⚡️ Speed up method TensorChunker._split_value by 12% in PR #272 (14__robusttraining)

Sure, I can make the given code more efficient. Here are the main improvements.
1. Simplify the chunk splitting and dummy chunk creation to use fewer operations.
2. Avoid repetitive appending in a loop by pre-determining the length and constructing the final list accordingly.

Here is the optimized version of the provided code.



Improvements made.
1. Instead of using a conditional and loop to append dummy chunks, I pre-determine the number of necessary dummy chunks and extend the list in one operation.
2. Created the `dummy_chunk_flags` list in one go, thus avoiding repeated appending operations.

With these changes, the function should run faster while maintaining the intended behavior.

Author: codeflash-ai[bot]

src/ldp/nn/handlers/chunking.py

@@ -154,18 +154,18 @@ def _split_value(self, value):
         """
         if isinstance(value, torch.Tensor):
             chunks = list(torch.chunk(value, self.num_chunks, dim=0))
-            dummy_chunk_flags = []
-            for i in range(self.num_chunks):
-                if i >= len(chunks):
-                    # Chunk 0 will always exist, and we need only a batch of one ([:1])
-                    # to activate the model.
-                    # We use the first element of the existing chunks as real data to avoid
-                    # errors in the model that may expect a specific token structure.
-                    chunks.append(chunks[0][:1])
-                    dummy_chunk_flags.append(True)
-                else:
-                    dummy_chunk_flags.append(False)
+            real_chunks_len = len(chunks)
+            
+            # Pre-determining if dummy chunks are needed and their count
+            if real_chunks_len < self.num_chunks:
+                dummy_count = self.num_chunks - real_chunks_len
+                dummy_chunks = [chunks[0][:1]] * dummy_count
+                dummy_chunk_flags = [False] * real_chunks_len + [True] * dummy_count
+                chunks.extend(dummy_chunks)
+            else:
+                dummy_chunk_flags = [False] * self.num_chunks
 
             return chunks, dummy_chunk_flags
+        
         # Non-tensor values are replicated
         return [value] * self.num_chunks, None