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e6066e8 | 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 | # Copyright (c) 2026 SandAI. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import collections
import operator
from typing import Any, Dict
import torch
from magi_compiler.config import CompileConfig
from magi_compiler.offload.profiler import OffloadProfiler
from magi_compiler.offload.scheduler import OffloadRuntimeContext, SchedulerFactory
from magi_compiler.utils.nvtx import add_nvtx_event
from torch.fx import GraphModule, Node
from torch.fx.node import map_arg
class OffloadExecutor:
def __init__(self, graph_module: GraphModule, compile_config: CompileConfig):
self.graph_module = graph_module
self.compile_config = compile_config
self.compute_stream = torch.cuda.current_stream()
self.h2d_stream = torch.cuda.Stream()
self.warmup = True
self.second_call = False
self.buffers: Dict[str, torch.Tensor] = {}
self.persistent_weights: Dict[str, torch.Tensor] = {}
self.submod_0_weight_handoff: Dict[Node, torch.Tensor] = {}
self._analyze_graph()
self.profiler = OffloadProfiler()
common_args = {
"submod_nodes": self.submod_nodes,
"submod_weights_map": self.submod_weights_map,
"name_node_map": self.name_node_map,
"weight_sizes": self.submod_weight_sizes,
}
self.scheduler = SchedulerFactory.create(self.compile_config, common_args)
def _analyze_graph(self):
self.submod_nodes = [n for n in self.graph_module.graph.nodes if n.op == "call_module"]
self.placeholder_nodes = []
self.arg_index_weight = {}
self.user_counts = collections.defaultdict(int)
self.name_node_map = {}
placeholder_idx = 0
for node in self.graph_module.graph.nodes:
for input_node in node.all_input_nodes:
self.user_counts[input_node] += 1
if node.op == "placeholder":
is_w = isinstance(node.meta.get("example_value"), torch.nn.Parameter)
self.arg_index_weight[placeholder_idx] = is_w
self.placeholder_nodes.append(node)
self.name_node_map[node.name] = node
placeholder_idx += 1
self.submod_weights_map = {}
self.submod_weight_sizes = {}
for node in self.submod_nodes:
weight_names = []
size = 0
for arg in node.args:
if isinstance(arg, Node) and self._is_weight_node(arg):
if arg.name in self.name_node_map:
weight_names.append(arg.name)
val = arg.meta.get("example_value")
if val is not None:
size += val.numel() * val.element_size()
self.submod_weights_map[node.name] = weight_names
self.submod_weight_sizes[node.name] = size
def _is_weight_node(self, node: Node) -> bool:
return node.op == "placeholder" and isinstance(node.meta.get("example_value"), torch.nn.Parameter)
def _prepare_inputs(self, args) -> Dict[Node, Any]:
env = {}
args = list(args)
submod_0 = self.submod_nodes[0]
for i, node in enumerate(self.placeholder_nodes):
arg_val = args[i]
is_weight = self.arg_index_weight[i]
# case 1: input tensor
if not is_weight:
if isinstance(arg_val, torch.Tensor):
arg_val = arg_val.to("cuda", non_blocking=False)
env[node] = arg_val
continue
# case 2: kept weight
if self.scheduler.is_kept(node.name):
if node.name not in self.persistent_weights:
t = arg_val.to("cuda", non_blocking=False) if arg_val.device.type == "cpu" else arg_val
self.persistent_weights[node.name] = t
env[node] = self.persistent_weights[node.name]
continue
# case 3: submod 0 weight
if submod_0 in node.users:
if self.warmup and arg_val.device.type == "cpu":
self.buffers[node.name] = arg_val
arg_val = arg_val.to("cuda", non_blocking=False)
elif not self.warmup:
if node in self.submod_0_weight_handoff:
arg_val = self.submod_0_weight_handoff[node]
del self.submod_0_weight_handoff[node]
env[node] = arg_val
return env
def _finalize_warmup(self):
profile_results = self.profiler.summarize()
self.scheduler.schedule_kept_weights(profile_results)
self.warmup = False
def __call__(self, *args):
env = self._prepare_inputs(args)
current_user_counts = self.user_counts.copy()
runtime_ctx = OffloadRuntimeContext(
env=env,
h2d_stream=self.h2d_stream,
compute_stream=self.compute_stream,
buffers=self.buffers,
submod_0_handoff=self.submod_0_weight_handoff,
need_profile=self.second_call or self.warmup,
)
need_profile = self.second_call
for node in self.graph_module.graph.nodes:
if node.op == "placeholder":
continue
elif node.op == "call_module":
self.scheduler.prefetch(node.name, runtime_ctx)
if need_profile:
if torch.distributed.is_initialized():
torch.distributed.barrier()
self.profiler.start_compute_profile(node.name, self.compute_stream)
with add_nvtx_event(node.name):
with torch.cuda.stream(self.compute_stream):
s_args = map_arg(node.args, lambda n: env[n])
s_kwargs = map_arg(node.kwargs, lambda n: env[n])
env[node] = getattr(self.graph_module, node.target)(*s_args, **s_kwargs)
del s_args, s_kwargs
if need_profile:
if torch.distributed.is_initialized():
torch.distributed.barrier()
self.profiler.end_compute_profile(node.name, self.compute_stream)
elif node.op == "call_function":
# ... (Standard execution logic same as before)
if node.target == operator.getitem:
parent_node, idx = node.args
env[node] = env[parent_node][idx]
else:
with torch.cuda.stream(self.compute_stream):
f_args = map_arg(node.args, lambda n: env[n])
f_kwargs = map_arg(node.kwargs, lambda n: env[n])
env[node] = node.target(*f_args, **f_kwargs)
elif node.op == "output":
if self.second_call:
self._finalize_warmup()
self.second_call = False
if self.warmup:
self.second_call = True
self.warmup = False
return map_arg(node.args[0], lambda n: env[n])
# Memory Management
for input_node in node.all_input_nodes:
current_user_counts[input_node] -= 1
if current_user_counts[input_node] == 0:
if input_node in env:
tensor_obj = env[input_node]
if isinstance(tensor_obj, torch.Tensor) and tensor_obj.is_cuda:
tensor_obj.record_stream(self.compute_stream)
del env[input_node]
return None
class OffloadWrapper:
def __init__(self, graph_module: torch.fx.GraphModule, compile_config: CompileConfig):
self.executor = OffloadExecutor(graph_module, compile_config)
def __call__(self, *args):
return self.executor(*args)
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