File size: 11,076 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 | """NVLink peer-to-peer collectives for the TP = 2 worker (p2p.cu): build / load and the per-rank state.
The extension is compiled once with torch.utils.cpp_extension (nvcc from CUDA 12.6, sm_86 only) into
$DSPARK_P2P_BUILD (default ~/dsv41-offload/e/build/p2p); later loads reuse the build while the source is unchanged.
"""
from __future__ import annotations
import math
import os
import time
from collections.abc import Iterable
from types import ModuleType
import torch
_EXT: ModuleType | None = None
SRC = os.path.join(os.path.dirname(os.path.abspath(__file__)), "p2p.cu")
DEFAULT_TIMEOUT_MS = 2000.0
def ext() -> ModuleType:
global _EXT
if _EXT is None and os.environ.get("DSPARK_P2P_PREBUILT", "").strip():
# production: the pinned build of exactly this p2p.cu, never a compile at start (prebuilt.py)
from .prebuilt import load_prebuilt
_EXT = load_prebuilt(os.environ["DSPARK_P2P_PREBUILT"].strip(), SRC)
if _EXT is None:
from torch.utils.cpp_extension import load
cuda = os.environ.get("DSPARK_P2P_CUDA_HOME", "/usr/local/cuda-12.6")
os.environ["CUDA_HOME"] = cuda
if os.path.join(cuda, "bin") not in os.environ.get("PATH", "").split(":"):
os.environ["PATH"] = os.path.join(cuda, "bin") + ":" + os.environ.get("PATH", "")
build = os.path.expanduser(os.environ.get("DSPARK_P2P_BUILD", "~/dsv41-offload/e/build/p2p"))
os.makedirs(build, exist_ok=True)
_EXT = load(name="dspark_p2p", sources=[SRC], build_directory=build, verbose=False,
extra_cflags=["-O2"], extra_cuda_cflags=["-O3", "-gencode=arch=compute_86,code=sm_86"])
return _EXT
def preload(dev: torch.device) -> None:
"""Launch every collective kernel once on `dev` with itself as the peer (each passes its own handshake at once).
CUDA lazy loading may synchronize the context on a kernel's first launch; a first launch while a collective
kernel spins on the same device, waiting for work the blocked host has not issued yet, would deadlock until the
timeout (CUDA programming guide, lazy loading: concurrent execution). Run before any collective is in flight."""
e = ext()
with torch.cuda.device(dev):
st = RankState(dev, 4 * 5120)
x = torch.ones(5120, dtype=torch.float32, device=dev)
e.ar_sum(x, torch.empty_like(x), st.buf, st.ptr, st.max_words, 1, int(1e9))
e.ar_sum(x, torch.empty(5120, dtype=torch.bfloat16, device=dev), st.buf, st.ptr, st.max_words, 1, int(1e9))
v = torch.zeros(1, dtype=torch.float32, device=dev)
i = torch.zeros(1, dtype=torch.int64, device=dev)
out = torch.zeros(1, 1, dtype=torch.int64, device=dev)
e.xchg_argmax(v, i, 0, 1, out, 0, True, st.buf, st.ptr, st.max_words, int(1e9))
e.xchg_margin(torch.zeros(1, 2, dtype=torch.float32, device=dev), i, 0, 1, out,
torch.zeros(1, 1, dtype=torch.float32, device=dev), 0, True, st.buf, st.ptr, st.max_words,
int(1e9))
e.xchg_markov(i, torch.zeros(1, dtype=torch.int32, device=dev), torch.zeros(1, 1, device=dev),
torch.zeros(1, 1, dtype=torch.int64, device=dev), torch.zeros(1, 1, device=dev),
torch.zeros(1, 1, dtype=torch.int64, device=dev), 0, 1, out, 0, True, st.buf, st.ptr,
st.max_words, int(1e9))
torch.cuda.synchronize(dev)
h = [int(a) for a in st.hdr[:5].cpu()]
if h[2] != 0 or h[4] != 5:
raise RuntimeError(f"p2p preload on {dev}: header {h}")
e.cancel(st.buf)
e.reset(st.buf)
torch.cuda.synchronize(dev)
class RankState:
"""One rank's state block (header, flags, double receive buffer) in its own device memory."""
def __init__(self, dev: torch.device, max_words: int):
self.dev = dev
self.max_words = max_words
self.buf = ext().allocate_state(max_words, dev.index)
self.hdr = self.buf[:64].view(torch.int32) # epoch, done, error, err_epoch, count, ...
@property
def ptr(self) -> int:
return self.buf.data_ptr()
def check_allocator() -> None:
"""Async Torch allocation remains unsupported. Peer blocks use native cudaMalloc independently of Torch's
expandable-segment configuration, including runtime changes made by exllamav3's Python import."""
for var in ("PYTORCH_CUDA_ALLOC_CONF", "PYTORCH_ALLOC_CONF"):
v = os.environ.get(var, "").replace(" ", "").lower()
if "backend:cudamallocasync" in v:
raise RuntimeError(f"{var}={os.environ[var]!r}: the p2p worker has not qualified cudaMallocAsync")
def wait_idle(devs: Iterable[torch.device], timeout_s: float,
streams: Iterable[torch.cuda.Stream] = ()) -> None:
"""Wait until every device's current stream is idle, at most timeout_s (raises TimeoutError): never an unbounded
synchronize on a path that runs after a failure, where a wedged GPU would block the worker forever."""
evs = []
all_streams = dict.fromkeys([*streams, *(torch.cuda.current_stream(d) for d in dict.fromkeys(devs))])
for stream in all_streams:
d = stream.device
with torch.cuda.device(d):
ev = torch.cuda.Event()
ev.record(stream)
evs.append((d, ev))
deadline = time.monotonic() + timeout_s
for d, ev in evs:
while not ev.query():
if time.monotonic() > deadline:
raise TimeoutError(f"device {d} did not go idle within {timeout_s:.1f} s")
time.sleep(0.0005)
class P2PPair:
"""The two ranks' states with peer access enabled both ways. Collective calls launch on the calling rank's
current stream; both ranks must issue the same sequence of collectives (TPDrafter does). vocab bounds the ids an
exchange accepts from the peer."""
def __init__(self, devs: list[torch.device], max_words: int, timeout_ms: float | None = None,
vocab: int = 1 << 62):
if len(devs) != 2 or devs[0] == devs[1] or any(d.type != "cuda" or d.index is None for d in devs):
raise ValueError(f"P2PPair needs two distinct devices, got {devs}")
if max_words <= 0 or max_words % 4 or vocab <= 0:
raise ValueError("P2PPair requires positive 4-aligned max_words and positive vocab")
if timeout_ms is None:
timeout_ms = float(os.environ.get("DSPARK_TP_TIMEOUT_MS", DEFAULT_TIMEOUT_MS))
if not math.isfinite(timeout_ms) or timeout_ms <= 0:
raise ValueError(f"P2PPair requires finite positive timeout_ms, got {timeout_ms}")
check_allocator()
self.poisoned: str | None = None
self.launch_streams: set[torch.cuda.Stream] = set()
self.vocab = int(vocab)
e = ext()
e.enable_peer(devs[0].index, devs[1].index)
e.enable_peer(devs[1].index, devs[0].index)
for d in devs:
preload(d)
self.devs = devs
self.max_words = max_words
self.timeout_ns = int(timeout_ms * 1e6)
self.idle_timeout_s = max(10.0, 20 * timeout_ms / 1000.0) # host waits on a failure path are bounded too
self.st = [RankState(d, max_words) for d in devs]
self.cancel_streams = [torch.cuda.Stream(d) for d in devs]
for d in devs:
torch.cuda.synchronize(d)
def peer(self, r: int) -> int:
return self.st[1 - r].ptr
def poison(self, reason: str) -> None:
"""Host-only latch. A CUDA exception requires process restart, never header clearing or buffer reuse."""
if self.poisoned is None:
self.poisoned = reason
def _usable(self) -> None:
if self.poisoned is not None:
raise RuntimeError(f"TP CUDA context poisoned; restart worker: {self.poisoned}")
def _launch(self, r: int, name: str, *args: object) -> None:
self._usable()
try:
# Retain every eager stream: checking only the caller's current stream can miss a parked collective.
stream = torch.cuda.current_stream(self.devs[r])
self.launch_streams.add(stream)
getattr(ext(), name)(*args)
except RuntimeError as error:
self.poison(str(error))
raise
def ar_sum(self, r: int, x: torch.Tensor, out: torch.Tensor, nblocks: int) -> torch.Tensor:
self._launch(r, "ar_sum", x, out, self.st[r].buf, self.peer(r), self.max_words, nblocks, self.timeout_ns)
return out
def xchg_argmax(self, r: int, val: torch.Tensor, idx: torch.Tensor, v_off: int, out: torch.Tensor,
col: int) -> None:
self._launch(r, "xchg_argmax", val, idx, v_off, self.vocab, out, col, r == 0, self.st[r].buf, self.peer(r),
self.max_words, self.timeout_ns)
def xchg_margin(self, r: int, top2: torch.Tensor, idx: torch.Tensor, v_off: int, out: torch.Tensor,
mg: torch.Tensor, col: int) -> None:
self._launch(r, "xchg_margin", top2, idx, v_off, self.vocab, out, mg, col, r == 0, self.st[r].buf, self.peer(r),
self.max_words, self.timeout_ns)
def xchg_markov(self, r: int, prev: torch.Tensor, slot_of_token: torch.Tensor, parts: tuple, v_off: int,
out: torch.Tensor, col: int) -> None:
vals_c, idx_c, vals_u, idx_u = parts
self._launch(r, "xchg_markov", prev, slot_of_token, vals_c, idx_c, vals_u, idx_u, v_off, self.vocab, out, col, r == 0,
self.st[r].buf, self.peer(r), self.max_words, self.timeout_ns)
def headers(self) -> list[list[int]]:
"""Host read of both headers once both devices are idle (bounded wait: TimeoutError)."""
self._usable()
try:
wait_idle(self.devs, self.idle_timeout_s, self.launch_streams)
out = []
for s in self.st:
with torch.cuda.device(s.dev):
out.append([int(v) for v in s.hdr[:5].cpu()])
return out
except (RuntimeError, TimeoutError) as error:
self.poison(str(error))
raise
def cancel(self) -> None:
"""Abort both ranks on independent, pre-created streams. Buffers remain owned until idle/reset completes."""
self._usable()
for r, stream in enumerate(self.cancel_streams):
with torch.cuda.device(self.devs[r]), torch.cuda.stream(stream):
self._launch(r, "cancel", self.st[r].buf)
def reset(self) -> None:
"""Zero both headers and flag sets. Only with both devices idle (bounded waits before and after)."""
self._usable()
try:
wait_idle(self.devs, self.idle_timeout_s, self.launch_streams)
for s in self.st:
with torch.cuda.device(s.dev):
ext().reset(s.buf)
wait_idle(self.devs, self.idle_timeout_s)
self.launch_streams.clear()
except (RuntimeError, TimeoutError) as error:
self.poison(str(error))
raise
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