ColossalAI/colossalai/auto_parallel/offload/mem_optimize.py

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from typing import Dict
import torch
import torch.fx
from torch.fx import GraphModule
from torch.utils._pytree import tree_map
from colossalai.fx import ColoTracer, is_compatible_with_meta
from colossalai.fx.passes.meta_info_prop import MetaInfoProp
from .base_offload_module import BaseOffloadModule
from .region_manager import RegionManager
from .runtime import runtime_asyn_offload_apply_pass, runtime_syn_offload_apply_pass
from .util import GlobalRuntimeInfo, compute_act_peak_mem, compute_max_param_mem, compute_total_param_mem
def memory_optimize(model: torch.nn.Module,
inps: Dict[str, torch.Tensor],
memory_budget: float = -1.0,
solver_name: str = 'asyn'):
model = model.cpu().half()
tracer = ColoTracer()
assert is_compatible_with_meta()
wrap_fn = lambda x: x.to("meta") if isinstance(x, torch.Tensor) else x
meta_args = tree_map(wrap_fn, inps)
graph = tracer.trace(model, meta_args=meta_args)
gm = GraphModule(model, graph, model.__class__.__name__)
interp = MetaInfoProp(gm)
interp.propagate(*meta_args.values())
region_manager = RegionManager(graph, solver_name=solver_name, memory_budget=memory_budget)
region_manager._build_regions()
GlobalRuntimeInfo().region_list = region_manager.region_list
act_peak_mem = compute_act_peak_mem(region_manager.region_list) / 1024**2
max_param_mem = compute_max_param_mem(region_manager.region_list) / 1024**2
total_param_mem = compute_total_param_mem(region_manager.region_list) / 1024**2
print(
f"act_peak_mem={act_peak_mem:.3f} MB | max_param_mem={max_param_mem:.3f} MB | total_param_mem={total_param_mem:.3f}"
)
if solver_name == 'syn':
gm = runtime_syn_offload_apply_pass(gm, region_manager.region_list)
elif solver_name == 'asyn':
gm = runtime_asyn_offload_apply_pass(gm, region_manager.region_list)
else:
raise TypeError(f"Unknown solver name {solver_name}!")
gm.recompile()
optimized_model = BaseOffloadModule(gm, region_manager, solver_name == 'syn')
return optimized_model