Datasets:

DavidBShan
/

asb-bench-pub

"""Cut Cross-Entropy (CCE) — opt-in fused linear-CE for the large-vocab LM head.
Qwen3.5/3.6 have a ~152k-token vocab, so the LM head's logit tensor ([tokens, 152k]) is the
single largest activation in an SFT step. Liger's fused-linear-CE (the current default) chunks it;
Cut Cross-Entropy (apple/ml-cross-entropy, arXiv 2411.09009) goes further — it never materializes
the logits at all (correct-token logit + a streamed log-sum-exp in SRAM) and skips gradient
contributions below bf16 precision. Published head-to-head at 256k vocab: ~2.1x faster and ~21%
lower memory than Liger; the win grows as vocab >> hidden, exactly our regime.
Safety (via engine.kernel_safety.run_gpu_self_test):
  - Gated by AUTOSLM_CCE=1; default OFF.
  - Runs a numeric self-test (CCE loss+grad vs eager CE) on the live GPU and only patches if it
    matches within tolerance; any import/patch/self-test failure leaves the model untouched
    (correctness over speed — the run falls back to Liger/eager).
This is a production-scale capability: like every fused kernel it pays a one-time JIT cost, so on a
short micro-benchmark it can look neutral/negative — the win shows on real (long) runs.
"""

from __future__ import annotations

import os

from autoslm.engine.kernel_safety import run_gpu_self_test


def _cce_enabled() -> bool:
    # strip()+lower() so a blank/whitespace value (e.g. AUTOSLM_CCE=" " from env forwarding) is
    # treated as OFF. The falsey set mirrors autoslm.spec._FALSE_STRINGS so "no"/"off"/"none" (and
    # "false"/"0") all read as OFF too, not accidentally enabling the self-test + patching.
    return os.environ.get("AUTOSLM_CCE", "0").strip().lower() not in (
        "0",
        "false",
        "no",
        "off",
        "none",
        "",
    )


def cce_will_install() -> bool:
    """True only when CCE is enabled AND its package is importable — i.e. ``install_cce`` will
    really patch the loss path. Used by the trainer to decide whether to SUPPRESS Liger: suppress
    only when CCE will actually take over the loss, else an enabled-but-uninstallable CCE (e.g.
    RunPod, where the worker deps don't ship cut_cross_entropy) silently drops the run to slow
    eager cross-entropy instead of keeping the Liger fused-CE win."""
    if not _cce_enabled():
        return False
    try:
        import importlib.util

        return importlib.util.find_spec("cut_cross_entropy") is not None
    except Exception:
        return False


def _self_test() -> bool:
    """Numeric parity of CCE's linear_cross_entropy vs eager F.cross_entropy (loss + dhidden)."""
    return run_gpu_self_test(_self_test_body)


def _self_test_body() -> bool:
    import torch
    import torch.nn.functional as F

    try:
        from cut_cross_entropy import linear_cross_entropy
    except Exception as e:
        print("[cce] package not importable; skipping:", e)
        return False
    V, H, T = 152064, 2048, 64  # Qwen-scale vocab/hidden, a small token block
    h = torch.randn(T, H, device="cuda", dtype=torch.bfloat16, requires_grad=True)
    W = torch.randn(V, H, device="cuda", dtype=torch.bfloat16) / (H**0.5)
    labels = torch.randint(0, V, (T,), device="cuda")
    # CCE
    loss_cce = linear_cross_entropy(h, W, labels)
    loss_cce.backward()
    dh_cce = h.grad.clone()
    h.grad = None
    # Eager reference (fp32 logits)
    ref = F.cross_entropy((h.float() @ W.float().t()), labels)
    ref.backward()
    dh_ref = h.grad.clone()
    if not torch.allclose(loss_cce.float(), ref, atol=2e-2, rtol=2e-2):
        print(f"[cce] self-test FAILED loss ({loss_cce.item():.4f} vs {ref.item():.4f}); fallback")
        return False
    # CCE filters tiny grads, so allow a looser tolerance on dhidden.
    if not torch.allclose(dh_cce.float(), dh_ref.float(), atol=5e-2, rtol=5e-2):
        print("[cce] self-test FAILED dhidden parity; fallback")
        return False
    print("[cce] self-test passed (loss+dhidden parity vs eager)")
    return True


def install_cce(model) -> bool:
    """Patch the model's LM-head loss to Cut Cross-Entropy, IFF enabled + self-test passes.
    Returns True if installed. `model` may be a PEFT-wrapped trainer model — we patch the underlying
    HF model. Never raises (correctness-preserving: on any failure the caller keeps its existing
    Liger/eager loss path)."""
    if not _cce_enabled():
        return False
    try:
        if not _self_test():
            return False
        from cut_cross_entropy.transformers import cce_patch

        # Unwrap PEFT to reach the HF base model the patch keys off (model_type).
        base = model
        for attr in ("get_base_model", "base_model"):
            inner = getattr(base, attr, None)
            base = inner() if callable(inner) else (inner or base)
        cce_patch(base)
        print(f"[cce] Cut Cross-Entropy installed on {type(base).__name__}")
        return True
    except Exception as e:
        print("[cce] install skipped:", e)
        return False

	"""Cut Cross-Entropy (CCE) — opt-in fused linear-CE for the large-vocab LM head.

	Qwen3.5/3.6 have a ~152k-token vocab, so the LM head's logit tensor ([tokens, 152k]) is the
	single largest activation in an SFT step. Liger's fused-linear-CE (the current default) chunks it;
	Cut Cross-Entropy (apple/ml-cross-entropy, arXiv 2411.09009) goes further — it never materializes
	the logits at all (correct-token logit + a streamed log-sum-exp in SRAM) and skips gradient
	contributions below bf16 precision. Published head-to-head at 256k vocab: ~2.1x faster and ~21%
	lower memory than Liger; the win grows as vocab >> hidden, exactly our regime.

	Safety (via engine.kernel_safety.run_gpu_self_test):
	- Gated by AUTOSLM_CCE=1; default OFF.
	- Runs a numeric self-test (CCE loss+grad vs eager CE) on the live GPU and only patches if it
	matches within tolerance; any import/patch/self-test failure leaves the model untouched
	(correctness over speed — the run falls back to Liger/eager).
	This is a production-scale capability: like every fused kernel it pays a one-time JIT cost, so on a
	short micro-benchmark it can look neutral/negative — the win shows on real (long) runs.
	"""

	from __future__ import annotations

	import os

	from autoslm.engine.kernel_safety import run_gpu_self_test


	def _cce_enabled() -> bool:
	# strip()+lower() so a blank/whitespace value (e.g. AUTOSLM_CCE=" " from env forwarding) is
	# treated as OFF. The falsey set mirrors autoslm.spec._FALSE_STRINGS so "no"/"off"/"none" (and
	# "false"/"0") all read as OFF too, not accidentally enabling the self-test + patching.
	return os.environ.get("AUTOSLM_CCE", "0").strip().lower() not in (
	"0",
	"false",
	"no",
	"off",
	"none",
	"",
	)


	def cce_will_install() -> bool:
	"""True only when CCE is enabled AND its package is importable — i.e. ``install_cce`` will
	really patch the loss path. Used by the trainer to decide whether to SUPPRESS Liger: suppress
	only when CCE will actually take over the loss, else an enabled-but-uninstallable CCE (e.g.
	RunPod, where the worker deps don't ship cut_cross_entropy) silently drops the run to slow
	eager cross-entropy instead of keeping the Liger fused-CE win."""
	if not _cce_enabled():
	return False
	try:
	import importlib.util

	return importlib.util.find_spec("cut_cross_entropy") is not None
	except Exception:
	return False


	def _self_test() -> bool:
	"""Numeric parity of CCE's linear_cross_entropy vs eager F.cross_entropy (loss + dhidden)."""
	return run_gpu_self_test(_self_test_body)


	def _self_test_body() -> bool:
	import torch
	import torch.nn.functional as F

	try:
	from cut_cross_entropy import linear_cross_entropy
	except Exception as e:
	print("[cce] package not importable; skipping:", e)
	return False
	V, H, T = 152064, 2048, 64 # Qwen-scale vocab/hidden, a small token block
	h = torch.randn(T, H, device="cuda", dtype=torch.bfloat16, requires_grad=True)
	W = torch.randn(V, H, device="cuda", dtype=torch.bfloat16) / (H**0.5)
	labels = torch.randint(0, V, (T,), device="cuda")
	# CCE
	loss_cce = linear_cross_entropy(h, W, labels)
	loss_cce.backward()
	dh_cce = h.grad.clone()
	h.grad = None
	# Eager reference (fp32 logits)
	ref = F.cross_entropy((h.float() @ W.float().t()), labels)
	ref.backward()
	dh_ref = h.grad.clone()
	if not torch.allclose(loss_cce.float(), ref, atol=2e-2, rtol=2e-2):
	print(f"[cce] self-test FAILED loss ({loss_cce.item():.4f} vs {ref.item():.4f}); fallback")
	return False
	# CCE filters tiny grads, so allow a looser tolerance on dhidden.
	if not torch.allclose(dh_cce.float(), dh_ref.float(), atol=5e-2, rtol=5e-2):
	print("[cce] self-test FAILED dhidden parity; fallback")
	return False
	print("[cce] self-test passed (loss+dhidden parity vs eager)")
	return True


	def install_cce(model) -> bool:
	"""Patch the model's LM-head loss to Cut Cross-Entropy, IFF enabled + self-test passes.

	Returns True if installed. `model` may be a PEFT-wrapped trainer model — we patch the underlying
	HF model. Never raises (correctness-preserving: on any failure the caller keeps its existing
	Liger/eager loss path)."""
	if not _cce_enabled():
	return False
	try:
	if not _self_test():
	return False
	from cut_cross_entropy.transformers import cce_patch

	# Unwrap PEFT to reach the HF base model the patch keys off (model_type).
	base = model
	for attr in ("get_base_model", "base_model"):
	inner = getattr(base, attr, None)
	base = inner() if callable(inner) else (inner or base)
	cce_patch(base)
	print(f"[cce] Cut Cross-Entropy installed on {type(base).__name__}")
	return True
	except Exception as e:
	print("[cce] install skipped:", e)
	return False