File size: 9,602 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 | """Opt-in V1 diagnostics. Imported/installed only with DSPARK_V1_DUMP_DIR.
Selected graph replies are checked against a traced eager replay on the SAME
state. The reply is never replaced. A graph/eager disagreement fails the round.
Window KV is read from the live ring; main_x is the insert graph's actual output,
not a differently batched reconstruction. This mode deliberately synchronizes and
must be disabled for latency/engine measurements. Dumps contain private data.
"""
from __future__ import annotations
import json
import os
from pathlib import Path
from typing import Any, Callable, cast
import numpy as np
import torch
from . import fused as FU
from .server import DraftCore
from .tp2 import TP2Backend
def chosen_rounds(value: str) -> frozenset[int]:
rounds = frozenset(int(x) for x in value.split(","))
if not rounds or min(rounds) < 1 or len(rounds) > 1024:
raise ValueError("DSPARK_V1_ROUNDS requires 1..1024 positive round numbers")
return rounds
class V1Dump:
def __init__(self, model: Any, ident: dict[str, Any], directory: str, rounds: str = "1"):
self.m = model
self.ident = ident
self.directory = Path(directory).resolve()
source = Path(__file__).resolve().parents[3]
if self.directory == source or source in self.directory.parents:
raise ValueError("V1 dumps must be outside the repository (private capture data)")
self.rounds = chosen_rounds(rounds)
self.directory.mkdir(mode=0o700, parents=True, exist_ok=True)
self.main_x: dict[tuple[int, int], torch.Tensor] = {}
self.sources: dict[tuple[int, int], str] = {}
self.call = 0
self.wire: dict[str, int] = {}
self.samples: list[dict[str, Any]] | None = None
self.files: list[str] = []
self._install_insert_observers()
def _remember(self, slots: torch.Tensor, pos: torch.Tensor, mx: torch.Tensor, source: str) -> None:
slot_values, positions = slots.cpu().tolist(), pos.cpu().tolist()
saved_mx = mx.detach().cpu().clone()
for j, (slot, p) in enumerate(zip(slot_values, positions)):
if slot >= 4: # graph warmup/padding is outside the contract
continue
self.main_x[slot, p] = saved_mx[j]
self.sources[slot, p] = source
old = [(s, q) for s, q in self.main_x if s == slot and q < p - self.m.s.window + 1]
for key in old:
self.main_x.pop(key)
self.sources.pop(key)
def _install_insert_observers(self) -> None:
m = self.m
m._dump_insert_mx = {}
original_mx = cast(Callable[[Any, torch.Tensor], torch.Tensor], m._main_x)
def traced_mx(rk: Any, taps: torch.Tensor) -> torch.Tensor:
y = original_mx(rk, taps)
m._dump_insert_mx[rk.r] = y
return y
m._main_x = traced_mx
original_insert = m.insert_taps_graphed
def insert(slots: torch.Tensor, positions: torch.Tensor, taps: torch.Tensor) -> None:
original_insert(slots, positions, taps)
bucket = min(n for n in m.insert_graphs if n >= slots.numel())
self._remember(slots, positions, m.insert_graphs[bucket]["dump_main_x"][0][:slots.numel()], "taps")
m.insert_taps_graphed = insert
original_eager = m.insert_eager
def eager(slots: torch.Tensor, positions: torch.Tensor, taps: torch.Tensor) -> None:
m._dump_insert_mx.clear()
original_eager(slots, positions, taps)
self._remember(slots, positions, m._dump_insert_mx[0], "taps")
m.insert_eager = eager
original_inject = m.insert_mx_eager
def inject(slots: torch.Tensor, positions: torch.Tensor, mx: torch.Tensor) -> None:
original_inject(slots, positions, mx)
self._remember(slots, positions, mx, "engine_mx")
m.insert_mx_eager = inject
original_reset = m.reset_slots
def reset(slots: list[int]) -> None:
original_reset(slots)
for key in [key for key in self.main_x if key[0] in slots]:
self.main_x.pop(key)
self.sources.pop(key)
m.reset_slots = reset
def emit(self, host: torch.Tensor, N: int, n: int, want_margin: bool,
reply: tuple[np.ndarray, np.ndarray, np.ndarray | None]) -> None:
self.call += 1
if self.call not in self.rounds:
return
if tuple(self.m.ALL) != (0, 1):
raise RuntimeError("V1 dumps require complete TP2; a solo shard is not a reference sample")
trace: dict[str, Any] = {}
ids, conf, margin, peer_ids = self.m.draft(host, N, n_out=n, margin=want_margin, trace=trace)
if not (np.array_equal(ids.cpu().numpy(), reply[0]) and
np.array_equal(conf.cpu().numpy(), reply[1]) and torch.equal(ids, peer_ids.to(ids.device))):
raise RuntimeError("V1 graph/eager dump replay differs from the actual reply")
if want_margin:
if margin is None or reply[2] is None or not np.array_equal(margin.cpu().numpy(), reply[2]):
raise RuntimeError("V1 graph/eager margins differ")
rk = self.m.ranks[0]
B = host.shape[1]
hh = trace["head_hidden"]
head_norm = FU.rms_norm(hh, rk.norm, self.m.eps).cpu().view(B, N, -1)
for j in range(B):
slot, anchor, P, lo = (int(t) for t in host[:, j])
positions = list(range(max(lo, P - self.m.s.window + 1), P + 1))
keys = [(slot, p) for p in positions]
if not keys or any(key not in self.main_x for key in keys):
raise RuntimeError("V1 dump lacks inserted main_x provenance for its window")
src = [self.sources[key] for key in keys]
sample = self.samples[j] if self.samples else {}
key = sample.get("key", f"r{self.call:06d}_b{j}")
record: dict[str, Any] = {
"schema": "dspark.v1.arm-dump.v1", "P": P, "anchor": anchor, "n_out": n,
"drafts": reply[0][j].tolist(), "conf": torch.from_numpy(reply[1][j].copy()),
"base_logits": trace["base_logits"][j].detach().float().cpu(),
"main_x_P": self.main_x[slot, P].float(),
"kv_window": rk.ring_kv[slot, :, [p % self.m.s.window for p in positions]].transpose(0, 1).float().cpu(),
"window": {"n": len(positions), "taps_rows": src.count("taps"),
"engine_mx_rows": src.count("engine_mx"), "P_source": src[-1],
"first": positions[0], "last": positions[-1]},
"head_x": hh.view(B, N, -1)[j].float().cpu(), "head_hidden": head_norm[j],
"sample": sample, "wire": dict(self.wire),
"meta": {"arm": self.ident.get("arm", "k2-tp2"), "draft_head": self.ident.get("draft_head", "K6"),
"drafter_identity": self.ident["drafter_identity"], "weights": "official 1017 below K6 head",
"git_sha": os.environ.get("DSPARK_SOURCE_COMMIT", "unrecorded"),
"devices": os.environ.get("CUDA_VISIBLE_DEVICES"), "dtype_policy": "arm config/identity recipe",
"trace": "eager replay checked bit-for-bit against graph reply"},
}
for i in range(self.m.s.n_stages):
tag = f"stage{i}"
st = rk.stages[i]
xin = trace[tag + ".ffn_in"].view(B, N, -1)[j]
biased = torch.nn.functional.softplus(xin.float() @ st.gate_w.t()).sqrt() + st.gate_bias
top = biased.topk(self.m.s.top_k + 1, dim=-1)
record[f"s{i}"] = {
"attn_out": trace[tag + ".attn_out"].view(B, N, -1)[j].float().cpu(),
"ffn_in": xin.float().cpu(),
"ffn_out": trace[tag + ".ffn_out"].view(B, N, -1)[j].float().cpu(),
"h_out": trace[tag + ".stream"].view(B, N, self.m.s.hc_mult, -1)[j].float().cpu(),
"route": {"idx": trace[tag + ".experts"].view(B, N, -1)[j].long().cpu(),
"w": trace[tag + ".route_w"].view(B, N, -1)[j].float().cpu(),
"biased": biased.cpu(), "top_vals": top.values.cpu(), "top_ids": top.indices.cpu(),
"margin": (top.values[:, -2] - top.values[:, -1]).cpu()},
}
# Exclusive creation prevents silently mixing identities or reusing stale samples.
path = self.directory / f"{key}.pt"
fd = os.open(path, os.O_WRONLY | os.O_CREAT | os.O_EXCL, 0o600)
with os.fdopen(fd, "wb") as f:
torch.save(record, f)
self.files.append(str(path))
class DumpTP2Backend(TP2Backend):
arm_identity: dict[str, Any]
def __init__(self, *args: Any, sink: V1Dump, **kwargs: Any):
super().__init__(*args, **kwargs)
self.sink = sink
def draft(self, reqs: Any, N: int, want_margin: bool) -> tuple[np.ndarray, np.ndarray, np.ndarray | None]:
reply = super().draft(reqs, N, want_margin)
self.sink.emit(self.inp_buf[len(reqs)], N, max(k for *_r, k in reqs), want_margin, reply)
return cast(tuple[np.ndarray, np.ndarray, np.ndarray | None], reply)
class DumpDraftCore(DraftCore):
def _step(self, h: Any, body: Any, t_in: float) -> bytes | None:
self.backend.sink.wire = {"session": h.session, "seq": h.seq}
return super()._step(h, body, t_in)
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