Parallelize optimized op_log_softmax

Straightforward application of parallel_for.

Differential Revision: [D76831122](https://our.internmc.facebook.com/intern/diff/D76831122/)

[ghstack-poisoned]

Author: swolchok

kernels/optimized/cpu/op_log_softmax.cpp

@@ -54,33 +54,49 @@ void log_softmax_kernel(const Tensor& input, int64_t dim, Tensor& out) {
   }
 
   if (dim == input.dim() - 1) {
-    at::native::serial_vec_log_softmax_lastdim_range(
-        input_data_base,
-        output_data_base,
-        dim_size,
-        at::native::vec_log_softmax_lastdim_chunk_size<IN_T>(
-            executorch::extension::internal::GRAIN_SIZE, outer_size, dim_size),
-        // TODO: parallelize.
+    ::executorch::extension::parallel_for(
         0,
-        outer_size);
+        outer_size,
+        ::executorch::extension::internal::GRAIN_SIZE,
+        [&](const auto begin, const auto end) {
+          at::native::serial_vec_log_softmax_lastdim_range(
+              input_data_base,
+              output_data_base,
+              dim_size,
+              at::native::vec_log_softmax_lastdim_chunk_size<IN_T>(
+                  executorch::extension::internal::GRAIN_SIZE,
+                  outer_size,
+                  dim_size),
+              begin,
+              end);
+        });
   } else {
     // BLOCK_SIZE in PyTorch is intended for server CPUs; let's
     // halve it to try and have a better chance of fitting in mobile
     // chip caches.
-    const auto [chunk_size, num_chunks] =
+    const auto [chunk_size_binding, num_chunks_binding] =
         at::native::vec_logsoftmax_chunk_size_and_num_chunks<
             float,
             /*BLOCK_SIZE=*/64 * 1024>(inner_size, dim_size);
-    at::native::serial_vec_logsoftmax_range(
-        input_data_base,
-        output_data_base,
-        inner_size,
-        chunk_size,
-        num_chunks,
-        dim_size,
-        // TODO: parallelize
+    // Work around "capturing a structured binding is not yet supported in
+    // OpenMP".
+    const auto chunk_size = chunk_size_binding;
+    const auto num_chunks = num_chunks_binding;
+    ::executorch::extension::parallel_for(
         0,
-        outer_size * num_chunks);
+        outer_size * num_chunks,
+        ::executorch::extension::internal::GRAIN_SIZE,
+        [&](const auto begin, const auto end) {
+          at::native::serial_vec_logsoftmax_range(
+              input_data_base,
+              output_data_base,
+              inner_size,
+              chunk_size,
+              num_chunks,
+              dim_size,
+              begin,
+              end);
+        });
   }
   return;
 }