ViperEkura
4dc5e923e0
- last-block epilogue (threadfence + counter elect) folds amax into hist[idx], reduces the window and publishes the next scale on device — zero extra launches per linear layer - _ScaleRing packs [hist | scale | counter] into one CUDA buffer; the eager hist-write / max / scale-copy chain and update() are gone - split FP8QuantizeParams out of FP8Params so each operator owns its fields; linear_forward/backward_fp8 take optional ring arguments - e2e 12L/dim1024/B4xT512 (fused AdamW): fp8 137.8ms/step vs bf16 210.3ms, 1.53x; fwd 1.82x, bwd 1.50x
393 lines
14 KiB
Python
393 lines
14 KiB
Python
"""FP8 training: scaling recipes, per-tensor state, and aten::linear dispatch.
|
|
|
|
Layered (see also ``ops/fp8.py`` for the CUDA interface adapter):
|
|
|
|
1. Kernel interface: ``ops.fp8`` — the only module touching the pybind.
|
|
2. Training state (this module): scaling *recipes* (TE-style delayed scaling
|
|
or dynamic current-amax scaling), per-tensor scales + amax history, and
|
|
the ``fp8_autocast`` context (like ``torch.autocast``).
|
|
3. aten::linear integration (this module): registers the CUDA impl and the
|
|
dtype guard.
|
|
|
|
Usage::
|
|
|
|
from astrai.extension.fp8 import fp8_autocast
|
|
|
|
with fp8_autocast(enabled=True, fp8_format="hybrid"):
|
|
logits = model(input_ids)
|
|
loss.backward() # fp8 backward runs wherever it is called: the
|
|
# forward captures the fmt/recipe/meta on the autograd node
|
|
|
|
Importing this module registers the aten::linear CUDA and AutogradCUDA
|
|
implementations.
|
|
|
|
Format defaults follow the ecosystem consensus: E4M3 for the forward pass,
|
|
E5M2 for the backward (gradient) pass ("hybrid"); every operand's scale is a
|
|
quantization step derived from its amax history by the active recipe.
|
|
"""
|
|
|
|
from contextlib import contextmanager
|
|
from dataclasses import dataclass
|
|
from enum import Enum
|
|
from typing import Optional
|
|
|
|
import torch
|
|
from torch.library import Library
|
|
|
|
from astrai.extension.ops.fp8 import (
|
|
linear_backward_fp8,
|
|
linear_forward_fp8,
|
|
)
|
|
|
|
# Max representable value per FP8 format (E4M3: 448, E5M2: 57344).
|
|
FP8_MAX = {"e4m3": 448.0, "e5m2": 57344.0}
|
|
|
|
|
|
class FP8Format(str, Enum):
|
|
"""Per-direction FP8 format. HYBRID = E4M3 forward / E5M2 backward."""
|
|
|
|
E4M3 = "e4m3"
|
|
E5M2 = "e5m2"
|
|
HYBRID = "hybrid"
|
|
|
|
def fwd(self) -> str:
|
|
return "e4m3" if self is FP8Format.HYBRID else self.value
|
|
|
|
def bwd(self) -> str:
|
|
return "e5m2" if self is FP8Format.HYBRID else self.value
|
|
|
|
|
|
class FP8Recipe:
|
|
"""Scale-from-amax policy; the scale computation is the injection point.
|
|
|
|
``scale_from_history`` receives the amax tensor for this operand (a ring
|
|
window for delayed scaling, the current amax for dynamic scaling) and
|
|
returns the quantization step: ``scale = (amax / FP8_MAX[fmt]) / 2^margin``.
|
|
"""
|
|
|
|
history_len: int = 16
|
|
margin: int = 0
|
|
|
|
def scale_from_history(self, amax: torch.Tensor, fmt: str) -> torch.Tensor:
|
|
raise NotImplementedError
|
|
|
|
|
|
@dataclass
|
|
class DelayedScaling(FP8Recipe):
|
|
"""TE-style delayed scaling: max over the amax history window.
|
|
|
|
The scale is computed from amax measured in *previous* steps (delayed one
|
|
step); the window length trades responsiveness against stability.
|
|
"""
|
|
|
|
history_len: int = 16
|
|
margin: int = 0
|
|
|
|
def scale_from_history(self, amax: torch.Tensor, fmt: str) -> torch.Tensor:
|
|
peak = amax.max()
|
|
return ((peak / FP8_MAX[fmt]) / (2**self.margin)).clamp_min(1e-12)
|
|
|
|
|
|
@dataclass
|
|
class DynamicScaling(FP8Recipe):
|
|
"""Current-amax scaling (torchao DYNAMIC): measure, then quantize.
|
|
|
|
No history — the scale is derived from the amax of the tensor being
|
|
quantized in the same step, at the cost of an extra reduction pass.
|
|
"""
|
|
|
|
history_len: int = 1
|
|
margin: int = 0
|
|
|
|
def scale_from_history(self, amax: torch.Tensor, fmt: str) -> torch.Tensor:
|
|
peak = amax.max()
|
|
return ((peak / FP8_MAX[fmt]) / (2**self.margin)).clamp_min(1e-12)
|
|
|
|
|
|
class _ScaleRing:
|
|
"""One operand's delayed-scaling state, packed for in-kernel finalization.
|
|
|
|
``state`` is a single float32 CUDA buffer ``[hist[n] | scale | counter]``
|
|
(``hist`` / ``scale`` are views). The quantize kernel's last-finishing
|
|
block records the freshly measured amax into ``hist[idx]``, reduces the
|
|
window and publishes the next step's scale entirely on device — the
|
|
Python-side hist-write / max / scale-write chain is gone. The counter
|
|
slot stays int32-zero (float bits) between launches. ``idx`` advances
|
|
host-side each step; ``margin`` is fixed by the recipe.
|
|
"""
|
|
|
|
__slots__ = ("recipe", "state", "hist", "scale", "idx", "initialized")
|
|
|
|
def __init__(self, device: torch.device, recipe: FP8Recipe):
|
|
self.recipe = recipe
|
|
n = recipe.history_len
|
|
# [hist | scale | counter]; the counter slot must start at int 0.
|
|
self.state = torch.zeros(n + 2, device=device, dtype=torch.float32)
|
|
self.hist = self.state[:n]
|
|
self.scale = self.state[n : n + 1]
|
|
self.idx = 0
|
|
self.initialized = False
|
|
|
|
def advance(self) -> None:
|
|
"""Rotate to the next history slot after an in-kernel finalize."""
|
|
self.idx = (self.idx + 1) % self.hist.numel()
|
|
|
|
def seed(self, t: torch.Tensor, fmt: str) -> None:
|
|
amax = t.abs().amax().to(torch.float32).clamp_min(1e-12)
|
|
self.hist.fill_(amax)
|
|
self.scale.copy_(self.recipe.scale_from_history(self.hist, fmt))
|
|
self.initialized = True
|
|
|
|
|
|
class FP8TensorMeta:
|
|
"""Per-weight delayed-scaling state: one ring per operand role.
|
|
|
|
Holds the ``w`` / ``x`` / ``g`` rings; fused kernels record the amax
|
|
while quantizing, so the scale used at step N reflects amax from steps
|
|
< N. DynamicScaling never allocates a meta — it measures the current
|
|
amax inline (``_dynamic_scale``), so it needs no history storage.
|
|
"""
|
|
|
|
__slots__ = ("w", "x", "g")
|
|
|
|
def __init__(self, device: torch.device, recipe: FP8Recipe):
|
|
self.w = _ScaleRing(device, recipe)
|
|
self.x = _ScaleRing(device, recipe)
|
|
self.g = _ScaleRing(device, recipe)
|
|
|
|
|
|
class FP8State:
|
|
"""Global fp8 training state: active recipe + per-tensor metas."""
|
|
|
|
def __init__(self):
|
|
self.enabled = False
|
|
self.recipe: FP8Recipe = DelayedScaling()
|
|
self.fp8_format: FP8Format = FP8Format.HYBRID
|
|
self._metas: dict[tuple, FP8TensorMeta] = {}
|
|
|
|
def get_weight_meta(self, w: torch.Tensor) -> FP8TensorMeta:
|
|
key = (w.data_ptr(), w.shape, w.dtype)
|
|
meta = self._metas.get(key)
|
|
if meta is None:
|
|
meta = FP8TensorMeta(w.device, self.recipe)
|
|
self._metas[key] = meta
|
|
return meta
|
|
|
|
def reset(self) -> None:
|
|
self.enabled = False
|
|
self._metas.clear()
|
|
|
|
|
|
# Global singleton: autograd backward runs on the engine worker threads, so
|
|
# thread-local state would lose the fp8 flag during loss.backward(). The GIL
|
|
# protects Python-side mutation; the CUDA kernels take their own mutex.
|
|
_state = FP8State()
|
|
|
|
|
|
def fp8_state() -> FP8State:
|
|
return _state
|
|
|
|
|
|
@contextmanager
|
|
def fp8_autocast(
|
|
enabled: bool = True,
|
|
update_interval: int = 16,
|
|
recipe: Optional[FP8Recipe] = None,
|
|
fp8_format: str = "hybrid",
|
|
margin: int = 0,
|
|
):
|
|
"""Autocast-style context: fp8 linear dispatch on this thread.
|
|
|
|
Usage::
|
|
|
|
with fp8_autocast(enabled=True, fp8_format="hybrid"):
|
|
logits = model(input_ids) # aten::linear -> fp8 path
|
|
loss.backward() # fp8 backward; state was captured at forward time
|
|
|
|
Args:
|
|
enabled: toggle fp8 dispatch for aten::linear.
|
|
update_interval: legacy alias for the delayed-scaling history window
|
|
(used only when ``recipe`` is not given).
|
|
recipe: scaling policy; defaults to ``DelayedScaling(update_interval)``.
|
|
fp8_format: ``"e4m3"`` / ``"e5m2"`` / ``"hybrid"`` (default) — hybrid
|
|
means E4M3 forward, E5M2 backward.
|
|
margin: scale headroom (``scale = (amax / FP8_MAX) / 2^margin``) used
|
|
with the default delayed recipe.
|
|
"""
|
|
state = fp8_state()
|
|
prev = (state.enabled, state.recipe, state.fp8_format)
|
|
if recipe is None:
|
|
recipe = DelayedScaling(history_len=update_interval, margin=margin)
|
|
state.enabled = enabled
|
|
state.recipe = recipe
|
|
state.fp8_format = FP8Format(fp8_format)
|
|
try:
|
|
yield
|
|
finally:
|
|
state.enabled, state.recipe, state.fp8_format = prev
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Strategy-level forward / backward (called from the aten::linear impl)
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
def _dynamic_scale(t: torch.Tensor, recipe: FP8Recipe, fmt: str) -> torch.Tensor:
|
|
amax = t.abs().amax().to(torch.float32).clamp_min(1e-12)
|
|
return recipe.scale_from_history(amax, fmt)
|
|
|
|
|
|
def fp8_linear_forward(x: torch.Tensor, w: torch.Tensor, bias=None):
|
|
"""Scaled fp8 linear forward (called from the aten::linear impl).
|
|
|
|
Pure FP8 path for both recipes: quantize x/w with the active scales, run
|
|
the pre-quantized GEMM. With delayed scaling the rings finalize inside
|
|
the quantize kernels (amax folded into the window, next step's scale
|
|
published on device); dynamic scaling measures the current amax itself.
|
|
"""
|
|
if bias is None:
|
|
bias = torch.empty(0, device=x.device, dtype=x.dtype)
|
|
state = fp8_state()
|
|
fmt = state.fp8_format.fwd()
|
|
if isinstance(state.recipe, DynamicScaling):
|
|
meta = None
|
|
sx = _dynamic_scale(x.reshape(-1, w.size(1)), state.recipe, fmt)
|
|
sw = _dynamic_scale(w, state.recipe, fmt)
|
|
out, amax_x, amax_w = linear_forward_fp8(x, w, bias, sx, sw, fmt)
|
|
else:
|
|
meta = state.get_weight_meta(w)
|
|
if not meta.w.initialized:
|
|
meta.w.seed(w, fmt)
|
|
if not meta.x.initialized:
|
|
meta.x.seed(x, fmt)
|
|
# In-kernel ring finalization: the kernels write hist[idx] and the
|
|
# next scale; idx rotates host-side (the device counter self-rearms).
|
|
w_is_fp8 = w.dtype != torch.bfloat16
|
|
out, amax_x, amax_w = linear_forward_fp8(
|
|
x,
|
|
w,
|
|
bias,
|
|
meta.x.scale,
|
|
meta.w.scale,
|
|
fmt,
|
|
None,
|
|
meta.x.state,
|
|
meta.x.idx,
|
|
state.recipe.margin,
|
|
None if w_is_fp8 else meta.w.state,
|
|
meta.w.idx,
|
|
state.recipe.margin,
|
|
)
|
|
meta.x.advance()
|
|
if not w_is_fp8:
|
|
meta.w.advance()
|
|
return out
|
|
|
|
|
|
class _LinearFp8(torch.autograd.Function):
|
|
"""The fp8 linear forward/backward pair (standard Function style).
|
|
|
|
The forward runs inside the ``fp8_autocast`` region and captures the
|
|
active fmt/recipe/meta on ``ctx``; the backward reads only the captured
|
|
state, so ``loss.backward()`` may run after the context exits. The
|
|
gradient is quantized once (E5M2 in hybrid mode) and the dX / dW GEMMs
|
|
share that quantization; output masks come from ``needs_input_grad``.
|
|
"""
|
|
|
|
@staticmethod
|
|
def forward(ctx, x, w, bias):
|
|
out = fp8_linear_forward(x, w, bias)
|
|
state = fp8_state()
|
|
ctx.save_for_backward(x, w)
|
|
ctx.fmt_bwd = state.fp8_format.bwd()
|
|
ctx.recipe = state.recipe
|
|
ctx.is_dynamic = isinstance(state.recipe, DynamicScaling)
|
|
ctx.meta = None if ctx.is_dynamic else state.get_weight_meta(w)
|
|
return out
|
|
|
|
@staticmethod
|
|
@torch.autograd.function.once_differentiable
|
|
def backward(ctx, g):
|
|
x, w = ctx.saved_tensors
|
|
fmt = ctx.fmt_bwd
|
|
if ctx.is_dynamic:
|
|
sg = _dynamic_scale(g, ctx.recipe, fmt)
|
|
sw = _dynamic_scale(w, ctx.recipe, fmt)
|
|
sx = _dynamic_scale(x, ctx.recipe, fmt)
|
|
grad_x, grad_w, grad_b, amax_g = linear_backward_fp8(
|
|
g, x, w, list(ctx.needs_input_grad), sg, sw, sx, fmt
|
|
)
|
|
else:
|
|
meta = ctx.meta
|
|
if not meta.g.initialized:
|
|
meta.g.seed(g, fmt)
|
|
# The g quantize kernel finalizes the gradient's ring in-kernel.
|
|
grad_x, grad_w, grad_b, amax_g = linear_backward_fp8(
|
|
g,
|
|
x,
|
|
w,
|
|
list(ctx.needs_input_grad),
|
|
meta.g.scale,
|
|
meta.w.scale,
|
|
meta.x.scale,
|
|
fmt,
|
|
meta.g.state,
|
|
meta.g.idx,
|
|
ctx.recipe.margin,
|
|
)
|
|
meta.g.advance()
|
|
return grad_x, grad_w, grad_b if ctx.needs_input_grad[2] else None
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# aten::linear integration
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
def fp8_linear_enable(enabled: bool = True) -> None:
|
|
"""Toggle fp8 dispatch for aten::linear (global; backward runs on engine
|
|
worker threads, so a thread-local flag would be lost during backward)."""
|
|
fp8_state().enabled = enabled
|
|
|
|
|
|
def fp8_linear_enabled() -> bool:
|
|
return fp8_state().enabled
|
|
|
|
|
|
def _fp8_supported(x: torch.Tensor, w: torch.Tensor) -> bool:
|
|
"""Shape guard for the fp8 linear path.
|
|
|
|
Unlike a strict 16-alignment requirement, the fp8 kernels handle unaligned
|
|
M/N via boundary checks (slower but correct) — so no whole-call bf16
|
|
fallback for small decode batches. Only the K-dimension contraction must
|
|
match, and the weight must be 2D.
|
|
"""
|
|
return x.dim() >= 2 and w.dim() == 2 and x.size(-1) == w.size(1)
|
|
|
|
|
|
def _linear_cuda_impl(x: torch.Tensor, w: torch.Tensor, bias=None):
|
|
if (
|
|
fp8_linear_enabled()
|
|
and x.dtype == torch.bfloat16
|
|
and w.dtype == torch.bfloat16
|
|
and _fp8_supported(x, w)
|
|
):
|
|
return _LinearFp8.apply(x, w, bias)
|
|
return torch.ops.aten.linear.default.redispatch(
|
|
torch._C.DispatchKeySet(torch._C.DispatchKey.CompositeImplicitAutograd),
|
|
x,
|
|
w,
|
|
bias,
|
|
)
|
|
|
|
|
|
_lib = Library("aten", "IMPL", "CUDA")
|
|
_lib.impl("linear", _linear_cuda_impl)
|
|
# Also replace torch's generated linear autograd formula (which would call
|
|
# aten::linear_backward after the fp8_autocast region exits). The fp8
|
|
# backward is owned by _LinearFp8 with its state captured at forward time,
|
|
# so loss.backward() works wherever it is called; the same CUDA registration
|
|
# still covers inference_mode, where autograd keys are skipped entirely.
|
|
_lib_autograd = Library("aten", "IMPL", "AutogradCUDA")
|
|
_lib_autograd.impl("linear", _linear_cuda_impl)
|