Files
sglang/python/sglang/srt/operations.py

155 lines
4.2 KiB
Python

import os
from contextlib import contextmanager
from dataclasses import dataclass
from typing import Any, Callable, Dict, Generator, List, Sequence, Union
import torch
_ENABLE_PROFILE = bool(int(os.environ.get("SGLANG_OPERATIONS_ENABLE_PROFILE", "0")))
if _ENABLE_PROFILE:
import nvtx
def execute_operations(inputs, operations):
stages = _convert_operations_to_stages(decorate_operations(operations))
executor = _StageExecutor("primary", stages, inputs=inputs)
for _ in range(executor.num_stages):
executor.next()
assert executor.done
return executor.output
class YieldOperation:
pass
@dataclass
class ExecutionOperation:
debug_name: str
fn: Callable
Operation = Union[YieldOperation, ExecutionOperation, Callable]
Stage = List[ExecutionOperation]
class _StageExecutor:
def __init__(self, debug_name: str, stages: List[Stage], inputs):
self._debug_name = debug_name
self._stages = stages
self._index = 0
self._stage_state = _StateDict()
self._stage_output = inputs
def next(self):
assert not self.done
stage = self._stages[self._index]
with _annotate_region(debug_name=f"{self._debug_name}{self._index}"):
for op in stage:
with _annotate_region(debug_name=op.debug_name):
self._stage_output = op.fn(
state=self._stage_state,
**(
self._stage_output if self._stage_output is not None else {}
),
)
self._index += 1
@property
def output(self):
assert self.done
return self._stage_output
@property
def done(self):
return self._index >= self.num_stages
@property
def num_stages(self):
return len(self._stages)
@contextmanager
def _annotate_region(debug_name):
if _ENABLE_PROFILE:
with torch.autograd.profiler.record_function(debug_name):
with nvtx.annotate(debug_name):
yield
else:
yield
class _StateDict:
def __init__(self):
self._data = {}
def __setattr__(self, key, value):
if key == "_data":
super().__setattr__(key, value)
return
assert (
key not in self._data
), f"`{key}` already exist, are you sure you want to override it?"
self._data[key] = value
def __getattr__(self, item):
return self._data[item]
def __delattr__(self, item):
del self._data[item]
def pop(self, item):
return self._data.pop(item)
def update(self, values: Dict[str, Any]):
for k, v in values.items():
setattr(self, k, v)
def clear(self, expect_keys: Sequence[str]):
if set(self._data.keys()) != set(expect_keys):
raise Exception(
f"Unexpected keys when clearning. This may indicate you do not release memory early enough but leave it to here. {list(self._data.keys())=} {expect_keys=}"
)
self._data.clear()
def _convert_operations_to_stages(operations: List[Operation]) -> List[Stage]:
operation_chunks = list(
_chunk_by_separator(operations, lambda op: isinstance(op, YieldOperation))
)
assert all(len(chunk) > 0 for chunk in operation_chunks)
return operation_chunks
def _chunk_by_separator(
items: List[Any], is_separator: Callable[[Any], bool]
) -> Generator[List[Any], None, None]:
pending_items = []
for item in items:
if is_separator(item):
yield pending_items
pending_items = []
else:
pending_items.append(item)
if len(pending_items) > 0:
yield pending_items
def decorate_operations(operations: List[Operation], debug_name_prefix: str = ""):
return [_decorate_operation(op, debug_name_prefix) for op in operations]
def _decorate_operation(operation: Operation, debug_name_prefix: str):
if isinstance(operation, YieldOperation):
return operation
return ExecutionOperation(
debug_name=debug_name_prefix
+ getattr(operation, "__name__", "unknown").replace("op_", ""),
fn=operation,
)