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TP convention: column/output sharding slices B rows; row/input sharding slices
A columns, with partials reduced by the existing worker. Install once BEFORE
capture. Every trained norm/Markov/confidence tensor is consumed or we refuse.
"""
from __future__ import annotations
from dataclasses import asdict
import hashlib
import json
from pathlib import Path
from typing import Callable, Mapping, Protocol, cast
import torch
from torch import Tensor
from torch.nn import functional as F
from ...native.arms import ArmConfig
from .cache_guard import DependencyGuard
from .model import Config
class NativeLinear(Protocol):
in_features: int
out_features: int
def __call__(self, x: Tensor, out_f32: bool = False, out: Tensor | None = None) -> Tensor: ...
def nbytes(self) -> int: ...
class DeltaLinear:
def __init__(self, base: NativeLinear, a: Tensor, b: Tensor):
if a.ndim != 2 or b.shape != (base.out_features, a.shape[0]) or a.shape[1] != base.in_features:
raise ValueError("adapter shard shape differs from native base")
self.base, self.a, self.b = base, a.detach().float().contiguous(), b.detach().float().contiguous()
self.in_features, self.out_features = base.in_features, base.out_features
def __call__(self, x: Tensor, out_f32: bool = False, out: Tensor | None = None) -> Tensor:
# x has already undergone the model's input QDQ. Add BEFORE output
# rounding and, for row-parallel projections, before the all-reduce.
value = self.base(x, out_f32=True).float()+F.linear(F.linear(x.float(), self.a), self.b)
value = value if out_f32 else value.to(x.dtype)
if out is not None:
out.copy_(value)
return out
return value
def nbytes(self) -> int:
return self.base.nbytes()+4*(self.a.numel()+self.b.numel())
class GroupedDelta:
def __init__(self, weight: Tensor, factors: list[tuple[Tensor, Tensor]]):
if weight.ndim != 3 or len(factors) != weight.shape[0]:
raise ValueError("grouped adapter ownership mismatch")
self.weight, self.factors = weight, factors
if any(a.ndim != 2 or a.shape[1] != weight.shape[2] or b.shape != (weight.shape[1], a.shape[0]) for a, b in factors):
raise ValueError("grouped adapter factor shape mismatch")
def __call__(self, x: Tensor) -> Tensor:
grouped = x.reshape(x.shape[0], len(self.factors), -1).float()
base = torch.einsum("tgd,god->tgo", grouped, self.weight.float())
delta = torch.stack([F.linear(F.linear(grouped[:, g], a), b) for g, (a, b) in enumerate(self.factors)], 1)
return (base+delta).reshape(x.shape[0], -1).to(x.dtype)
class Stage(Protocol):
wq_a: NativeLinear
wq_b: NativeLinear
wkv: NativeLinear
wo_b: NativeLinear
sh_w1: NativeLinear
sh_w2: NativeLinear
sh_w3: NativeLinear
wo_a_n: object
adapter_wo_a: Callable[[Tensor], Tensor] | None
shared_pair: object
attn_norm: Tensor
ffn_norm: Tensor
q_norm: Tensor
kv_norm: Tensor
class Markov(Protocol):
w1: Tensor
w2: Tensor
cache: Tensor
slot_of_token: Tensor
class Rank(Protocol):
g0: int
g1: int
h0: int
h1: int
i0: int
i1: int
v0: int
v1: int
dev: torch.device
stages: list[Stage]
main_norm: Tensor
norm: Tensor
conf_w: Tensor
markov: Markov
mrows: object
ring_kv: Tensor
ring_pos: Tensor
class Worker(Protocol):
cfg: ArmConfig
ALL: tuple[int, ...]
ranks: list[Rank]
graphs: dict[object, object]
margin_graphs: dict[object, object]
insert_graphs: dict[object, object]
adapter_guard: DependencyGuard
CacheFactory = Callable[[Tensor, Tensor, list[int], int], object]
def install(model: Worker, values: Mapping[str, Tensor], config: Config, rank: int,
cache_factory: CacheFactory, hot_tokens: list[int]) -> None:
if (model.cfg.dense != "bf16" or model.cfg.head != "bf16" or model.cfg.combine != "official"
or not model.cfg.wo_a_qdq or model.cfg.kv_qdq != "official" or model.cfg.dense_kernel != "n1"):
raise ValueError("adapter evaluation requires native BF16 dense/head and official QDQ/combine")
if model.graphs or model.margin_graphs or model.insert_graphs or hasattr(model, "adapter_guard"):
raise ValueError("install a single immutable adapter before graph capture; recreate worker")
expected: dict[str, tuple[int, ...]] = {}
projections = {"attn.wq_a": (config.qr, config.dim), "attn.wq_b": (config.heads*config.hd, config.qr),
"attn.wkv": (config.hd, config.dim), "attn.wo_b": (config.dim, config.groups*config.outr),
"ffn.shared_experts.w1": (config.inter, config.dim), "ffn.shared_experts.w3": (config.inter, config.dim),
"ffn.shared_experts.w2": (config.dim, config.inter)}
def tensor_key(name: str) -> str:
return name.replace(".", "__")
expected["t.mtp__0__main_norm__weight"] = (config.dim,)
for name, shape in {"norm.weight": (config.dim,), "markov_head.embed.weight": (config.vocab, config.markov),
"markov_head.head.weight": (config.vocab, config.markov),
"confidence_head.proj.weight": (1, config.dim+config.markov)}.items():
expected["t."+tensor_key(f"mtp.{config.stages-1}."+name)] = shape
for i in range(config.stages):
for name, (out, width) in projections.items():
stem = f"stages.{i}.dense.{tensor_key(name)}."
expected[stem+"a"], expected[stem+"b"] = (rank, width), (out, rank)
for group in range(config.groups):
stem = f"stages.{i}.wo_a.{group}."
expected[stem+"a"] = (rank, config.heads//config.groups*config.hd)
expected[stem+"b"] = (config.outr, rank)
for name, width in (("attn_norm", config.dim), ("ffn_norm", config.dim), ("attn.q_norm", config.qr), ("attn.kv_norm", config.hd)):
expected[f"stages.{i}.t."+tensor_key(f"mtp.{i}.{name}.weight")] = (width,)
if set(expected) != set(values) or rank not in (32, 64):
raise ValueError("adapter trainable coverage/rank mismatch; no ignored tensors")
for name, shape in expected.items():
if values[name].shape != shape or not bool(torch.isfinite(values[name]).all()):
raise ValueError(f"adapter shape/nonfinite mismatch: {name}")
# Build all replacements before mutation. LoRA never changes any base bytes.
changes: list[tuple[object, str, object]] = []
dependencies: dict[str, Tensor] = {}
for r in model.ALL:
rk = model.ranks[r]
def on_device(name: str) -> Tensor:
tensor = values[name].detach().to(device=rk.dev, dtype=torch.float32).clone().contiguous()
dependencies[f"{r}/{name}"] = tensor
return tensor
def replace(owner: object, attribute: str, name: str, selection: slice | None = None, flatten: bool = False) -> None:
t = on_device(name)
if selection is not None:
t = t[selection].contiguous()
if flatten:
t = t.flatten()
changes.append((owner, attribute, t))
replace(rk, "main_norm", "t.mtp__0__main_norm__weight")
last = "t."+tensor_key(f"mtp.{config.stages-1}.")
replace(rk, "norm", last+"norm__weight")
replace(rk, "conf_w", last+"confidence_head__proj__weight", flatten=True)
w1 = on_device(last+"markov_head__embed__weight")
w2 = on_device(last+"markov_head__head__weight")[rk.v0:rk.v1].contiguous()
# Rebuild exact adapted Markov hot rows; discard all original BF16 rows.
changes.extend(((rk.markov, "w1", w1), (rk.markov, "w2", w2),
(rk, "mrows", cache_factory(w1, w2, hot_tokens, model.cfg.markov_rows))))
for i, stage in enumerate(rk.stages):
for op, attribute in (("attn.wq_a", "wq_a"), ("attn.wq_b", "wq_b"), ("attn.wkv", "wkv"),
("attn.wo_b", "wo_b"), ("ffn.shared_experts.w1", "sh_w1"),
("ffn.shared_experts.w2", "sh_w2"), ("ffn.shared_experts.w3", "sh_w3")):
stem = f"stages.{i}.dense.{tensor_key(op)}."
a, b = on_device(stem+"a"), on_device(stem+"b")
if op == "attn.wq_b":
b = b[rk.h0*config.hd:rk.h1*config.hd]
elif op == "attn.wo_b":
a = a[:, rk.g0*config.outr:rk.g1*config.outr]
elif op in ("ffn.shared_experts.w1", "ffn.shared_experts.w3"):
b = b[rk.i0:rk.i1]
elif op == "ffn.shared_experts.w2":
a = a[:, rk.i0:rk.i1]
changes.append((stage, attribute, DeltaLinear(cast(NativeLinear, getattr(stage, attribute)), a, b)))
# Native BF16 wo_a is grouped (not a dense matrix over all groups).
weight = cast(Tensor, getattr(stage.wo_a_n, "w"))
factors = [(on_device(f"stages.{i}.wo_a.{g}.a"), on_device(f"stages.{i}.wo_a.{g}.b")) for g in range(rk.g0, rk.g1)]
changes.append((stage, "adapter_wo_a", GroupedDelta(weight.reshape(rk.g1-rk.g0, config.outr, -1), factors)))
changes.append((stage, "shared_pair", None)) # prevent a fused base-only bypass
for op, attribute in (("attn_norm", "attn_norm"), ("ffn_norm", "ffn_norm"), ("attn.q_norm", "q_norm"), ("attn.kv_norm", "kv_norm")):
replace(stage, attribute, f"stages.{i}.t."+tensor_key(f"mtp.{i}.{op}.weight"))
with torch.no_grad():
for owner, attribute, value in changes:
setattr(owner, attribute, value)
# Resolve installed bindings on each pre-replay check, detecting both
# in-place updates and replacement of a tensor/factor object.
def current_dependencies() -> dict[str, Tensor]:
current = dict(dependencies)
for i, (owner, attribute, _) in enumerate(changes):
value = getattr(owner, attribute)
if isinstance(value, Tensor):
current[f"installed/{i}"] = value
elif isinstance(value, DeltaLinear):
current[f"delta/{i}/a"], current[f"delta/{i}/b"] = value.a, value.b
elif isinstance(value, GroupedDelta):
for g, (a, b) in enumerate(value.factors):
current[f"group/{i}/{g}/a"], current[f"group/{i}/{g}/b"] = a, b
return current
def invalidate() -> None:
model.graphs.clear()
model.margin_graphs.clear()
model.insert_graphs.clear()
for r in model.ALL:
rk = model.ranks[r]
rk.ring_kv.zero_()
rk.ring_pos.fill_(-1)
rk.markov.cache.zero_()
rk.markov.slot_of_token.fill_(-1)
rk.mrows = None
# All feature windows are invalidated before the first adapted insertion.
for r in model.ALL:
model.ranks[r].ring_kv.zero_()
model.ranks[r].ring_pos.fill_(-1)
model.ranks[r].markov.cache.zero_()
model.ranks[r].markov.slot_of_token.fill_(-1)
model.adapter_guard = DependencyGuard(current_dependencies(), invalidate, current_dependencies)
def adapted_identity(base: Mapping[str, object], checkpoint_sha256: str, config: Config) -> dict[str, object]:
from .pilot import sources
from .data import sha256
runtime_sources = sources()
proxy_root = Path(__file__).resolve().parents[2]/"spark/remote_dspark"
runtime_sources.update({"process_proxy/"+p.name: sha256(p) for p in sorted(proxy_root.iterdir())
if p.is_file() and p.suffix in (".py", ".c")})
def identity(role: str) -> str:
return hashlib.sha256(json.dumps({"schema": "dsv41.d2.native-adapter.v1", "role": role, "base": dict(base),
"adapter_sha256": checkpoint_sha256, "config": asdict(config), "runtime_sources": runtime_sources,
"semantics": "native-base-plus-fp32-delta-before-rounding;immutable;official-QDQ"}, sort_keys=True).encode()).hexdigest()
return {"schema": "dsv41.d2.adapted-native-profile.v1", "source_receipt": dict(base),
"native_base_profile": "native-hybrid-head-hot", "arm": "d2-adapted-native", "profile_label": "adapted; not unchanged source-faithful",
"adapter_sha256": checkpoint_sha256, "draft_head": "official BF16 head with FP32 logits", "drafter_identity": identity("drafter"), "markov_identity": identity("markov")}
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