229 lines
9.0 KiB
Python
229 lines
9.0 KiB
Python
# Adapted from https://github.com/vllm-project/vllm/blob/v0.10.0/vllm/compilation/cuda_piecewise_backend.py
|
|
|
|
import dataclasses
|
|
import logging
|
|
from contextlib import ExitStack
|
|
from typing import Any, Callable, Optional, Union
|
|
from unittest.mock import patch
|
|
|
|
import torch
|
|
import torch.fx as fx
|
|
|
|
import sglang.srt.compilation.weak_ref_tensor_jit # noqa: F401
|
|
from sglang.srt.compilation.compilation_config import CompilationConfig
|
|
from sglang.srt.compilation.compilation_counter import compilation_counter
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
def weak_ref_tensor(tensor: Any) -> Any:
|
|
"""
|
|
Create a weak reference to a tensor.
|
|
The new tensor will share the same data as the original tensor,
|
|
but will not keep the original tensor alive.
|
|
"""
|
|
if isinstance(tensor, torch.Tensor):
|
|
# TODO(yuwei): introduce weak_ref_tensor from sgl_kernel
|
|
return torch.ops.jit_weak_ref_tensor.weak_ref_tensor(tensor)
|
|
return tensor
|
|
|
|
|
|
def weak_ref_tensors(
|
|
tensors: Union[torch.Tensor, list[torch.Tensor], tuple[torch.Tensor]]
|
|
) -> Union[torch.Tensor, list[Any], tuple[Any], Any]:
|
|
"""
|
|
Convenience function to create weak references to tensors,
|
|
for single tensor, list of tensors or tuple of tensors.
|
|
"""
|
|
if isinstance(tensors, torch.Tensor):
|
|
return weak_ref_tensor(tensors)
|
|
if isinstance(tensors, list):
|
|
return [weak_ref_tensor(t) for t in tensors]
|
|
if isinstance(tensors, tuple):
|
|
return tuple(weak_ref_tensor(t) for t in tensors)
|
|
raise ValueError("Invalid type for tensors")
|
|
|
|
|
|
@dataclasses.dataclass
|
|
class ConcreteSizeEntry:
|
|
runtime_shape: int
|
|
need_to_compile: bool # the size is in compile_sizes
|
|
use_cudagraph: bool # the size is in cudagraph_capture_sizes
|
|
|
|
compiled: bool = False
|
|
runnable: Callable = None # type: ignore
|
|
num_finished_warmup: int = 0
|
|
cudagraph: Optional[torch.cuda.CUDAGraph] = None
|
|
output: Optional[Any] = None
|
|
|
|
# for cudagraph debugging, track the input addresses
|
|
# during capture, and check if they are the same during replay
|
|
input_addresses: Optional[list[int]] = None
|
|
|
|
|
|
class CUDAPiecewiseBackend:
|
|
|
|
def __init__(
|
|
self,
|
|
graph: fx.GraphModule,
|
|
compile_config: CompilationConfig,
|
|
inductor_config: dict[str, Any],
|
|
graph_pool: Any,
|
|
piecewise_compile_index: int,
|
|
total_piecewise_compiles: int,
|
|
sym_shape_indices: list[int],
|
|
compiled_graph_for_general_shape: Callable,
|
|
sglang_backend,
|
|
):
|
|
"""
|
|
The backend for piecewise compilation.
|
|
It mainly handles the compilation and cudagraph capturing.
|
|
|
|
We will compile `self.graph` once for the general shape,
|
|
and then compile for different shapes specified in
|
|
`compilation_config.compile_sizes`.
|
|
|
|
Independently, we will capture cudagraph for different shapes.
|
|
|
|
If a shape needs both compilation and cudagraph, we will
|
|
compile it first, and then capture cudagraph.
|
|
"""
|
|
self.graph = graph
|
|
self.inductor_config = inductor_config
|
|
self.graph_pool = graph_pool
|
|
self.piecewise_compile_index = piecewise_compile_index
|
|
self.total_piecewise_compiles = total_piecewise_compiles
|
|
self.sglang_backend = sglang_backend
|
|
|
|
self.is_first_graph = piecewise_compile_index == 0
|
|
self.is_last_graph = piecewise_compile_index == total_piecewise_compiles - 1
|
|
|
|
self.compile_sizes: set[int] = set([])
|
|
self.compile_config = compile_config
|
|
self.cudagraph_capture_sizes: set[int] = set(compile_config.get_capture_sizes())
|
|
|
|
self.first_run_finished = False
|
|
|
|
self.compiled_graph_for_general_shape = compiled_graph_for_general_shape # noqa
|
|
|
|
self.sym_shape_indices = sym_shape_indices
|
|
|
|
self.is_debugging_mode = True
|
|
|
|
# the entries for different shapes that we need to either
|
|
# compile or capture cudagraph
|
|
self.concrete_size_entries: dict[int, ConcreteSizeEntry] = {}
|
|
|
|
# to_be_compiled_sizes tracks the remaining sizes to compile,
|
|
# and updates during the compilation process, so we need to copy it
|
|
self.to_be_compiled_sizes: set[int] = self.compile_sizes.copy()
|
|
for shape in self.compile_sizes.union(self.cudagraph_capture_sizes):
|
|
self.concrete_size_entries[shape] = ConcreteSizeEntry(
|
|
runtime_shape=shape,
|
|
need_to_compile=shape in self.compile_sizes,
|
|
use_cudagraph=shape in self.cudagraph_capture_sizes,
|
|
)
|
|
|
|
def check_for_ending_compilation(self):
|
|
if self.is_last_graph and not self.to_be_compiled_sizes:
|
|
# no specific sizes to compile
|
|
# save the hash of the inductor graph for the next run
|
|
self.sglang_backend.compiler_manager.save_to_file()
|
|
|
|
def __call__(self, *args) -> Any:
|
|
if not self.first_run_finished:
|
|
self.first_run_finished = True
|
|
self.check_for_ending_compilation()
|
|
return self.compiled_graph_for_general_shape(*args)
|
|
runtime_shape = args[self.sym_shape_indices[0]]
|
|
if runtime_shape not in self.concrete_size_entries:
|
|
# we don't need to do anything for this shape
|
|
return self.compiled_graph_for_general_shape(*args)
|
|
|
|
entry = self.concrete_size_entries[runtime_shape]
|
|
|
|
if entry.runnable is None:
|
|
entry.runnable = self.compiled_graph_for_general_shape
|
|
|
|
if entry.need_to_compile and not entry.compiled:
|
|
entry.compiled = True
|
|
self.to_be_compiled_sizes.remove(runtime_shape)
|
|
# args are real arguments
|
|
entry.runnable = self.sglang_backend.compiler_manager.compile(
|
|
self.graph,
|
|
args,
|
|
self.inductor_config,
|
|
graph_index=self.piecewise_compile_index,
|
|
num_graphs=self.total_piecewise_compiles,
|
|
runtime_shape=runtime_shape,
|
|
)
|
|
|
|
# finished compilations for all required shapes
|
|
if self.is_last_graph and not self.to_be_compiled_sizes:
|
|
self.check_for_ending_compilation()
|
|
|
|
# Skip CUDA graphs if this entry doesn't use them OR
|
|
# if we're supposed to skip them globally
|
|
# skip_cuda_graphs = get_forward_context().skip_cuda_graphs
|
|
# if not entry.use_cudagraph or skip_cuda_graphs:
|
|
# return entry.runnable(*args)
|
|
|
|
if entry.cudagraph is None:
|
|
if entry.num_finished_warmup < 1: # noqa
|
|
entry.num_finished_warmup += 1
|
|
return entry.runnable(*args)
|
|
|
|
input_addresses = [
|
|
x.data_ptr() for x in args if isinstance(x, torch.Tensor)
|
|
]
|
|
entry.input_addresses = input_addresses
|
|
cudagraph = torch.cuda.CUDAGraph()
|
|
|
|
with ExitStack() as stack:
|
|
if not self.is_first_graph:
|
|
# during every model forward, we will capture
|
|
# many pieces of cudagraphs (roughly one per layer).
|
|
# running gc again and again across layers will
|
|
# make the cudagraph capture very slow.
|
|
# therefore, we only run gc for the first graph,
|
|
# and disable gc for the rest of the graphs.
|
|
stack.enter_context(patch("gc.collect", lambda: None))
|
|
stack.enter_context(patch("torch.cuda.empty_cache", lambda: None))
|
|
|
|
# mind-exploding: carefully manage the reference and memory.
|
|
with torch.cuda.graph(cudagraph, pool=self.graph_pool):
|
|
# `output` is managed by pytorch's cudagraph pool
|
|
output = entry.runnable(*args)
|
|
if self.is_last_graph:
|
|
# by converting it to weak ref,
|
|
# the original `output` will immediately be released
|
|
# to save memory. It is only safe to do this for
|
|
# the last graph, because the output of the last graph
|
|
# will not be used by any other cuda graph.
|
|
output = weak_ref_tensors(output)
|
|
|
|
# here we always use weak ref for the output
|
|
# to save memory
|
|
entry.output = weak_ref_tensors(output)
|
|
entry.cudagraph = cudagraph
|
|
|
|
compilation_counter.num_cudagraph_captured += 1
|
|
|
|
# important: we need to return the output, rather than
|
|
# the weak ref of the output, so that pytorch can correctly
|
|
# manage the memory during cuda graph capture
|
|
return output
|
|
|
|
if self.is_debugging_mode:
|
|
# check if the input addresses are the same
|
|
new_input_addresses = [
|
|
x.data_ptr() for x in args if isinstance(x, torch.Tensor)
|
|
]
|
|
assert new_input_addresses == entry.input_addresses, (
|
|
"Input addresses for cudagraphs are different during replay."
|
|
f" Expected {entry.input_addresses}, got {new_input_addresses}"
|
|
)
|
|
|
|
entry.cudagraph.replay()
|
|
return entry.output
|