removes existing_bdim_boxed (which did nothing sensible...)

Author: Chillee

functorch/csrc/BatchRulesHelper.h

@@ -261,69 +261,6 @@ inline void boxed_existing_bdim_all_batch_rule(
 #define EXISTING_BDIM_ALL_BOXED(op) \
   m.impl(#op, torch::CppFunction::makeFromBoxedFunction<boxed_existing_bdim_all_batch_rule>());
 
-inline void boxed_existing_bdim_batch_rule(const c10::OperatorHandle& op, torch::jit::Stack* stack) {
-  const auto& schema = op.schema();
-  const auto num_returns = schema.returns().size();
-  const auto num_arguments = schema.arguments().size();
-  auto arguments = torch::jit::pop(*stack, num_arguments);
-
-  c10::impl::ExcludeDispatchKeyGuard guard(kBatchedKey);
-  auto maybe_layer = maybeCurrentDynamicLayer();
-  TORCH_INTERNAL_ASSERT(maybe_layer.has_value());
-  int64_t cur_level = maybe_layer->layerId();
-
-  std::vector<std::pair<Tensor, optional<int64_t>>> tensor_inputs;
-  std::vector<int64_t> tensor_pos;
-  for (const auto idx : c10::irange(0, num_arguments)) {
-    const auto& ivalue = arguments[idx];
-    if (!ivalue.isTensor()) {
-      continue;
-    }
-    Tensor tensor_value;
-    optional<int64_t> tensor_bdim;
-    std::tie(tensor_value, tensor_bdim) = unwrapTensorAtLevel(ivalue.toTensor(), cur_level);
-    tensor_inputs.push_back(std::make_pair(tensor_value, tensor_bdim));
-    tensor_pos.push_back(idx);
-  }
-
-  int64_t batch_size = -1;
-  for (auto& tensor_input : tensor_inputs) {
-    if (tensor_input.second) {
-      if (batch_size == -1) {
-        batch_size = tensor_input.first.size(*tensor_input.second);
-      }
-      TORCH_INTERNAL_ASSERT(batch_size == tensor_input.first.size(*tensor_input.second));
-      tensor_input.first = reshape_dim_into(*tensor_input.second, 0, tensor_input.first);
-    }
-  }
-
-  size_t tensor_idx = 0;
-  TORCH_INTERNAL_ASSERT(tensor_pos.size() > 0);
-  for (const auto arg_idx : c10::irange(0, num_arguments)) {
-    if (tensor_idx >= tensor_pos.size() || (int64_t)arg_idx != tensor_pos[tensor_idx]) {
-      torch::jit::push(stack, arguments[arg_idx]);
-    } else {
-      TORCH_INTERNAL_ASSERT(tensor_idx < tensor_inputs.size());
-      torch::jit::push(stack, tensor_inputs[tensor_idx].first);
-      tensor_idx++;
-    }
-  }
-
-  op.callBoxed(stack);
-  const auto returns = torch::jit::pop(*stack, num_returns);
-  for (const auto& ret : returns) {
-    if (ret.isTensor()) {
-      torch::jit::push(stack, makeBatched(reshape_dim_outof(0, batch_size, ret.toTensor()), 0, cur_level));
-    } else {
-      TORCH_INTERNAL_ASSERT(false, "This boxed batching rule does not currently support ops that return non-tensor values");
-    }
-  }
-}
-
-#define EXISTING_BDIM_BOXED(op) \
-  m.impl(#op, torch::CppFunction::makeFromBoxedFunction<boxed_existing_bdim_batch_rule>());
-
-
 template <typename A, A a, typename C>
 struct ExistingBdimBatchRuleHelper;