Fix advanced indexing

functorch/csrc/BatchRulesScatterOps.cpp

@@ -16,7 +16,7 @@
 
 namespace at { namespace functorch {
 
-std::vector<optional<Tensor>> batchIndices(
+std::tuple<std::vector<optional<Tensor>>, int64_t, int64_t> batchIndices(
   ArrayRef<optional<Tensor>> indices,
   ArrayRef<optional<int64_t>> indices_bdims,
   int64_t batch_size,
@@ -29,23 +29,38 @@ std::vector<optional<Tensor>> batchIndices(
   //
   // 2. self is not batched, some indices are batched.
   // In this case, we don't need to do anything - indices will automatically
-  // broadcast to work with the unbatched self.
+  // broadcast to work with the unbatched self. If there are leading Nones or
+  // empty tensors in the indices, the batch dimension will appear after
+  // those leading blanks, so the 1st ret value captures that for the batching
+  // rules that use this
   //
   // 3. self is batched, some indices are batched.
   // In this case, we simply need to add an arange that indexes along the first
   // dimension (i.e. the batch dimension). We also need to make sure this
-  // broadcasts with the rest of the indices.
+  // broadcasts with the rest of the indices. If there are leading Nones or empty
+  // tensors, then this will mess us up because we will get [batch_dim, index_dim0, ...]
+  // instead of [batch_dim, self_dim0, ...] matching the number of leading Nones. By
+  // returning batchLoc (the number of leading Nones) and maxIndexDim (the maximum
+  // dim of any of the index), we can move dims of the result to make the right shape
   //
   // There is one more case worth mentioning - boolean tensor indices. If we
   // have "batched" boolean tensor indices, that is unrepresentable, as each
   // batch would result in a tensor with different values.
   std::vector<optional<Tensor>> indices_;
-  int64_t minIndexDim = 0;
+  int64_t maxLogicalRank = 0;
+  bool indices_batched = false;
+  for (size_t i = 0; i < indices.size(); i++) {
+    if (indices[i].has_value()) {
+      maxLogicalRank = std::max(maxLogicalRank, rankWithoutBatchDim(indices[i].value(), indices_bdims[i]));
+    }
+    indices_batched = indices_batched || indices_bdims[i].has_value();
+  }
+
   for (size_t i = 0; i < indices.size(); i++) {
     auto index = indices[i];
-    if (index.has_value()) {
-      indices_.emplace_back(moveBatchDimToFront(index.value(), indices_bdims[i]));
-      minIndexDim = std::max(minIndexDim, index.value().dim());
+    if (index.has_value() && index->numel() != 0) {
+      const auto idx_bdim = indices_bdims[i];
+      indices_.emplace_back(maybePadToLogicalRank(moveBatchDimToFront(index.value(), idx_bdim), idx_bdim, maxLogicalRank));
       if (index.value().dtype() == kBool && indices_bdims[i].has_value()) {
         throw std::runtime_error("vmap: We do not support batching operators that can support dynamic shape. Attempting to batch over indexing with a boolean mask.");
       }
@@ -54,12 +69,17 @@ std::vector<optional<Tensor>> batchIndices(
     }
   }
 
-  bool indices_batched = false;
-  for (auto idx : indices_bdims) {
-    indices_batched = indices_batched || idx.has_value();
+  auto maxIndexDim = maxLogicalRank;
+  if (indices_batched || values_bdim.has_value()) {
+    maxIndexDim += 1;
   }
-  if (!indices_batched && values_bdim.has_value()) {
-    minIndexDim += 1;
+
+  size_t batchLoc = 0;
+  // If there's leading Nones ([:, :,...]) and indices is batched, the batch dimension will show up after the skipped dimensinos
+  if (indices_batched) {
+    while(!indices_[batchLoc].has_value() || indices_[batchLoc]->numel() == 0) {
+      batchLoc += 1;
+    }
   }
 
   if (!indices_batched && self_bdim.has_value()) {
@@ -68,12 +88,12 @@ std::vector<optional<Tensor>> batchIndices(
     // do nothing
   } else if (indices_batched && (self_bdim.has_value() || values_bdim.has_value())) {
     auto arange_index = at::arange(0, batch_size);
-    while (arange_index.dim() < minIndexDim) {
+    while (arange_index.dim() < maxIndexDim) {
       arange_index = arange_index.unsqueeze(-1);
     }
     indices_.insert(indices_.begin(), arange_index);
   }
-  return indices_;
+  return std::make_tuple(indices_, batchLoc, maxIndexDim);
 }
 
 std::tuple<Tensor,optional<int64_t>> index_batch_rule(
@@ -84,8 +104,23 @@ std::tuple<Tensor,optional<int64_t>> index_batch_rule(
 
   auto self_ = moveBatchDimToFront(self, self_bdim);
   TORCH_INTERNAL_ASSERT(indices.size() == indices_bdims.size());
-  std::vector<optional<Tensor>> indices_ = batchIndices(indices, indices_bdims, self_.size(0), self_bdim);
-  return std::make_tuple(at::index(self_, List<optional<Tensor>>(indices_)), 0);
+  const auto ret = batchIndices(indices, indices_bdims, self_.size(0), self_bdim);
+  const std::vector<optional<Tensor>> indices_ = std::get<0>(ret);
+  auto res = at::index(self_, List<optional<Tensor>>(indices_));
+  auto outDim = std::get<1>(ret);
+  const auto maxIndexDim = std::get<2>(ret);
+  if (self_bdim.has_value() && outDim != 0) {
+    // this will only happen if at least one index is batched and there is at least one leading None.
+    // In this case, we will have [batch_dim, index_dim0, ..., self_dim0, ...] instead of [batch_dim, self_dim0, ..., index_dim0]
+    // so we move dims around until we get to the right shape. We know where self_dim0 is because maxIndexDim is
+    // how long the broadcasted indices will be and we know how many to move because outDim is the number of leading
+    // Nones
+    for (int i = 0; i < outDim; i ++) {
+      res = res.movedim(maxIndexDim + i, i + 1);
+    }
+    outDim = 0;
+  }
+  return std::make_tuple(res, outDim);
 }
 
 // plumbing done since we don't support List<optional<Tensor>> in codegen
@@ -195,7 +230,8 @@ namespace {
     TORCH_INTERNAL_ASSERT(indices.size() == indices_bdims.size());
 
     // we've already made sure that self has bdim at 0.
-    std::vector<optional<Tensor>> indices_ = batchIndices(indices, indices_bdims, batch_size, /*self_bdim=*/0, values_bdim);
+    // TODO(samdow): fix. We made changes to batchIndices that fixed index and probably show issues in index_put
+    std::vector<optional<Tensor>> indices_ = std::get<0>(batchIndices(indices, indices_bdims, batch_size, /*self_bdim=*/0, values_bdim));
 
     auto indexed_shape = get_indexed_shape(self_, List<optional<Tensor>>(indices_));
 

test/test_vmap.py

@@ -3515,6 +3515,57 @@ def f(x, y):
         y = torch.randn(2, 3, device=device)
         self.assertTrue(isinstance(vmap(f)(x, y), Point))
 
+    def test_advanced_indexing(self, device):
+        def test(f, args):
+            for loop_out, batched_out in get_fallback_and_vmap_exhaustive(f, args, {}):
+                self.assertEqual(loop_out, batched_out)
+
+        def f(x, idx):
+            return x[:, idx]
+
+        def f2(x, idx):
+            return x[idx, :]
+
+        def f3(x, idx):
+            return x[:, :, idx]
+
+        inps = (torch.randn(5, 5, 5, device=device),
+                torch.randn(5, 5, 5, 5, device=device),
+                torch.randn(5, 5, 5, 5, 5, device=device))
+        idxes = (torch.tensor([0, 1, 2], device=device),
+                 torch.tensor([0, 1, 2], device=device).reshape(3, 1),
+                 torch.tensor([0, 1, 2], device=device).reshape(3, 1, 1))
+        for (inp, idx) in itertools.product(inps, idxes):
+            test(f, (inp, idx))
+            test(f2, (inp, idx))
+            test(f3, (inp, idx))
+
+    def test_nested_advanced_indexing(self, device):
+        e = torch.rand(7, 4, device=device)
+        idx = torch.tensor([0, 1], device=device).view(2, 1)
+
+        # simple reference implementation for comparison
+        def _fake_vmap(f, in_dims=0, out_dims=0):
+            def w(input):
+                r = [f(input.select(in_dims, i)) for i in range(input.size(in_dims))]
+                return torch.stack(r, out_dims)
+
+            return w
+
+        def with_vmap(_vmap):
+            def g(idx_):
+                def f(e_):
+                    return e_[idx_]
+
+                return _vmap(f, in_dims=1)(e)
+
+            r = _vmap(g)(idx)
+            return r
+
+        a = with_vmap(vmap)
+        b = with_vmap(_fake_vmap)
+        self.assertEqual(a, b)
+
 
 class TestRandomness(TestCase):
     def _reset_random(self, generator, orig_state, use_generator, seed):