GatherScatterCuda(
-    const at::Tensor input,
-    const at::Tensor edges,
+    const at::Tensor& input,
+    const at::Tensor& edges,
+    bool directed,
+    bool backward);
+
+at::Tensor GatherScatterCpu(
+    const at::Tensor& input,
+    const at::Tensor& edges,
     bool directed,
     bool backward);
 
 // Exposed implementation.
 at::Tensor GatherScatter(
-    const at::Tensor input,
-    const at::Tensor edges,
+    const at::Tensor& input,
+    const at::Tensor& edges,
     bool directed,
     bool backward) {
   if (input.is_cuda() && edges.is_cuda()) {
@@ -42,5 +47,5 @@ at::Tensor GatherScatter(
     AT_ERROR("Not compiled with GPU support.");
 #endif
   }
-  AT_ERROR("Not implemented on the CPU");
+  return GatherScatterCpu(input, edges, directed, backward);
 }

pytorch3d/csrc/gather_scatter/gather_scatter_cpu.cpp

@@ -0,0 +1,35 @@
+// Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+
+#include <ATen/ATen.h>
+
+at::Tensor GatherScatterCpu(
+    const at::Tensor& input,
+    const at::Tensor& edges,
+    bool directed,
+    bool backward) {
+  const auto num_vertices = input.size(0);
+  const auto input_feature_dim = input.size(1);
+  const auto num_edges = edges.size(0);
+
+  auto output = at::zeros({num_vertices, input_feature_dim}, input.options());
+
+  auto input_a = input.accessor<float, 2>();
+  auto edges_a = edges.accessor<int64_t, 2>();
+  auto output_a = output.accessor<float, 2>();
+  const int v0_idx = backward ? 1 : 0;
+  const int v1_idx = backward ? 0 : 1;
+
+  for (int e = 0; e < num_edges; ++e) {
+    // Get indices of vertices which form the edge.
+    const int64_t v0 = edges_a[e][v0_idx];
+    const int64_t v1 = edges_a[e][v1_idx];
+
+    for (int d = 0; d < input_feature_dim; ++d) {
+      output_a[v0][d] += input_a[v1][d];
+      if (!directed) {
+        output_a[v1][d] += input_a[v0][d];
+      }
+    }
+  }
+  return output;
+}

tests/test_graph_conv.py

@@ -101,17 +101,24 @@ class TestGraphConv(TestCaseMixin, unittest.TestCase):
         mesh = ico_sphere()
         verts = mesh.verts_packed()
         edges = mesh.edges_packed()
+        verts_cpu = verts.clone()
+        edges_cpu = edges.clone()
         verts_cuda = verts.clone().to(device)
         edges_cuda = edges.clone().to(device)
         verts.requires_grad = True
+        verts_cpu.requires_grad = True
         verts_cuda.requires_grad = True
 
         neighbor_sums_cuda = gather_scatter(verts_cuda, edges_cuda, False)
+        neighbor_sums_cpu = gather_scatter(verts_cpu, edges_cpu, False)
         neighbor_sums = gather_scatter_python(verts, edges, False)
-        neighbor_sums_cuda.sum().backward()
-        neighbor_sums.sum().backward()
+        randoms = torch.rand_like(neighbor_sums)
+        (neighbor_sums_cuda * randoms.cuda()).sum().backward()
+        (neighbor_sums_cpu * randoms).sum().backward()
+        (neighbor_sums * randoms).sum().backward()
 
-        self.assertClose(verts.grad.cpu(), verts_cuda.grad.cpu())
+        self.assertClose(verts.grad, verts_cuda.grad.cpu())
+        self.assertClose(verts.grad, verts_cpu.grad)
 
     def test_repr(self):
         conv = GraphConv(32, 64, directed=True)
@@ -141,22 +148,24 @@ class TestGraphConv(TestCaseMixin, unittest.TestCase):
         w0 = nn.Linear(3, 1)
         input = w0(verts)
 
-        # output
-        output_cpu = gather_scatter_python(input, edges, False)
+        # undirected
+        output_python = gather_scatter_python(input, edges, False)
         output_cuda = _C.gather_scatter(
             input.to(device=device), edges.to(device=device), False, False
         )
-        self.assertClose(output_cuda.cpu(), output_cpu)
-        with self.assertRaises(Exception) as err:
-            _C.gather_scatter(input.cpu(), edges.cpu(), False, False)
-        self.assertTrue("Not implemented on the CPU" in str(err.exception))
+        self.assertClose(output_cuda.cpu(), output_python)
+
+        output_cpu = _C.gather_scatter(input.cpu(), edges.cpu(), False, False)
+        self.assertClose(output_cpu, output_python)
 
         # directed
-        output_cpu = gather_scatter_python(input, edges, True)
+        output_python = gather_scatter_python(input, edges, True)
         output_cuda = _C.gather_scatter(
             input.to(device=device), edges.to(device=device), True, False
         )
-        self.assertClose(output_cuda.cpu(), output_cpu)
+        self.assertClose(output_cuda.cpu(), output_python)
+        output_cpu = _C.gather_scatter(input.cpu(), edges.cpu(), True, False)
+        self.assertClose(output_cpu, output_python)
 
     @staticmethod
     def graph_conv_forward_backward(