GPUMODE/huggingface_op_trace

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]):

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:

((T([48, 24, 28, 28], f16),), {})

= Function called with one 48×24×28×28 float16 tensor, no keyword arguments

Multiple tensors:

((T([8, 8, 8, 8, 8], f16), T([8, 8, 8, 8, 8], f16)), {})

= Function with two identical 5D tensors

Mixed arguments:

((T([128, 256], f16), [1024, 249, 249]), {'dtype': torch.float16, 'device': 'cuda'})

= Function with tensor, list, and keyword arguments

Complex nested:

(([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 for these traces and intuitive format