[skip ci] WIP on index_fill batch rule

functorch/csrc/BatchRulesScatterOps.cpp

@@ -541,6 +541,99 @@ std::tuple<Tensor,optional<int64_t>> index_add_batch_rule(
   return std::make_tuple(at::stack(results), 0);
 }
 
+std::tuple<Tensor,optional<int64_t>> index_fill_int_scalar_batch_rule(
+    const Tensor& self, optional<int64_t> self_bdim,
+    int64_t dim,
+    const Tensor& index, optional<int64_t> index_bdim,
+    const Scalar& value) {
+
+  if (!index_bdim) {
+    // Handle scalar tensors... self, other can be scalar tensors
+    const auto self_logical_rank = rankWithoutBatchDim(self, self_bdim);
+    auto self_ = moveBatchDimToFront(self, self_bdim);
+    if (self_logical_rank == 0) {
+      self_ = self_.unsqueeze(-1);
+    }
+    dim = maybe_wrap_dim(dim, self_logical_rank);
+
+    optional<int64_t> out_bdim = nullopt;
+    if (self_bdim) {
+      const auto batch_size = self.size(*self_bdim);
+      self_ = ensure_has_bdim(self_, self_bdim.has_value(), batch_size);
+      dim = dim + 1;
+      out_bdim = 0;
+    }
+
+    auto result = self_.index_fill(dim, index, value);
+    if (self_logical_rank == 0) {
+      result = result.squeeze(-1);
+    }
+    return std::make_tuple(result, out_bdim);
+  }
+
+  // SAME AS FOR index_add
+  // Index is batched. For-loop and stack is the best thing I can come up with
+  // right now. We really want generalized index_fill kernel in PyTorch
+  auto batch_size = get_bdim_size2(self, self_bdim, index, index_bdim);
+  std::vector<Tensor> results;
+  results.reserve(batch_size);
+  for (const auto i : c10::irange(0, batch_size)) {
+    const auto& self_slice = self_bdim.has_value() ?
+      self.select(*self_bdim, i) : self;
+    const auto& index_slice = index_bdim.has_value() ?
+      index.select(*index_bdim, i) : index;
+    results.push_back(at::index_fill(self_slice, dim, index_slice, value));
+  }
+  return std::make_tuple(at::stack(results), 0);
+}
+
+std::tuple<Tensor,optional<int64_t>> index_fill_int_tensor_batch_rule(
+    const Tensor& self, optional<int64_t> self_bdim,
+    int64_t dim,
+    const Tensor& index, optional<int64_t> index_bdim,
+    const Tensor& value, optional<int64_t> value_bdim) {
+
+  if (!index_bdim && !value_bdim) {
+    // Handle scalar tensors... self, other can be scalar tensors
+    const auto self_logical_rank = rankWithoutBatchDim(self, self_bdim);
+    auto self_ = moveBatchDimToFront(self, self_bdim);
+    if (self_logical_rank == 0) {
+      self_ = self_.unsqueeze(-1);
+    }
+    dim = maybe_wrap_dim(dim, self_logical_rank);
+
+    optional<int64_t> out_bdim = nullopt;
+    if (self_bdim) {
+      const auto batch_size = self.size(*self_bdim);
+      self_ = ensure_has_bdim(self_, self_bdim.has_value(), batch_size);
+      dim = dim + 1;
+      out_bdim = 0;
+    }
+    auto result = self_.index_fill(dim, index, value);
+    if (self_logical_rank == 0) {
+      result = result.squeeze(-1);
+    }
+    return std::make_tuple(result, out_bdim);
+  }
+
+  // SAME AS FOR index_add
+  // Index is batched. For-loop and stack is the best thing I can come up with
+  // right now. We really want generalized index_fill kernel in PyTorch
+  auto batch_size = get_bdim_size3(self, self_bdim, index, index_bdim, value, value_bdim);
+  std::vector<Tensor> results;
+  results.reserve(batch_size);
+  for (const auto i : c10::irange(0, batch_size)) {
+    const auto& self_slice = self_bdim.has_value() ?
+      self.select(*self_bdim, i) : self;
+    const auto& index_slice = index_bdim.has_value() ?
+      index.select(*index_bdim, i) : index;
+    const auto& value_slice = value_bdim.has_value() ?
+      value.select(*value_bdim, i) : value;
+    results.push_back(at::index_fill(self_slice, dim, index_slice, value_slice));
+  }
+  return std::make_tuple(at::stack(results), 0);
+}
+
 TORCH_LIBRARY_IMPL(aten, FT_BATCHED_KEY, m) {
   m.impl("index.Tensor", index_plumbing);
   m.impl("index_put_", index_put__plumbing);
@@ -550,6 +643,8 @@ TORCH_LIBRARY_IMPL(aten, FT_BATCHED_KEY, m) {
   m.impl("index_copy", index_copy_decomp);
   m.impl("index_select", index_select_decomp);
   VMAP_SUPPORT("index_add", index_add_batch_rule);
+  VMAP_SUPPORT("index_fill.int_Scalar", index_fill_int_scalar_batch_rule);
+  VMAP_SUPPORT("index_fill.int_Tensor", index_fill_int_tensor_batch_rule);
   VMAP_SUPPORT("diagonal_scatter", diagonal_scatter_batch_rule);
   VMAP_SUPPORT("gather", gather_batch_rule);
   VMAP_SUPPORT("gather_backward", gather_backward_batch_rule);

test/test_ops.py

@@ -513,7 +513,6 @@ def vjp_of_vjp(*args_and_cotangents):
         xfail('fmax'),
         xfail('fmin'),
         xfail('index_copy'),
-        xfail('index_fill'),
         xfail('linalg.det', ''),
         xfail('linalg.eigh'),
         xfail('linalg.householder_product'),
@@ -595,7 +594,6 @@ def test_vmapvjp(self, device, dtype, op):
         xfail('block_diag'),  # TODO: We expect this to fail in core, but it doesn't
         xfail('index_copy'),
         xfail('index_put'),
-        xfail('index_fill'),
         xfail('masked_fill'),
         xfail('masked_scatter'),
 
@@ -701,7 +699,6 @@ def test_vmapjvp(self, device, dtype, op):
         xfail('max', 'binary'),
         xfail('nn.functional.gaussian_nll_loss'),
         xfail('min', 'binary'),
-        xfail('index_fill'),
         xfail('index_put'),
         xfail('std_mean'),
         xfail('double', 'channels_last'),
@@ -760,7 +757,7 @@ def test_vmapjvpall(self, device, dtype, op):
         xfail('fmax'),
         xfail('fmin'),
         xfail('index_copy'),
-        xfail('index_fill'),
+        xfail('index_fill'),  # RuntimeError: aten::_unique hit the vmap fallback which is currently disabled
         xfail('linalg.cholesky'),
         xfail('linalg.cholesky_ex'),
         xfail('linalg.det'),

test/test_vmap.py

@@ -3181,7 +3181,6 @@ def test_vmap_exhaustive(self, device, dtype, op):
         xfail('gradient'),
         xfail('histogram'),
         xfail('hsplit'),
-        xfail('index_fill'),
         xfail('index_put'),
         xfail('isin'),
         xfail('linalg.cholesky'),