Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: TypeError
Message: Couldn't cast array of type
struct<batch_size: struct<type: string>, num_index_heads: struct<type: string, value: int64, description: string>, index_head_dim: struct<type: string, value: int64, description: string>, page_size: struct<type: string, value: int64, description: string>, topk: struct<type: string, value: int64, description: string>, max_num_pages: struct<type: string, description: string>, num_pages: struct<type: string, description: string>, kv_cache_num_heads: struct<type: string, value: int64, description: string>, head_dim_with_scale: struct<type: string, value: int64, description: string>>
to
{'num_tokens': {'type': Value('string'), 'description': Value('string')}, 'num_qo_heads': {'type': Value('string'), 'value': Value('int64'), 'description': Value('string')}, 'head_dim_ckv': {'type': Value('string'), 'value': Value('int64'), 'description': Value('string')}, 'head_dim_kpe': {'type': Value('string'), 'value': Value('int64'), 'description': Value('string')}, 'page_size': {'type': Value('string'), 'value': Value('int64'), 'description': Value('string')}, 'topk': {'type': Value('string'), 'value': Value('int64'), 'description': Value('string')}, 'num_pages': {'type': Value('string'), 'description': Value('string')}}
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 265, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 120, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2224, in cast_table_to_schema
cast_array_to_feature(
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1795, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2092, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<batch_size: struct<type: string>, num_index_heads: struct<type: string, value: int64, description: string>, index_head_dim: struct<type: string, value: int64, description: string>, page_size: struct<type: string, value: int64, description: string>, topk: struct<type: string, value: int64, description: string>, max_num_pages: struct<type: string, description: string>, num_pages: struct<type: string, description: string>, kv_cache_num_heads: struct<type: string, value: int64, description: string>, head_dim_with_scale: struct<type: string, value: int64, description: string>>
to
{'num_tokens': {'type': Value('string'), 'description': Value('string')}, 'num_qo_heads': {'type': Value('string'), 'value': Value('int64'), 'description': Value('string')}, 'head_dim_ckv': {'type': Value('string'), 'value': Value('int64'), 'description': Value('string')}, 'head_dim_kpe': {'type': Value('string'), 'value': Value('int64'), 'description': Value('string')}, 'page_size': {'type': Value('string'), 'value': Value('int64'), 'description': Value('string')}, 'topk': {'type': Value('string'), 'value': Value('int64'), 'description': Value('string')}, 'num_pages': {'type': Value('string'), 'description': Value('string')}}Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
MLSys 2026 FlashInfer-Bench Challenge Dataset
This repository contains the FlashInfer-Bench dataset for the MLSys 2026 Kenrel Generation Challenge.
This dataset targets to be used in the FlashInfer-Bench benchmark system.
It follows the FlashInfer Trace Schema. To use the dataset in the competition, please refer to our starter kit.
Download
Use this command to download the dataset:
git lfs install
git clone https://huggingface.co/datasets/flashinfer-ai/mlsys26-contest
Set the environment variable so that FlashInfer-Bench can find the dataset:
export FIB_DATASET_PATH=/path/to/mlsys26-contest
Tasks
This dataset contains the definitions and workloads for these kernels:
- Fused Mixture of Experts (MoE)
- Gated Delta Network (GDN)
- DeepSeek Sparse Attention (DSA)
Dataset Structure
It is organized as follows:
mlsys26-contest/
├── definitions/
└── workloads/
These components are provided in the dataset:
- Definition: describes the input, output, and computation logic of a kernel task.
- Workload: describes the inputs for a definition during real inference. This will be used to benchmark the Solution you provided.
During benchmarking, these components should be provided or generated:
- Solution: provided by participants, your implementation of the kernel task.
- Trace: generated by FlashInfer-Bench, the performance and correctness results of your solution on the workloads.
- Downloads last month
- 573