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repro-sf-mamba

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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
equivalence: struct<max_abs_err_fold_with_reset: double, max_abs_err_fold_no_reset_control: double, config: strin (... 2 chars omitted)
  child 0, max_abs_err_fold_with_reset: double
  child 1, max_abs_err_fold_no_reset_control: double
  child 2, config: string
gpu: string
kernel_bench: struct<T-stage3: struct<config: struct<B: int64, D: int64, L: int64, S: int64>, ms_per_call_by_B2: s (... 982 chars omitted)
  child 0, T-stage3: struct<config: struct<B: int64, D: int64, L: int64, S: int64>, ms_per_call_by_B2: struct<1: double,  (... 157 chars omitted)
      child 0, config: struct<B: int64, D: int64, L: int64, S: int64>
          child 0, B: int64
          child 1, D: int64
          child 2, L: int64
          child 3, S: int64
      child 1, ms_per_call_by_B2: struct<1: double, 2: double, 4: double, 8: double, 16: double, 32: double, 64: double, 128: double>
          child 0, 1: double
          child 1, 2: double
          child 2, 4: double
          child 3, 8: double
          child 4, 16: double
          child 5, 32: double
          child 6, 64: double
          child 7, 128: double
      child 2, baseline_ms: double
      child 3, best_B2: int64
      child 4, best_ms: double
      child 5, speedup_pct: double
  child 1, T-stage4: struct<config: struct<B: int64, D: int64, L: int64, S: int64>, ms_per_call_by_B2: struct<1: double,  (... 157 chars omitted)
      child 0, config: struct<B: int64, D: int64, L: int64, S: int64>
          child 0, B: int64
          chi
...
        child 3, S: int64
      child 1, ms_per_call_by_B2: struct<1: double, 2: double, 4: double, 8: double, 16: double, 32: double, 64: double, 128: double>
          child 0, 1: double
          child 1, 2: double
          child 2, 4: double
          child 3, 8: double
          child 4, 16: double
          child 5, 32: double
          child 6, 64: double
          child 7, 128: double
      child 2, baseline_ms: double
      child 3, best_B2: int64
      child 4, best_ms: double
      child 5, speedup_pct: double
modes: struct<uni: struct<per_position_acc: list<item: double>, nonfinal_mean_acc: double, final_acc: doubl (... 200 chars omitted)
  child 0, uni: struct<per_position_acc: list<item: double>, nonfinal_mean_acc: double, final_acc: double>
      child 0, per_position_acc: list<item: double>
          child 0, item: double
      child 1, nonfinal_mean_acc: double
      child 2, final_acc: double
  child 1, swap: struct<per_position_acc: list<item: double>, nonfinal_mean_acc: double, final_acc: double>
      child 0, per_position_acc: list<item: double>
          child 0, item: double
      child 1, nonfinal_mean_acc: double
      child 2, final_acc: double
  child 2, bidir: struct<per_position_acc: list<item: double>, nonfinal_mean_acc: double, final_acc: double>
      child 0, per_position_acc: list<item: double>
          child 0, item: double
      child 1, nonfinal_mean_acc: double
      child 2, final_acc: double
device: string
chance: double
task: string
to
{'task': Value('string'), 'chance': Value('float64'), 'device': Value('string'), 'modes': {'uni': {'per_position_acc': List(Value('float64')), 'nonfinal_mean_acc': Value('float64'), 'final_acc': Value('float64')}, 'swap': {'per_position_acc': List(Value('float64')), 'nonfinal_mean_acc': Value('float64'), 'final_acc': Value('float64')}, 'bidir': {'per_position_acc': List(Value('float64')), 'nonfinal_mean_acc': Value('float64'), 'final_acc': Value('float64')}}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                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 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              equivalence: struct<max_abs_err_fold_with_reset: double, max_abs_err_fold_no_reset_control: double, config: strin (... 2 chars omitted)
                child 0, max_abs_err_fold_with_reset: double
                child 1, max_abs_err_fold_no_reset_control: double
                child 2, config: string
              gpu: string
              kernel_bench: struct<T-stage3: struct<config: struct<B: int64, D: int64, L: int64, S: int64>, ms_per_call_by_B2: s (... 982 chars omitted)
                child 0, T-stage3: struct<config: struct<B: int64, D: int64, L: int64, S: int64>, ms_per_call_by_B2: struct<1: double,  (... 157 chars omitted)
                    child 0, config: struct<B: int64, D: int64, L: int64, S: int64>
                        child 0, B: int64
                        child 1, D: int64
                        child 2, L: int64
                        child 3, S: int64
                    child 1, ms_per_call_by_B2: struct<1: double, 2: double, 4: double, 8: double, 16: double, 32: double, 64: double, 128: double>
                        child 0, 1: double
                        child 1, 2: double
                        child 2, 4: double
                        child 3, 8: double
                        child 4, 16: double
                        child 5, 32: double
                        child 6, 64: double
                        child 7, 128: double
                    child 2, baseline_ms: double
                    child 3, best_B2: int64
                    child 4, best_ms: double
                    child 5, speedup_pct: double
                child 1, T-stage4: struct<config: struct<B: int64, D: int64, L: int64, S: int64>, ms_per_call_by_B2: struct<1: double,  (... 157 chars omitted)
                    child 0, config: struct<B: int64, D: int64, L: int64, S: int64>
                        child 0, B: int64
                        chi
              ...
                      child 3, S: int64
                    child 1, ms_per_call_by_B2: struct<1: double, 2: double, 4: double, 8: double, 16: double, 32: double, 64: double, 128: double>
                        child 0, 1: double
                        child 1, 2: double
                        child 2, 4: double
                        child 3, 8: double
                        child 4, 16: double
                        child 5, 32: double
                        child 6, 64: double
                        child 7, 128: double
                    child 2, baseline_ms: double
                    child 3, best_B2: int64
                    child 4, best_ms: double
                    child 5, speedup_pct: double
              modes: struct<uni: struct<per_position_acc: list<item: double>, nonfinal_mean_acc: double, final_acc: doubl (... 200 chars omitted)
                child 0, uni: struct<per_position_acc: list<item: double>, nonfinal_mean_acc: double, final_acc: double>
                    child 0, per_position_acc: list<item: double>
                        child 0, item: double
                    child 1, nonfinal_mean_acc: double
                    child 2, final_acc: double
                child 1, swap: struct<per_position_acc: list<item: double>, nonfinal_mean_acc: double, final_acc: double>
                    child 0, per_position_acc: list<item: double>
                        child 0, item: double
                    child 1, nonfinal_mean_acc: double
                    child 2, final_acc: double
                child 2, bidir: struct<per_position_acc: list<item: double>, nonfinal_mean_acc: double, final_acc: double>
                    child 0, per_position_acc: list<item: double>
                        child 0, item: double
                    child 1, nonfinal_mean_acc: double
                    child 2, final_acc: double
              device: string
              chance: double
              task: string
              to
              {'task': Value('string'), 'chance': Value('float64'), 'device': Value('string'), 'modes': {'uni': {'per_position_acc': List(Value('float64')), 'nonfinal_mean_acc': Value('float64'), 'final_acc': Value('float64')}, 'swap': {'per_position_acc': List(Value('float64')), 'nonfinal_mean_acc': Value('float64'), 'final_acc': Value('float64')}, 'bidir': {'per_position_acc': List(Value('float64')), 'nonfinal_mean_acc': Value('float64'), 'final_acc': Value('float64')}}}
              because column names don't match

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Check out the documentation for more information.

Reproduction: SF-Mamba: Rethinking State Space Model for Vision

Independent reproduction of ICML 2026 paper R9AUrEgZEq (arXiv 2603.16423) by Yoshimura, Hayashi, Hoshino, Wang, Ohashi (Sony).

No official code or checkpoints are released ("We will release the source code after publication"). SF-Mamba builds on the public NVlabs/MambaVision backbone (MambaVisionMixer(d_state=8, d_conv=3, expand=1), SSM on dim/2 channels).

What is reproduced

Claim Approach Script
C1 auxiliary patch swapping (Sec 3.2, Eq 2-3) mechanism test: tiny unidirectional Mamba vs +aux-swap vs bidirectional on a synthetic future-information task claim1_swap.py
C2 batch folding + periodic state reset, 110–180% SSM speedup (Sec 3.3, Fig 3-4) (a) fp64 reference proof of fold+reset ⇔ unfolded equality; (b) upstream mamba_ssm selective-scan CUDA kernel benchmark at the paper's exact Fig-4 configs on A100, sweeping B2 claim2_bench.py
C3 ImageNet acc/throughput of SF-Mamba-T/S/B accuracy: not reproducible (no code/weights; 300-epoch ×3 training infeasible). Throughput side: benchmark public MambaVision-T/S/B + Swin-T/ConvNeXt-T on A100 batch 128 and compare with the paper's Table 1 claim34_throughput.py
C4 tradeoff vs Vim-S / VMamba-T baseline numbers cross-checked against official releases + our A100 ordering claim34_throughput.py + literature
C5 COCO / ADE20K documented; training infeasible here (8-GPU mmdet/mmseg schedules) logbook page

Rerun 

# CPU-ok correctness parts
python claim2_bench.py --out claim2.json          # fp64 fold+reset equivalence
python claim1_swap.py --steps 1500 --seeds 3      # mechanism test (GPU faster)

# A100 job (HF Jobs)
hf jobs run --flavor a100-large --timeout 3600 -v ./:/repro \
  -v hf://buckets/<user>/<bucket>:/data \
  pytorch/pytorch:2.4.0-cuda12.1-cudnn9-devel bash /repro/job.sh
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