File size: 9,271 Bytes
e1acc15 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 | """Official forward values; STE QDQ and recomputed frozen-weight input gradients.
No decoded routed matrix survives a call. Packed expert bytes alone are saved
for backward. BF16 decoding is exact for the pinned 1017 tensors (N1 audit).
FP64 accumulation is an oracle option, FP32 is the training path; TF32 is off.
"""
from __future__ import annotations
from typing import Any, Callable, cast
from contextlib import contextmanager
from collections.abc import Iterator
import threading
import torch
from torch import Tensor, nn
from torch.nn import functional as F
from ...native.formats import act_qdq, decode_fp4
_STOP: threading.Event | None = None
class TrainingCancelled(Exception):
"""Discard the current, uncommitted optimizer step and checkpoint its boundary."""
@contextmanager
def cancellation(stop: threading.Event) -> Iterator[None]:
global _STOP
old, _STOP = _STOP, stop
try:
yield
finally:
_STOP = old
def check_cancelled() -> None:
if _STOP is not None and _STOP.is_set():
raise TrainingCancelled()
class _QDQ(torch.autograd.Function):
@staticmethod
def forward(ctx: Any, x: Tensor) -> Tensor:
return act_qdq(x)
@staticmethod
def backward(ctx: Any, grad: Tensor) -> Tensor:
return grad
def qdq(x: Tensor, fast: bool = False) -> Tensor:
if fast:
from .fast_gpu import fast_qdq
return fast_qdq(x)
return cast(Callable[[Tensor], Tensor], _QDQ.apply)(x)
class _FrozenLinear(torch.autograd.Function):
@staticmethod
def forward(ctx: Any, x: Tensor, w: Tensor, scale: Tensor | None, acc: torch.dtype) -> Tensor:
check_cancelled()
ctx.save_for_backward(w, *(() if scale is None else (scale,)))
ctx.acc, ctx.input_dtype, ctx.input_device = acc, x.dtype, x.device
if scale is not None and w.is_cuda:
from .packed_forward import packed_matmul
result = packed_matmul(x.to(w.device), w, scale)
return result.to(device=x.device, dtype=x.dtype)
decoded = w if scale is None else decode_fp4(w, scale).to(torch.bfloat16)
return F.linear(x.to(device=w.device, dtype=acc), decoded.to(acc)).to(device=x.device, dtype=x.dtype)
@staticmethod
def backward(ctx: Any, grad: Tensor) -> tuple[Tensor, None, None, None]:
check_cancelled()
saved = ctx.saved_tensors
decoded = saved[0] if len(saved) == 1 else decode_fp4(*saved).to(torch.bfloat16)
# dX uses FP32 accumulation on CUDA; this changes no forward value.
gradient_acc = torch.float32 if decoded.is_cuda else ctx.acc
dx = (grad.to(device=decoded.device, dtype=gradient_acc) @ decoded.to(gradient_acc)).to(device=ctx.input_device, dtype=ctx.input_dtype)
return dx, None, None, None
def frozen_linear(x: Tensor, w: Tensor, scale: Tensor | None = None,
acc: torch.dtype = torch.float32) -> Tensor:
return cast(Callable[[Tensor, Tensor, Tensor | None, torch.dtype], Tensor], _FrozenLinear.apply)(x, w, scale, acc)
def head_product(a: Tensor, b: Tensor) -> Tensor:
"""FP32 head output from a two-part BF16 activation and exact BF16 weights.
No QDQ follows the vocabulary head, so its small input decomposition error
cannot cross any internal quantizer. Whole-model KL/loss/gradient gates
explicitly include this arithmetic. CPU is the FP32 oracle for tests.
"""
if not a.is_cuda or b.dtype != torch.bfloat16:
return a.float() @ b.float()
high = a.bfloat16()
low = (a.float() - high.float()).bfloat16()
return torch.mm(high, b, out_dtype=torch.float32) + torch.mm(low, b, out_dtype=torch.float32)
class _HeadLinear(torch.autograd.Function):
@staticmethod
def forward(ctx: Any, x: Tensor, w: Tensor) -> Tensor:
ctx.save_for_backward(w)
return head_product(x.reshape(-1, x.shape[-1]), w.T).reshape(*x.shape[:-1], w.shape[0])
@staticmethod
def backward(ctx: Any, grad: Tensor) -> tuple[Tensor, None]:
(w,) = ctx.saved_tensors
return head_product(grad.reshape(-1, grad.shape[-1]), w).reshape(*grad.shape[:-1], w.shape[1]), None
def fast_head(x: Tensor, w: Tensor) -> Tensor:
return cast(Callable[[Tensor, Tensor], Tensor], _HeadLinear.apply)(x, w)
class _GroupedLinear(torch.autograd.Function):
@staticmethod
def forward(ctx: Any, x: Tensor, w: Tensor) -> Tensor:
ctx.save_for_backward(w)
expanded = w.float()
if x.device.type == "cpu" and x.shape[0] > 1:
return torch.cat([torch.einsum("bsgd,grd->bsgr", x[b:b+1], expanded) for b in range(x.shape[0])], 0)
return torch.einsum("bsgd,grd->bsgr", x, expanded)
@staticmethod
def backward(ctx: Any, grad: Tensor) -> tuple[Tensor, None]:
(w,) = ctx.saved_tensors
return torch.einsum("bsgr,grd->bsgd", grad, w.float()), None
def grouped_linear(x: Tensor, w: Tensor) -> Tensor:
# model.py's unquantized wo_a is specifically an FP32 grouped einsum,
# including in the FP64 reference. Widening that sum changes QDQ later.
return cast(Callable[[Tensor, Tensor], Tensor], _GroupedLinear.apply)(x, w)
class Dense(nn.Module):
"""Frozen decoded BF16 master, optional r32/64 LoRA, official input QDQ.
LoRA is added before the linear output rounding. This is a floating adapter;
the merged FP8 path is deliberately a separate packaging/evaluation gate.
"""
weight: Tensor
def __init__(self, weight: Tensor, rank: int = 0, quant_input: bool = True,
acc: torch.dtype = torch.float32):
super().__init__()
self.register_buffer("weight", weight.to(torch.bfloat16))
self.quant_input, self.acc = quant_input, acc
self.fast_training = False
self.rank = rank
if rank:
if rank not in (32, 64):
raise ValueError("Phase 2a supports only LoRA r32 or r64")
self.a = nn.Parameter(torch.empty(rank, weight.shape[1], device=weight.device, dtype=torch.float32))
self.b = nn.Parameter(torch.zeros(weight.shape[0], rank, device=weight.device, dtype=torch.float32))
nn.init.kaiming_uniform_(self.a, a=5 ** .5)
def forward(self, x: Tensor) -> Tensor:
x = qdq(x, self.fast_training) if self.quant_input else x
if self.fast_training:
from .fast_gpu import fast_linear
base = fast_linear(x, self.weight, self.quant_input)
else:
base = frozen_linear(x, self.weight, acc=self.acc)
if self.rank:
base = base + F.linear(F.linear(x.to(device=self.a.device, dtype=torch.float32), self.a), self.b).to(x.device)
return base.to(x.dtype)
def merged(self) -> Tensor:
w = self.weight.float()
return w + self.b @ self.a if self.rank else w
class PackedLinear(nn.Module):
weight: Tensor
scale: Tensor
def __init__(self, weight: Tensor, scale: Tensor, acc: torch.dtype):
super().__init__()
self.register_buffer("weight", weight)
self.register_buffer("scale", scale)
self.acc = acc
def forward(self, x: Tensor) -> Tensor:
return frozen_linear(qdq(x), self.weight, self.scale, self.acc)
def rms(x: Tensor, weight: Tensor, eps: float) -> Tensor:
z = x.float()
z = z * torch.rsqrt(z.square().mean(-1, keepdim=True) + eps)
return (weight * z).to(x.dtype)
def rope(x: Tensor, positions: Tensor, rd: int, theta: float, inverse: bool = False) -> Tensor:
# Pure sliding-window layers explicitly disable YaRN in official model.py.
freq = 1 / theta ** (torch.arange(0, rd, 2, device=x.device, dtype=torch.float32) / rd)
angles = positions.float().unsqueeze(-1) * freq
phase = torch.polar(torch.ones_like(angles), -angles if inverse else angles)
if x.ndim == 4:
phase = phase.unsqueeze(-2)
tail = torch.view_as_complex(x[..., -rd:].float().reshape(*x.shape[:-1], rd//2, 2).contiguous())
rotated = torch.view_as_real(tail * phase).flatten(-2).to(x.dtype)
return torch.cat((x[..., :-rd], rotated), -1)
def mixes(x: Tensor, fn: Tensor, scale: Tensor, base: Tensor, hc: int, iters: int,
norm_eps: float, eps: float, acc: torch.dtype) -> tuple[Tensor, Tensor, Tensor]:
z = x.flatten(-2).float()
z = F.linear(z, fn) * torch.rsqrt(z.square().mean(-1, keepdim=True) + norm_eps)
z, scale, base = z.to(acc), scale.to(acc), base.to(acc)
pre = torch.sigmoid(z[..., :hc] * scale[0] + base[:hc]) + eps
post = 2 * torch.sigmoid(z[..., hc:2*hc] * scale[1] + base[hc:2*hc])
comb = (z[..., 2*hc:] * scale[2] + base[2*hc:]).reshape(*z.shape[:-1], hc, hc)
comb = comb.softmax(-1) + eps
comb = comb / (comb.sum(-2, keepdim=True) + eps)
for _ in range(iters - 1):
comb = comb / (comb.sum(-1, keepdim=True) + eps)
comb = comb / (comb.sum(-2, keepdim=True) + eps)
return pre.float(), post.float(), comb.float()
def collapse(x: Tensor, pre: Tensor) -> Tensor:
return (pre.unsqueeze(-1) * x.float()).sum(-2).to(x.dtype)
def expand(x: Tensor, residual: Tensor, post: Tensor, comb: Tensor) -> Tensor:
return (post.unsqueeze(-1) * x.unsqueeze(-2)
+ (comb.unsqueeze(-1) * residual.unsqueeze(-2)).sum(-3)).to(x.dtype)
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