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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
model: string
swapped_linears: int64
int4_bytes_gb: double
ppl_bf16: double
ppl_w4: double
decode_tok_s: double
sample: string
bf16: struct<tok_s: double, ppl: double>
  child 0, tok_s: double
  child 1, ppl: double
bnb_nf4: struct<tok_s: double, ppl: double>
  child 0, tok_s: double
  child 1, ppl: double
to
{'model': Value('string'), 'bf16': {'tok_s': Value('float64'), 'ppl': Value('float64')}, 'bnb_nf4': {'tok_s': Value('float64'), 'ppl': Value('float64')}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, 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 127, 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 483, 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 2951, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, 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 547, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, 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 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              model: string
              swapped_linears: int64
              int4_bytes_gb: double
              ppl_bf16: double
              ppl_w4: double
              decode_tok_s: double
              sample: string
              bf16: struct<tok_s: double, ppl: double>
                child 0, tok_s: double
                child 1, ppl: double
              bnb_nf4: struct<tok_s: double, ppl: double>
                child 0, tok_s: double
                child 1, ppl: double
              to
              {'model': Value('string'), 'bf16': {'tok_s': Value('float64'), 'ppl': Value('float64')}, 'bnb_nf4': {'tok_s': Value('float64'), 'ppl': Value('float64')}}
              because column names don't match

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Roofline-First Decoding: results

Result files for roofline-decoding: a fused 4-bit dequantise-plus-GEMV Triton kernel for batch-1 decoding, measured against the memory-bandwidth roofline of one RTX 3060 12GB.

Files

  • roofline.json: measured copy bandwidth (323.9 GB/s) and the ceilings table, ceiling = bandwidth / (weight bytes + KV-cache read at 2,048 tokens)
  • baselines.json: bf16 and bitsandbytes NF4 decode speed and probe perplexity (Qwen2.5-1.5B-Instruct)
  • decode.json: end-to-end decode with every linear swapped for the W4A16 kernel (197 layers)
  • kernels.json: per-shape kernel latencies (reference, v1, v2, v3) and % of roofline
  • pct_roofline.png: the percent-of-roofline chart

Key numbers (Qwen2.5-1.5B-Instruct, batch-1 greedy)

Setup tok/s Probe perplexity % of its roofline
bf16 (HF) 31.3 12.91 29.8%
bitsandbytes NF4 25.7 18.98 6.1%
own W4 kernel (v3) 25.5 15.76 6.1%

Caveats

  • Probe perplexity uses four sentences (noisy), and bitsandbytes quantises different layers than the swap, so the perplexity gap is directional.
  • The 6.1% figure is relative to the all-int4 ceiling (411.8 tok/s). The swapped model keeps the tied embedding / lm_head in bf16 and reads 1.263 GB of weights per token, so against bytes actually moved it is about 10% of measured bandwidth (model ceiling about 245 tok/s).
  • In the per-shape microbenchmarks (kernels.json) v3 is not faster than v2 on any of the five shapes measured; end-to-end decoding uses v3.
  • One GPU, one prompt for timing, batch-1 and greedy only. No Marlin or llama.cpp numbers.
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