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aff03ff 16d760d aff03ff | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 | """ZeRO-2 sharding of the backbone's fp32 master weights, AdamW moments and gradients
across data-parallel ranks (SPEC.md §12.9).
The trainer keeps bf16 compute weights on every rank (the functional backbone calls its
layers' weights directly, so they must be whole on the device) and, without sharding, a
full fp32 master copy + two AdamW moments + an fp32 gradient: 16 bytes per trainable
backbone parameter, ~22 GB for Qwen3.5-2B, more than an L4 holds. :class:`ZeroMaster`
splits those 16 bytes over ``world`` ranks:
* The trainable parameters are laid end to end in a flat vector of ``N`` elements cut
into buckets (``bucket`` elements, the last one padded to a multiple of ``world``).
Rank ``r`` owns the ``r``-th of ``world`` equal pieces of every bucket; its shard is
those pieces concatenated (``shard_len`` ~ ``N / world``).
* :attr:`master` is ONE fp32 parameter (the shard) that AdamW updates; the optimizer's
moments are shard-sized. Element-wise AdamW over a flat tensor is the same arithmetic
as over the separate tensors (same decay everywhere, as the unsharded trainer).
* A step's bf16 gradients stay on the parameters' ``.grad`` until the step's last part
(:meth:`collect`): a one-part step keeps them where autograd put them (no copy); a
several-part step accumulates them in a host fp32 vector (no second device copy).
:meth:`reduce_scatter` then sums every rank's gradient into the owners' fp32 shard
(bucket by bucket, fp32), so :attr:`master` ``.grad`` holds the shard of the sum.
* After ``opt.step()``, :meth:`gather_params` rounds each rank's updated shard to bf16
and all-gathers it into every rank's compute weights: bitwise the unsharded trainer's
``p.copy_(master)``.
``sim_world`` (smoke tests on one GPU): shard as rank 0 of a ``sim_world``-rank job with
no collectives, so memory and step time are the real per-rank ones; the other ranks'
pieces are simply not updated (their weights stay as loaded). Never for real training.
"""
from __future__ import annotations
import bisect
import math
from typing import Iterator
import torch
from torch import Tensor, nn
BUCKET = 1 << 25 # 32M elements: 128 MB fp32 per collective
def _dist():
import torch.distributed as dist
return dist
class ZeroMaster:
def __init__(self, params: list[nn.Parameter], rank: int, world: int,
bucket: int = BUCKET, sim_world: int = 0) -> None:
if not params:
raise ValueError("ZeroMaster needs trainable parameters")
self.params = params
self.sim = sim_world > 1
self.world = sim_world if self.sim else world
self.rank = 0 if self.sim else rank
self.comm = world > 1 and not self.sim
self.numel = [p.numel() for p in params]
self.offsets = [0]
for n in self.numel:
self.offsets.append(self.offsets[-1] + n)
self.N = self.offsets[-1]
w = self.world
bucket = max(w, bucket - bucket % w)
# (start, end, padded length) of every bucket in the flat vector
self.buckets: list[tuple[int, int, int]] = []
for s in range(0, self.N, bucket):
e = min(self.N, s + bucket)
self.buckets.append((s, e, math.ceil((e - s) / w) * w))
self.shard_len = sum(L // w for _, _, L in self.buckets)
dev = params[0].device
self.device = dev
shard = torch.empty(self.shard_len, dtype=torch.float32, device=dev)
o = 0
for b, (s, e, L) in enumerate(self.buckets):
piece = L // w
shard[o:o + piece] = self._bucket_values(b)[self.rank * piece:(self.rank + 1) * piece]
o += piece
self.master = nn.Parameter(shard)
self.grad_buf = torch.zeros_like(shard) # resident: the shard's fp32 gradient
self.held: list[Tensor | None] | None = None # one-part step: the bf16 grads
self.host: Tensor | None = None # several-part step: fp32 sum on the host
self._host_buf: Tensor | None = None # kept once allocated (pinned)
self.spills = 0
# ---------------------------------------------------------------- flat layout
def _segments(self, s: int, e: int) -> Iterator[tuple[int, int, int, int]]:
"""(param index, offset in param, offset in [s, e), length) covering [s, e)."""
i = bisect.bisect_right(self.offsets, s) - 1
while i < len(self.numel) and self.offsets[i] < e:
a = max(s, self.offsets[i])
b = min(e, self.offsets[i + 1])
yield i, a - self.offsets[i], a - s, b - a
i += 1
def _bucket_values(self, b: int) -> Tensor:
s, e, L = self.buckets[b]
out = torch.zeros(L, dtype=torch.float32, device=self.device)
for i, po, bo, n in self._segments(s, e):
out[bo:bo + n] = self.params[i].detach().reshape(-1)[po:po + n].float()
return out
def _pieces(self) -> Iterator[tuple[int, int, int, int, int]]:
"""(bucket start, end, padded length, piece length, shard offset)."""
o = 0
for s, e, L in self.buckets:
piece = L // self.world
yield s, e, L, piece, o
o += piece
# ---------------------------------------------------------------- gradients
@property
def has_grad(self) -> bool:
return self.held is not None or self.host is not None
def collect(self, more: bool) -> None:
"""Take the parameters' current ``.grad`` (one part's bf16 gradient) into the
step's sum and clear them. ``more``: further parts of this step will run, so the
sum moves to the host (the next part's backward then has the device memory)."""
grads = [p.grad for p in self.params]
for p in self.params:
p.grad = None
if all(g is None for g in grads):
return
if self.held is None and self.host is None and not more:
self.held = grads
return
if self.host is None:
if self._host_buf is None:
self._host_buf = torch.zeros(self.N, dtype=torch.float32,
pin_memory=self.device.type == "cuda")
else:
self._host_buf.zero_()
self.host = self._host_buf
self.spills += 1
if self.held is not None:
self._to_host(self.held)
self.held = None
self._to_host(grads)
def _to_host(self, grads: list[Tensor | None]) -> None:
assert self.host is not None
for i, g in enumerate(grads):
if g is not None:
o = self.offsets[i]
self.host[o:o + self.numel[i]] += g.detach().reshape(-1).float().cpu()
def discard(self) -> None:
self.held = None
self.host = None
for p in self.params:
p.grad = None
self.master.grad = None
def reduce_scatter(self) -> None:
"""Sum the step's gradient over ranks into :attr:`master` ``.grad`` (this rank's
shard, fp32). A rank without gradient contributes zeros."""
held, host = self.held, self.host
self.held = self.host = None
for s, e, L, piece, o in self._pieces():
buf = torch.zeros(L, dtype=torch.float32, device=self.device)
if host is not None:
buf[:e - s] = host[s:e].to(self.device, non_blocking=True)
elif held is not None:
for i, po, bo, n in self._segments(s, e):
g = held[i]
if g is not None:
buf[bo:bo + n] = g.reshape(-1)[po:po + n].float()
out = self.grad_buf[o:o + piece]
if self.comm:
_reduce_scatter(out, buf)
else:
out.copy_(buf[self.rank * piece:(self.rank + 1) * piece])
del buf
del held, host
self.master.grad = self.grad_buf
def grad_sq_norm(self) -> Tensor:
"""Squared L2 norm of the whole (summed) gradient, identical on every rank."""
sq = self.grad_buf.pow(2).sum()
if self.comm:
_dist().all_reduce(sq)
return sq
# ---------------------------------------------------------------- weights
@torch.no_grad()
def gather_params(self) -> None:
"""Every rank's updated shard -> bf16 -> the compute weights of every rank."""
dtype = self.params[0].dtype
for s, e, L, piece, o in self._pieces():
mine = self.master[o:o + piece].to(dtype)
if self.comm:
full = torch.empty(L, dtype=dtype, device=self.device)
_all_gather(full, mine)
lo, hi = 0, e - s
else: # one process: only this rank's piece is known
full = torch.zeros(L, dtype=dtype, device=self.device)
full[self.rank * piece:(self.rank + 1) * piece] = mine
lo = self.rank * piece
hi = min(e - s, lo + piece)
for i, po, bo, n in self._segments(s, e):
a, b = max(bo, lo), min(bo + n, hi)
if a < b:
self.params[i].view(-1)[po + a - bo:po + b - bo] = full[a:b]
@torch.no_grad()
def full_master(self) -> Tensor:
"""The whole fp32 master vector (all ranks; tests)."""
parts = []
for s, e, L, piece, o in self._pieces():
mine = self.master[o:o + piece].detach()
if self.comm:
full = torch.empty(L, dtype=mine.dtype, device=self.device)
_all_gather(full, mine.contiguous())
else:
full = mine
parts.append(full[:e - s])
return torch.cat(parts)
def _backend() -> str:
return str(_dist().get_backend())
def _reduce_scatter(out: Tensor, inp: Tensor) -> None:
dist = _dist()
if _backend() == "nccl":
dist.reduce_scatter_tensor(out, inp)
else: # gloo (CPU tests): all-reduce, keep this rank's piece
dist.all_reduce(inp)
r, n = dist.get_rank(), out.numel()
out.copy_(inp[r * n:(r + 1) * n])
def _all_gather(out: Tensor, inp: Tensor) -> None:
dist = _dist()
if _backend() == "nccl":
dist.all_gather_into_tensor(out, inp)
else:
dist.all_gather(list(out.chunk(dist.get_world_size())), inp)
def sharded_static_bytes(trainable_backbone: int, heads: int, weights: int, world: int
) -> float:
""":func:`deg.memplan.static_bytes` with ZeRO-2: bf16 weights + bf16 grads whole,
fp32 master + AdamW moments + fp32 gradient of the backbone per shard, heads whole."""
return (weights + 2.0 * trainable_backbone + 16.0 * trainable_backbone / world
+ 16.0 * heads)
def broadcast_module(module: nn.Module, src: int = 0) -> None:
"""Every parameter and buffer of ``module`` from rank ``src`` (fresh heads are drawn
from per-rank seeds; replicas must start identical)."""
dist = _dist()
with torch.no_grad():
for t in [*module.parameters(), *module.buffers()]:
dist.broadcast(t.data, src)
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