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0693474 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 | """Window-only numeric telemetry. No tensor contents, device reads or synchronization."""
from __future__ import annotations
import functools
import hashlib
import json
import os
import sys
import time
from collections import deque
from collections.abc import Callable, Iterator, Mapping, Sized
from contextlib import contextmanager
from typing import ContextManager
from dataclasses import dataclass
from pathlib import Path
from typing import ParamSpec, Protocol, TypeVar, cast
Scalar = str | int | float | bool | None
P = ParamSpec('P')
T = TypeVar('T')
ENABLED = os.environ.get('VLLM_DSV41_PREFILL_TRACE', '0') == '1'
class Event(Protocol):
def record(self) -> None: ...
def query(self) -> bool: ...
def elapsed_time(self, end: Event) -> float: ...
class Backend(Protocol):
def event(self) -> Event: ...
def rank(self) -> int: ...
def capturing(self) -> bool: ...
class CudaBackend:
def event(self) -> Event:
import torch
factory = cast(Callable[..., Event], torch.cuda.Event)
return factory(enable_timing=True)
def rank(self) -> int:
import torch.distributed as dist
return dist.get_rank()
def capturing(self) -> bool:
import torch
return torch.cuda.is_current_stream_capturing()
@dataclass
class Pending:
row: dict[str, object]
start: Event
end: Event
class Recorder:
def __init__(self, path: Path, backend: Backend, limit: int = 8192) -> None:
if limit < 1:
raise ValueError('trace pending limit must be positive')
self.backend = backend
self.limit = limit
self.pending: deque[Pending] = deque()
self.path = path
self.context: dict[str, object] | None = None
self.sequence = 0
self.whole: ContextManager[None] | None = None
self.occurrences: dict[str, int] = {}
# Exclusive file creation: never append into a previous window's evidence.
self.stream = path.open('x', buffering=1)
def write(self, row: dict[str, object]) -> None:
self.stream.write(json.dumps(row, sort_keys=True, allow_nan=False) + '\n')
def drain(self) -> None:
# query() is nonblocking; never synchronize, even at final batch.
remaining: deque[Pending] = deque()
while self.pending:
item = self.pending.popleft()
if item.end.query():
item.row['cuda_ms'] = item.start.elapsed_time(item.end)
self.write(item.row)
else:
remaining.append(item)
self.pending = remaining
def call(self, label: str, metadata: dict[str, Scalar], fn: Callable[P, T],
*args: P.args, **kwargs: P.kwargs) -> T:
if self.context is None:
return fn(*args, **kwargs)
if self.backend.capturing():
raise RuntimeError('PF1 trace requires eager prefill; graph capture is unsupported')
if len(self.pending) >= self.limit:
self.drain()
if len(self.pending) >= self.limit:
raise RuntimeError('PF1 trace pending event bound exceeded; stop window, do not drop samples')
start, end = self.backend.event(), self.backend.event()
row: dict[str, object] = {**self.context, **metadata, 'phase': label, 'event': self.sequence,
'host_start_ns': time.monotonic_ns()}
self.sequence += 1
start.record()
begin = time.perf_counter_ns()
try:
return fn(*args, **kwargs)
except BaseException:
row['failed'] = True
raise
finally:
row['wall_ms'] = (time.perf_counter_ns() - begin) / 1e6
end.record()
self.pending.append(Pending(row, start, end))
_recorder: Recorder | None = None
def recorder() -> Recorder:
global _recorder
if _recorder is None:
root = Path(os.environ['VLLM_DSV41_PREFILL_TRACE_DIR'])
if not root.is_dir():
raise RuntimeError(f'PF1 trace directory must already exist: {root}')
backend = CudaBackend()
_recorder = Recorder(root / f'prefill-r{backend.rank()}-p{os.getpid()}.jsonl', backend)
return _recorder
def attr(value: object, name: str) -> object:
return getattr(value, name, None)
def meta(scope: Mapping[str, object]) -> dict[str, Scalar]:
"""Read only scalar Python attributes and tensor SHAPES, never GPU values."""
out: dict[str, Scalar] = {}
for key in ('row_start', 'row_end', 'topk_tokens', 'num_rows', 'num_tokens',
'num_prefills', 'num_prefill_tokens', 'num_chunks', 'chunk_idx',
'chunk_start', 'chunk_end', 'total_seq_lens', 'head_dim', 'use_fp4_cache',
'candidate_write', 'candidate_block_size', 'topk_backend', 'layer_name'):
value = scope.get(key)
if isinstance(value, (int, float, bool, str)):
out[key] = value
owner = scope.get('self')
for key in ('compress_ratio', 'max_prefill_buffer_size', 'unique_name', 'prefix'):
value = attr(owner, key)
if isinstance(value, (int, float, bool, str)):
out[key] = value
chunk = scope.get('chunk')
for key in ('token_start', 'token_end', 'total_seq_lens', 'local_total_seq_lens',
'max_local_total_seq_lens', 'num_reqs', 'skip_kv_gather'):
value = attr(chunk, key)
if isinstance(value, (int, bool)):
out['indexer_chunk_' + key] = value
for key in ('logits', 'k_quant', 'k_scale', 'q_chunk', 'q_slice', 'swa_indices_chunk',
'extra_sparse_indices_chunk', 'topk_indices', 'block_table',
'indexer_block_table', 'input_'):
value = scope.get(key)
shape = attr(value, 'shape')
if shape is not None:
# torch.Size is host-only, including for device tensors.
dims = tuple(cast(tuple[int, ...], shape))
out[key + '_shape'] = 'x'.join(str(int(dim)) for dim in dims)
size = 1
for dim in dims:
size *= int(dim)
out[key + '_elements'] = size
width = cast(Callable[[], int], getattr(value, 'element_size'))()
out[key + '_bytes'] = size * width
cm = scope.get('common_attn_metadata')
table = attr(cm, 'block_table_tensor')
shape = attr(table, 'shape')
if shape is not None:
dims = tuple(cast(tuple[int, ...], shape))
out['metadata_block_table_rows'] = int(dims[0])
out['metadata_block_table_columns'] = int(dims[1])
out['metadata_block_table_elements_bound'] = int(dims[0]) * int(dims[1])
return out
class HostArray(Protocol):
def __getitem__(self, key: int) -> object: ...
class Batch(Protocol):
req_ids: list[str]
num_reqs: int
has_prefill: bool
is_prefilling_np: HostArray
query_start_loc_np: HostArray
num_computed_prefill_tokens_np: HostArray
def begin(batch: Batch, dummy: bool, mode: object, cap: int = 0) -> None:
if not ENABLED:
return
rec = recorder()
rec.drain()
rec.context = None
if dummy or not batch.has_prefill:
return
if str(mode).split('.')[-1] != 'NONE':
raise RuntimeError('PF1 trace requires CUDAGraphMode.NONE for prefill')
members: list[dict[str, object]] = []
salt = os.environ['VLLM_DSV41_PREFILL_TRACE_RUN']
for i in range(batch.num_reqs):
# *_np arrays are host-known; no .item() on a CUDA tensor.
if not bool(batch.is_prefilling_np[i]):
continue
key = hashlib.sha256((salt + ':' + batch.req_ids[i]).encode()).hexdigest()[:24]
prefix = int(cast(int, batch.num_computed_prefill_tokens_np[i]))
q = int(cast(int, batch.query_start_loc_np[i + 1])) - int(cast(int, batch.query_start_loc_np[i]))
identity = f'{key}:{prefix}:{q}'
occurrence = rec.occurrences.get(identity, 0)
rec.occurrences[identity] = occurrence + 1
if len(rec.occurrences) > 32768:
raise RuntimeError('PF1 trace chunk identity bound exceeded; rotate window')
members.append({'request': key, 'chunk': identity + f':{occurrence}',
'new_tokens': q, 'prefix_tokens': prefix,
'batch_row_start': int(cast(int, batch.query_start_loc_np[i])),
'batch_row_end': int(cast(int, batch.query_start_loc_np[i + 1]))})
rec.context = {'rank': rec.backend.rank(), 'run': salt, 'max_model_len': cap, 'members': members}
rec.write({**rec.context, 'phase': 'chunk.begin', 'host_start_ns': time.monotonic_ns()})
rec.whole = span('chunk.total', {})
rec.whole.__enter__()
def call(label: str, metadata: dict[str, Scalar], fn: Callable[P, T],
*args: P.args, **kwargs: P.kwargs) -> T:
if not ENABLED or _recorder is None:
return fn(*args, **kwargs)
return _recorder.call(label, metadata, fn, *args, **kwargs)
def execution(fn: Callable[P, T]) -> Callable[P, T]:
@functools.wraps(fn)
def wrapped(*args: P.args, **kwargs: P.kwargs) -> T:
try:
return fn(*args, **kwargs)
finally:
if ENABLED and _recorder is not None:
if _recorder.whole is not None:
_recorder.whole.__exit__(*sys.exc_info())
_recorder.whole = None
if _recorder.context is not None:
_recorder.write({**_recorder.context, 'phase': 'chunk.end',
'host_end_ns': time.monotonic_ns(),
'pending_events': len(_recorder.pending)})
_recorder.context = None
_recorder.drain()
return wrapped
def plan(chunks: list[tuple[slice, slice]], seq: HostArray, query: HostArray,
workspace: int, logits_bytes: int, offset: int) -> list[tuple[slice, slice]]:
if ENABLED and _recorder is not None and _recorder.context is not None:
rec = _recorder
assert rec.context is not None
requested_rows = sum(int(cast(int, seq[j])) for j in range(len(cast(Sized, seq))))
for i, (req, rows) in enumerate(chunks):
start, stop = cast(int, req.start) - offset, cast(int, req.stop) - offset
key_rows = sum(int(cast(int, seq[j])) for j in range(start, stop))
new_rows = sum(int(cast(int, query[j])) for j in range(start, stop))
rec.write({**rec.context, 'phase': 'indexer.plan', 'plan_chunk': i,
'req_start': start + offset, 'req_stop': stop + offset,
'query_start': rows.start, 'query_stop': rows.stop,
'compressed_key_rows': key_rows, 'query_rows_before_split': new_rows,
'workspace_rows': workspace, 'logits_budget_bytes': logits_bytes,
'workspace_exceeded': key_rows > workspace,
'all_requested_key_rows': requested_rows,
'workspace_group_split': requested_rows > workspace and key_rows <= workspace,
'query_subchunked': rows.start != 0 or rows.stop != new_rows,
'fallback': 'single_request_over_workspace' if key_rows > workspace else 'none'})
return chunks
# A context manager is also needed around existing collective bodies (including returns).
@contextmanager
def span(label: str, metadata: dict[str, Scalar]) -> Iterator[None]:
if not ENABLED or _recorder is None or _recorder.context is None:
yield
return
rec = _recorder
assert rec.context is not None
if rec.backend.capturing():
raise RuntimeError('PF1 trace cannot insert events into a captured graph')
if len(rec.pending) >= rec.limit:
rec.drain()
if len(rec.pending) >= rec.limit:
raise RuntimeError('PF1 trace pending event bound exceeded')
start, end = rec.backend.event(), rec.backend.event()
row: dict[str, object] = {**rec.context, **metadata, 'phase': label, 'event': rec.sequence,
'host_start_ns': time.monotonic_ns()}
rec.sequence += 1
start.record()
begin_ns = time.perf_counter_ns()
try:
yield
except BaseException:
row['failed'] = True
raise
finally:
row['wall_ms'] = (time.perf_counter_ns() - begin_ns) / 1e6
end.record()
rec.pending.append(Pending(row, start, end))
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