# Cross-Feature Transfer Learning for Efficient Tensor Program Generation
## 4 Evaluation
### Dataset Sampling  
A measurement record corresponding to a task in the dataset is encapsulated within JSON files, delineating three crucial components: first, the input information and the schedules generated (i); second, the measured performance across multiple runs (r); and \begin{table}
\begin{tabular}{l l} \hline \hline
**Hyperparameter** & **Value** \\ \hline Batch & 16, 32, 64, **256**, 512 \\ Epoch & 100, **200**, 400 \\ Learning Rate & 1 \(\times\) 10\({}^{-4}\) \\ Attention Head (fine-tuning) & 6 \\ \#Unrolling Steps for Attention Head & 2 \\ Optimizer & Adam \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Autotuner’s Hyperparameters. finally, the version information (v). The concrete manifestation of such a measured record for a randomly chosen task is depicted in Listing 1. This exemplar showcases an intricate array of details, including explicit hardware specifications such as \"llvm-keys =arm_cpu, cpu-device=arm_cpu-link-params=0\", intricate tensor information, and the automatically generated scheduling primitives (e.g., CI, SP, etc. ), along with their respective parameters. To rectify erroneous measurements, we incorporate warm runs for each measurement and meticulously exclude any configurations deemed invalid. In Table 5, a comprehensive list of scheduling primitives, derived from measurements conducted on hardware utilizing TVM, is presented. Each abbreviation corresponds to a specific scheduling step, offering a succinct representation of the intricacies involved in the optimization process. These primitives encapsulate a range of tasks, from annotation (AN) and fusing (FU) steps to pragma (PR) application and reordering (RE) procedures. Notably, the table encompasses more intricate steps such as storage alignment (SA), compute at (CA), and compute in-line (CI) steps, highlighting the nuanced nature of the optimization strategies employed. Additionally, the inclusion of cache-related steps such as cache read (CHR) and cache write (CHW) underscores the importance of memory considerations in optimizing performance. A schedule constitutes a compilation of computational transformations, often referred to as schedule primitives, that are applied to the loops within a program, thereby modifying the sequence of computations. Diverse schedules contribute to varying degrees of locality and performance for tensor programs. Consequently, it becomes imperative to delve into the search space and autonomously generate optimized schedules to enhance overall efficiency. ```
{ ''i'';[[''{[''{[''{[''{[''{[''{b401c3 da78 ae0d8352 eee38076266}]}'', 1, 8, 8, 960, 5, 5, 960, 1, 1, 1, 1, 960, 1, 8, 8, 960]'', ''11 vm-keys=arm_cpu,cpu-device=arm_cpu-link-params=0\",[24,64,64,0,0,0,0,0], '', 2, [], [[], [[''CI'', 10], [''CI'', 9], [''CI'', 8], [''CI'', 7], [''CI'', 6], [''CI'', ', 5], [''SP'', 3, 0, 1, [1, 1, 1], 1], [''SP'', 3, 4, 8, [2, 2, 2], 1], [''SP'', 3, 8, 8, [4, 1, 2], 1], [''SP'', 3, 12, 960, [16, 12, 1], [[''SP'', 3, 16, 5, 1], [1, 1], [''SP'', 3, 18, 5, [1, 1], 1], [''R'', 3, 0, 4, 8, 12, 1, 5, 9, 13, 16, 18, 2, 6, 10, 14, 17, 19, 3, 7, 11, 15], [''CA'', 1, 3, 4], [''FU'', 3, [0, 1, 2, 3, 4]], [''AN'', 3, 0, 3], [''PU'', 11, [0, 1, 2, 3], [''AN'', 11, 0, 3], [''PR'', 3, 0, '' auto_unroll_max_step864'], [''AN'', 3, 15, 2]]], ''r'';[[[0.000501874, 0.000620504, 0.0006003134, 0.000600345, 0.000598904, 0.000599284, 0.000599315, 0.000600214], 0, 6.08456, 1700954668], ''v'';'v0.6'';'v0.6' }
```  
We conducted a comprehensive exploration of hardware measurements, extending our analysis to encompass two additional hardware platforms: NVIDIA's H100 and ARM A64 FX. This endeavor aimed to unravel valuable insights from the recorded data, subsequently employed as embeddings in our attention-based autotuner. The cumulative count of measurements amassed across both hardware platforms reached an impressive total of 9,232,000. On the H100 hardware, the automatically generated schedule sequence lengths exhibited a diverse spectrum, spanning from 5 to 69.