Linter

Author: kirklandsign

extension/llm/runner/multimodal_prefiller.cpp

@@ -92,15 +92,16 @@ Result<uint64_t> MultimodalPrefiller::prefill(
 
   // 2. Run decoder model for prefill.
 
-
-  // Get expected shape of cache position tensor, which should be the second argument
+  // Get expected shape of cache position tensor, which should be the second
+  // argument
 
   int64_t seq_len = encoder_output.toTensor().size(1);
   if (seq_len == 0) {
     ET_LOG(Error, "The encoder returned an empty output.");
     return ::executorch::runtime::Error::InvalidState;
   }
-  auto cache_position_tensor = ET_UNWRAP(populate_start_pos_tensor(module_, start_pos, seq_len));
+  auto cache_position_tensor =
+      ET_UNWRAP(populate_start_pos_tensor(module_, start_pos, seq_len));
 
   auto prefill_result = module_->execute(
       kTextModelMethod, {encoder_output, cache_position_tensor});

extension/llm/runner/text_decoder_runner.cpp

@@ -53,7 +53,8 @@ ::executorch::runtime::Result<executorch::aten::Tensor> TextDecoderRunner::step(
     auto numel = sizes[0];
     std::vector<::executorch::aten::SizesType> sizes_vec = {numel};
 
-    auto start_pos_tensor = ET_UNWRAP(populate_start_pos_tensor(module_, start_pos, tokens->numel()));
+    auto start_pos_tensor = ET_UNWRAP(
+        populate_start_pos_tensor(module_, start_pos, tokens->numel()));
 
     std::vector<runtime::EValue> inputs;
     auto inputs_res = io_manager_->prepare_decode(tokens, start_pos_tensor);

extension/llm/runner/util.h

@@ -103,8 +103,10 @@ ET_EXPERIMENTAL size_t inline get_rss_bytes() {
   return 0;
 }
 
-inline runtime::Result<TensorPtr> populate_start_pos_tensor(Module* module, int64_t& start_pos, int seq_len) {
-  // Get expected shape of cache position tensor, which should be the second argument
+inline runtime::Result<TensorPtr>
+populate_start_pos_tensor(Module* module, int64_t& start_pos, int seq_len) {
+  // Get expected shape of cache position tensor, which should be the second
+  // argument
   auto method_meta = ET_UNWRAP(module->method_meta(kTextModelMethod));
   auto second_input_info = ET_UNWRAP(method_meta.input_tensor_meta(1));
   auto second_input_sizes = second_input_info.sizes();