| --- |
| configs: |
| - config_name: default |
| data_files: |
| - split: runtime_filtered |
| path: "backend_bench_problems_runtime_filtered.parquet" |
| - split: runtime_filtered_scaling_factor_2 |
| path: "backend_bench_problems_runtime_filtered_threshhold_2.parquet" |
| - split: benchmark |
| path: "backend_bench_problems.parquet" |
| --- |
| |
| # Understanding Trace Files in BackendBench |
|
|
| ## Format |
| Trace files capture PyTorch operations and their arguments from real model executions: |
|
|
| ``` |
| Operator: operation_name |
| cnt: count, serialized_arguments |
| cnt: count, serialized_arguments |
| ... |
| ``` |
|
|
| ## Structure |
|
|
| **Operator line**: Specifies the PyTorch operation |
| ``` |
| Operator: aten.add.Tensor |
| Operator: aten.relu.default |
| Operator: aten.linear.default |
| ``` |
|
|
| **Count lines**: Show how often each argument combination was used |
| ``` |
| cnt: 42, ((T([10, 20], f16), T([10, 20], f16)), {}) |
| cnt: 0, ((T([5, 5], f32), T([5, 5], f32)), {}) |
| ``` |
|
|
| ## Reading Count Lines |
|
|
| **Count `42`**: This argument combination appeared 42 times in traced models |
| - **`cnt: 0`** = Synthetic/generated arguments (not from real models) |
| - **`cnt: >0`** = Real usage frequency from model traces |
|
|
| **Arguments**: Same format as serialized arguments - `((args), {kwargs})` |
|
|
| ## Complete Example |
|
|
| ``` |
| Operator: aten.add.Tensor |
| cnt: 156, ((T([1, 512, 768], f16), T([1, 512, 768], f16)), {}) |
| cnt: 89, ((T([32, 128], f32), T([32, 128], f32)), {}) |
| cnt: 0, ((T([10, 10], f16), T([10, 10], f16)), {}) |
| |
| Operator: aten.relu.default |
| cnt: 234, ((T([64, 256], f16),), {}) |
| ``` |
|
|
| This shows: |
| - `aten.add.Tensor` called 156 times with 1×512×768 tensors |
| - Same operation called 89 times with 32×128 tensors |
| - One synthetic test case (cnt: 0) |
| - `aten.relu.default` called 234 times with 64×256 tensor |
|
|
| ## Interpretation |
| Trace files provide real-world operation usage patterns, showing which tensor shapes and operations are most common in actual PyTorch models. These are fairly useful for debugging. |
|
|
| **Note: These may be deprecated in the future, but are described as they are currently included in the dataset / codebase.** |
|
|
|
|
| # Understanding Serialized Arguments in BackendBench |
| ## Format |
| BackendBench stores function arguments as strings containing all parameters needed to reproduce PyTorch operations: |
|
|
| ``` |
| ((arg1, arg2, ...), {'key1': val1, 'key2': val2}) |
| ``` |
|
|
| ## Tensor Representation |
| Tensors use the format `T([shape], dtype)` or `T([shape], dtype, [stride])`: |
|
|
| ```python |
| T([10, 20], f32) # 10×20 float32 tensor |
| T([1, 512, 768], f16) # 1×512×768 float16 tensor |
| T([64], i32) # 64-element int32 vector |
| ``` |
|
|
| **Data types**: `f16/f32/f64` (float), `bf16` (bfloat16), `i32/i64` (int), `b8` (bool) |
|
|
| ## Complete Examples |
|
|
| **Single tensor argument:** |
| ```python |
| ((T([48, 24, 28, 28], f16),), {}) |
| ``` |
| = Function called with one 48×24×28×28 float16 tensor, no keyword arguments |
|
|
| **Multiple tensors:** |
| ```python |
| ((T([8, 8, 8, 8, 8], f16), T([8, 8, 8, 8, 8], f16)), {}) |
| ``` |
| = Function with two identical 5D tensors |
|
|
| **Mixed arguments:** |
| ```python |
| ((T([128, 256], f16), [1024, 249, 249]), {'dtype': torch.float16, 'device': 'cuda'}) |
| ``` |
| = Function with tensor, list, and keyword arguments |
|
|
| **Complex nested:** |
| ```python |
| (([T([5, 5], f32), T([3, 3], i64), 42],), {'weight': T([3, 3], f32)}) |
| ``` |
| = Function with list containing tensors and numbers, plus tensor keyword argument |
|
|
| ## Argument Types |
| - **Tensors**: `T([shape], dtype)` format |
| - **Lists**: `[item1, item2, ...]` (can contain tensors) |
| - **Primitives**: `42`, `'hello'`, `True`, `None` |
| - **PyTorch objects**: `torch.float16`, `torch.strided` |
|
|
| ## Acknowledgements |
| We are extremely grateful for the folks working on [TritonBench](https://github.com/pytorch-labs/tritonbench/tree/main) for these traces and intuitive format |
