[test] fixed gemini plugin test

colossalai/auto_parallel/offload/base_offload_module.py

@@ -1,10 +1,11 @@
-from typing import Optional, Set
 from functools import partial
+from typing import Optional, Set
+
 import torch
 import torch.nn as nn
 
-from colossalai.nn.parallel.data_parallel import _cast_float
 from colossalai.gemini.tensor_utils import free_storage
+from colossalai.nn.parallel.data_parallel import _cast_float
 
 from .region_manager import RegionManager
 from .util import GlobalRuntimeInfo
@@ -20,10 +21,7 @@ class BaseOffloadModule:
         is_sync (bool): synchronous mode or not.
     """
 
-    def __init__(self,
-                 model: nn.Module,
-                 region_manager: RegionManager,
-                 is_sync=True):
+    def __init__(self, model: nn.Module, region_manager: RegionManager, is_sync=True):
 
         self.model = model
         self.region_manager = region_manager
@@ -69,8 +67,8 @@ def _post_backward(self):
         for p in self.model.parameters():
             p.grad = None
 
-        GlobalRuntimeInfo.fwd_prefetch_event_map.clear()
-        GlobalRuntimeInfo.bwd_prefetch_event_map.clear()
+        GlobalRuntimeInfo().fwd_prefetch_event_map.clear()
+        GlobalRuntimeInfo().bwd_prefetch_event_map.clear()
 
     def grad_handle(self, p, grad):
         empty_grad = torch.empty_like(grad)
@@ -82,7 +80,7 @@ def grad_handle(self, p, grad):
                 self.overflow_counter += region.has_inf_or_nan
                 master_stream = torch.cuda.current_stream()
                 with torch.cuda.stream(self.grad_offload_stream):
-                    GlobalRuntimeInfo.d2h_stream.wait_stream(master_stream)
+                    GlobalRuntimeInfo().d2h_stream.wait_stream(master_stream)
                     region.move_grad_to_cpu()
         return empty_grad
 

colossalai/auto_parallel/offload/mem_optimize.py

@@ -1,4 +1,5 @@
 from typing import Dict
+
 import torch
 import torch.fx
 from torch.fx import GraphModule
@@ -7,10 +8,11 @@
 from colossalai.fx import ColoTracer, is_compatible_with_meta
 from colossalai.fx.passes.meta_info_prop import MetaInfoProp
 
-from .region_manager import RegionManager
-from .runtime import runtime_syn_offload_apply_pass, runtime_asyn_offload_apply_pass
 from .base_offload_module import BaseOffloadModule
-from .util import compute_max_param_mem, compute_total_param_mem, compute_act_peak_mem, GlobalRuntimeInfo
+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],
@@ -29,13 +31,14 @@ def memory_optimize(model: torch.nn.Module,
 
     region_manager = RegionManager(graph, solver_name=solver_name, memory_budget=memory_budget)
     region_manager._build_regions()
-    GlobalRuntimeInfo.region_list = region_manager.region_list
+    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
+    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}")
+        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)
@@ -45,5 +48,5 @@ def memory_optimize(model: torch.nn.Module,
         raise TypeError(f"Unknown solver name {solver_name}!")
 
     gm.recompile()
-    optimized_model = BaseOffloadModule(gm, region_manager, solver_name=='syn')
+    optimized_model = BaseOffloadModule(gm, region_manager, solver_name == 'syn')
     return optimized_model

colossalai/auto_parallel/offload/runtime.py

@@ -1,4 +1,5 @@
 from typing import List
+
 import torch
 from torch.fx.node import Node
 
@@ -23,13 +24,13 @@ def forward(ctx, input_, fwd_info, bwd_info):
         ctx.bwd_info = bwd_info
         d2h_rid = fwd_info.get('d2h_rid', None)
         if d2h_rid is not None:
-            free_region = GlobalRuntimeInfo.region_list[d2h_rid]
+            free_region = GlobalRuntimeInfo().region_list[d2h_rid]
             assert isinstance(free_region, Region)
             free_region.free_cuda_data()
 
         h2d_rid = fwd_info.get('h2d_rid', None)
         if h2d_rid is not None:
-            h2d_region = GlobalRuntimeInfo.region_list[h2d_rid]
+            h2d_region = GlobalRuntimeInfo().region_list[h2d_rid]
             assert isinstance(h2d_region, Region)
             h2d_region.move_param_to_cuda()
 
@@ -40,7 +41,7 @@ def backward(ctx, grad_output):
 
         h2d_rid = ctx.bwd_info.get('h2d_rid', None)
         if h2d_rid is not None:
-            pref_region = GlobalRuntimeInfo.region_list[h2d_rid]
+            pref_region = GlobalRuntimeInfo().region_list[h2d_rid]
             assert isinstance(pref_region, Region)
             pref_region.move_param_to_cuda()
 
@@ -65,23 +66,22 @@ def forward(ctx, input_, fwd_info, bwd_info):
 
         sync_rid = fwd_info.get('sync_rid', None)
         if sync_rid is not None:
-            prefetch_event = GlobalRuntimeInfo.fwd_prefetch_event_map.get(
-                sync_rid, None)
+            prefetch_event = GlobalRuntimeInfo().fwd_prefetch_event_map.get(sync_rid, None)
             if prefetch_event:
                 prefetch_event.wait()
 
         h2d_rid = fwd_info.get('h2d_rid', None)
         if h2d_rid is not None:
-            pref_region = GlobalRuntimeInfo.region_list[h2d_rid]
+            pref_region = GlobalRuntimeInfo().region_list[h2d_rid]
             assert isinstance(pref_region, Region)
             master_stream = torch.cuda.current_stream()
-            with torch.cuda.stream(GlobalRuntimeInfo.h2d_stream):
-                GlobalRuntimeInfo.h2d_stream.wait_stream(master_stream)
+            with torch.cuda.stream(GlobalRuntimeInfo().h2d_stream):
+                GlobalRuntimeInfo().h2d_stream.wait_stream(master_stream)
                 pref_region.move_param_to_cuda()
 
             prefetch_event = torch.cuda.Event()
-            prefetch_event.record(GlobalRuntimeInfo.h2d_stream)
-            GlobalRuntimeInfo.fwd_prefetch_event_map[h2d_rid] = prefetch_event
+            prefetch_event.record(GlobalRuntimeInfo().h2d_stream)
+            GlobalRuntimeInfo().fwd_prefetch_event_map[h2d_rid] = prefetch_event
 
         return input_
 
@@ -90,27 +90,26 @@ def backward(ctx, grad_output):
 
         sync_rid = ctx.bwd_info.get('sync_rid', None)
         if sync_rid is not None:
-            wait_region = GlobalRuntimeInfo.region_list[sync_rid]
+            wait_region = GlobalRuntimeInfo().region_list[sync_rid]
             assert isinstance(wait_region, Region)
-            prefetch_event = GlobalRuntimeInfo.bwd_prefetch_event_map.get(
-                sync_rid, None)
+            prefetch_event = GlobalRuntimeInfo().bwd_prefetch_event_map.get(sync_rid, None)
             if prefetch_event:
                 prefetch_event.wait()
             else:
                 wait_region.move_param_to_cuda()
 
         h2d_rid = ctx.bwd_info.get('h2d_rid', None)
         if h2d_rid is not None:
-            pref_region = GlobalRuntimeInfo.region_list[h2d_rid]
+            pref_region = GlobalRuntimeInfo().region_list[h2d_rid]
             assert isinstance(pref_region, Region)
             master_stream = torch.cuda.current_stream()
-            with torch.cuda.stream(GlobalRuntimeInfo.h2d_stream):
-                GlobalRuntimeInfo.h2d_stream.wait_stream(master_stream)
+            with torch.cuda.stream(GlobalRuntimeInfo().h2d_stream):
+                GlobalRuntimeInfo().h2d_stream.wait_stream(master_stream)
                 pref_region.move_param_to_cuda()
 
             prefetch_event = torch.cuda.Event()
-            prefetch_event.record(GlobalRuntimeInfo.h2d_stream)
-            GlobalRuntimeInfo.bwd_prefetch_event_map[h2d_rid] = prefetch_event
+            prefetch_event.record(GlobalRuntimeInfo().h2d_stream)
+            GlobalRuntimeInfo().bwd_prefetch_event_map[h2d_rid] = prefetch_event
         return grad_output, None, None
 
 
@@ -129,6 +128,7 @@ def convert_fwd_upload_bwd_offload_to_action(tensor, fwd_info, bwd_info):
         ret = SynPreFwdPostBwdOP.apply(tensor, fwd_info, bwd_info)
     return ret
 
+
 def convert_fwd_prefetch_bwd_offload_to_action(tensor, fwd_info, bwd_info):
     '''
     Convert Prefetch and Offload operation into runtime action.
@@ -189,7 +189,8 @@ def runtime_syn_offload_apply_pass(gm: torch.fx.GraphModule, region_list: List[R
 
         if fwd_info or bwd_info:
             with mod_graph.inserting_after(last_inp_node):
-                new_node = mod_graph.create_node('call_function', convert_fwd_upload_bwd_offload_to_action,
+                new_node = mod_graph.create_node('call_function',
+                                                 convert_fwd_upload_bwd_offload_to_action,
                                                  args=(last_inp_node, fwd_info, bwd_info))
             replace_node_users(last_inp_node, new_node)
 
@@ -206,11 +207,11 @@ def runtime_asyn_offload_apply_pass(gm: torch.fx.GraphModule, region_list: List[
 
     # upload parameters of the first region
     last_inp_node = tuple(mod_graph.nodes)[0]
-    first_region_with_p = [
-        region for region in region_list if region.param_size][0]
+    first_region_with_p = [region for region in region_list if region.param_size][0]
     fwd_info = {"h2d_rid": first_region_with_p.r_id}
     with mod_graph.inserting_after(last_inp_node):
-        upload_apply_node = mod_graph.create_node('call_function', convert_fwd_upload_bwd_offload_to_action,
+        upload_apply_node = mod_graph.create_node('call_function',
+                                                  convert_fwd_upload_bwd_offload_to_action,
                                                   args=(last_inp_node, fwd_info, {}))
     replace_node_users(last_inp_node, upload_apply_node)
     last_inp_node = upload_apply_node
@@ -225,19 +226,20 @@ def runtime_asyn_offload_apply_pass(gm: torch.fx.GraphModule, region_list: List[
             fwd_info['h2d_rid'] = fwd_prefetch_region.r_id
 
         # forward offload
-        if r_idx > 0 and region_list[r_idx-1].need_offload:
+        if r_idx > 0 and region_list[r_idx - 1].need_offload:
             fwd_info['d2h_rid'] = r_idx - 1
 
         bwd_info = {}
         # backward prefetch
-        if r_idx > 0 and region_list[r_idx-1].need_offload:
+        if r_idx > 0 and region_list[r_idx - 1].need_offload:
             bwd_info['sync_rid'] = r_idx - 1
-        if r_idx > 0 and region_list[r_idx-1].bwd_prefetch_region:
-            bwd_info['h2d_rid'] = region_list[r_idx-1].bwd_prefetch_region.r_id
+        if r_idx > 0 and region_list[r_idx - 1].bwd_prefetch_region:
+            bwd_info['h2d_rid'] = region_list[r_idx - 1].bwd_prefetch_region.r_id
 
         if fwd_info or bwd_info:
             with mod_graph.inserting_after(last_inp_node):
-                new_node = mod_graph.create_node('call_function', convert_fwd_prefetch_bwd_offload_to_action,
+                new_node = mod_graph.create_node('call_function',
+                                                 convert_fwd_prefetch_bwd_offload_to_action,
                                                  args=(last_inp_node, fwd_info, bwd_info))
             replace_node_users(last_inp_node, new_node)
 
@@ -246,7 +248,8 @@ def runtime_asyn_offload_apply_pass(gm: torch.fx.GraphModule, region_list: List[
     if region.bwd_prefetch_region:
         bwd_info = {'h2d_rid': region.bwd_prefetch_region.r_id}
         with mod_graph.inserting_after(last_inp_node):
-            new_node = mod_graph.create_node('call_function', convert_fwd_prefetch_bwd_offload_to_action,
+            new_node = mod_graph.create_node('call_function',
+                                             convert_fwd_prefetch_bwd_offload_to_action,
                                              args=(last_inp_node, {}, bwd_info))
         replace_node_users(last_inp_node, new_node)
     # gm.graph.print_tabular()

colossalai/auto_parallel/offload/util.py

@@ -1,6 +1,9 @@
 from dataclasses import dataclass
 from typing import List
+
 import torch
+
+from colossalai.context.singleton_meta import SingletonMeta
 from colossalai.fx.profiler import calculate_fwd_out, calculate_fwd_tmp
 
 from .region import Region
@@ -12,6 +15,7 @@ class NodeInfo:
     runtime_fwd_mem: float = 0
     runtime_bwd_mem: float = 0
 
+
 class NvDevicePower:
     """
     NVIDIA GPU computing performance (TFLOPs).
@@ -30,12 +34,14 @@ class NvDevicePower:
     A100_FP32 = 19.5
 
 
-class GlobalRuntimeInfo:
-    h2d_stream = torch.cuda.Stream()
-    d2h_stream = torch.cuda.Stream()
-    fwd_prefetch_event_map = {}
-    bwd_prefetch_event_map = {}
-    region_list = []
+class GlobalRuntimeInfo(metaclass=SingletonMeta):
+
+    def __init__(self):
+        self.h2d_stream = torch.cuda.Stream()
+        self.d2h_stream = torch.cuda.Stream()
+        self.fwd_prefetch_event_map = {}
+        self.bwd_prefetch_event_map = {}
+        self.region_list = []
 
 
 def compute_act_peak_mem(region_list: List[Region]) -> float:
@@ -70,21 +76,24 @@ def compute_act_peak_mem(region_list: List[Region]) -> float:
 
     return act_peak_mem
 
+
 def compute_max_param_mem(region_list: List[Region]) -> float:
     return max(region.param_size for region in region_list)
 
+
 def compute_total_param_mem(region_list: List[Region]) -> float:
     return sum(region.param_size for region in region_list if region.r_id <= region.shared_rid)
 
+
 def requires_upload_p_in_fwd(shared_reg: Region):
-    return (shared_reg.r_id >= shared_reg.shared_rid) or (
-                shared_reg.r_id < shared_reg.shared_rid and shared_reg.need_offload)
+    return (shared_reg.r_id >= shared_reg.shared_rid) or (shared_reg.r_id < shared_reg.shared_rid
+                                                          and shared_reg.need_offload)
+
 
 def requires_release_p_in_bwd(shared_reg: Region):
-    return (shared_reg.r_id >= shared_reg.shared_rid) or (
-                shared_reg.r_id < shared_reg.shared_rid and shared_reg.need_offload)
+    return (shared_reg.r_id >= shared_reg.shared_rid) or (shared_reg.r_id < shared_reg.shared_rid
+                                                          and shared_reg.need_offload)
+
 
 def requires_offload_g_in_bwd(region: Region):
     return region.param_size and (region.r_id <= region.shared_rid)
-
-