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fix(sdnq): run benchmark correctness checks with swapped-in eager prep

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Toggling torch._dynamo.config.disable to force eager input prep fails on
newer torch: a fullgraph-compiled function called inside a disable window
raises 'found no compiled frames', failing every check and poisoning the
first compiled call afterwards. Swap the module-global prep function for
its eager inner instead, matching the dequant section's mechanism.

- store per-check errors and exceptions in the json report, so remote
  reports carry the failure mode
This commit is contained in:
CalamitousFelicitousness
2026-07-12 17:30:16 +01:00
parent 41659f9f95
commit 44146ee096
1 changed files with 45 additions and 33 deletions
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cli/sdnq-attention-benchmark.py
+45 -33
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@@ -883,47 +883,59 @@ def run_correctness():
table.add_column("result", justify="center")
panel = Panel(table, title="kernel correctness", subtitle="[dim]small shapes, vs fp32 sdpa reference; stress rows pass on finite output[/dim]", box=ROUNDED_BOX, expand=False)
failed = []
dynamo_disable = getattr(torch._dynamo.config, "disable", False) # pylint: disable=protected-access
torch._dynamo.config.disable = True # pylint: disable=protected-access # checks target kernel behavior, run the input prep eager
details = {}
# checks target kernel behavior, so the input prep runs eager via a module-global swap.
# do not toggle torch._dynamo.config.disable for this: newer torch raises "found no
# compiled frames" when a fullgraph-compiled function is called inside a disable window,
# failing every check and poisoning the first compiled call afterwards
from modules.sdnq.kernels import triton_atten as atten_module
compiled_prep = atten_module.get_attn_inputs
inner_prep = getattr(compiled_prep, "_torchdynamo_orig_callable", None)
if inner_prep is not None:
atten_module.get_attn_inputs = inner_prep
progress, task = live_progress()
with Live(Group(panel, progress), console=console, refresh_per_second=4) as live:
for index, (name, (cq, ck, cv), kwargs, sdnq_kwargs) in enumerate(checks, start=1):
progress.reset(task, description=f"check {index}/{len(checks)} {name}")
ref_kwargs = dict(kwargs)
if ref_kwargs.get("attn_mask", None) is not None and torch.is_floating_point(ref_kwargs["attn_mask"]):
ref_kwargs["attn_mask"] = ref_kwargs["attn_mask"].to(torch.float32) # stock sdpa requires the additive mask dtype to match query
ref = fp32_reference(cq, ck, cv, **ref_kwargs)
try:
plain = sdnq_triton_atten(cq, ck, cv, do_quantize=False, **kwargs)
quant_kwargs = dict(matmul_dtype="auto", pv_matmul_dtype="auto", **kwargs)
quant_kwargs.update(sdnq_kwargs)
quant = sdnq_triton_atten(cq, ck, cv, **quant_kwargs)
err_plain = rel_err(plain, ref)
err_quant = rel_err(quant, ref)
max_err = max_token_err(quant, ref)
finite = bool(torch.isfinite(plain).all().item() and torch.isfinite(quant).all().item())
if name.startswith("stress:"):
ok = finite
else:
ok = finite and err_plain < 0.01 and err_quant < 0.2
if not ok:
try:
with Live(Group(panel, progress), console=console, refresh_per_second=4) as live:
for index, (name, (cq, ck, cv), kwargs, sdnq_kwargs) in enumerate(checks, start=1):
progress.reset(task, description=f"check {index}/{len(checks)} {name}")
ref_kwargs = dict(kwargs)
if ref_kwargs.get("attn_mask", None) is not None and torch.is_floating_point(ref_kwargs["attn_mask"]):
ref_kwargs["attn_mask"] = ref_kwargs["attn_mask"].to(torch.float32) # stock sdpa requires the additive mask dtype to match query
ref = fp32_reference(cq, ck, cv, **ref_kwargs)
try:
plain = sdnq_triton_atten(cq, ck, cv, do_quantize=False, **kwargs)
quant_kwargs = dict(matmul_dtype="auto", pv_matmul_dtype="auto", **kwargs)
quant_kwargs.update(sdnq_kwargs)
quant = sdnq_triton_atten(cq, ck, cv, **quant_kwargs)
err_plain = rel_err(plain, ref)
err_quant = rel_err(quant, ref)
max_err = max_token_err(quant, ref)
finite = bool(torch.isfinite(plain).all().item() and torch.isfinite(quant).all().item())
if name.startswith("stress:"):
ok = finite
else:
ok = finite and err_plain < 0.01 and err_quant < 0.2
if not ok:
failed.append(name)
details[name] = dict(kernel_err=err_plain, int8_err=err_quant, max_token_err=max_err, finite=finite, ok=ok)
verdict = "[green]pass[/green]" if ok else "[red]fail[/red]"
if not finite:
verdict = "[red]non-finite[/red]"
table.add_row(name, err_cell(err_plain), err_cell(err_quant), err_cell(max_err), verdict)
except Exception as e:
failed.append(name)
verdict = "[green]pass[/green]" if ok else "[red]fail[/red]"
if not finite:
verdict = "[red]non-finite[/red]"
table.add_row(name, err_cell(err_plain), err_cell(err_quant), err_cell(max_err), verdict)
except Exception as e:
failed.append(name)
table.add_row(name, "-", "-", "-", failure_text(e))
live.update(panel)
details[name] = dict(error=error_summary(e, 200))
table.add_row(name, "-", "-", "-", failure_text(e))
live.update(panel)
finally:
atten_module.get_attn_inputs = compiled_prep
console.line() # separate sections; Live's final frame also lacks a trailing newline when output is piped
transcript.append(panel)
torch._dynamo.config.disable = dynamo_disable # pylint: disable=protected-access
if failed:
emit(f"[red]failed checks: {', '.join(failed)}[/red]")
if "head dim 40 + hadamard" in failed and shared.opts.sdnq_attention_use_hadamard:
emit("[yellow]current config has Use Hadamard enabled: models with non power-of-2 head dims (SD 1.5) fail at generation with it[/yellow]")
report["correctness"] = dict(failed=failed, total=len(checks))
report["correctness"] = dict(failed=failed, total=len(checks), checks=details)
return failed