[fx] fixed compatiblity issue with torch 1.10 (#1331)

colossalai/fx/passes/split_module.py

@@ -2,6 +2,7 @@ import torch
 from torch.fx.graph_module import GraphModule
 from typing import Callable, List, Dict, Any, Optional
 from torch.fx._compatibility import compatibility
+from packaging import version
 import inspect
 
 
@@ -233,6 +234,9 @@ def split_module(
     base_mod_attrs: Dict[str, torch.fx.graph_module.GraphModule] = {}
     for node in m.graph.nodes:
         if node.op == 'placeholder':
+            if version.parse(torch.__version__) < version.parse('1.11.0'):
+                base_mod_env[node.name] = base_mod_graph.placeholder(node.name, type_expr=node.type)
+            else:
                 default_value = node.args[0] if len(node.args) > 0 else inspect.Signature.empty
                 base_mod_env[node.name] = base_mod_graph.placeholder(node.name,
                                                                      type_expr=node.type,

colossalai/fx/tracer/meta_patch/patched_function/arithmetic.py

@@ -3,6 +3,7 @@ from ..registry import meta_patched_function
 
 
 @meta_patched_function.register(torch.matmul)
+@meta_patched_function.register('matmul')    # for built-in op @
 def torch_matmul(input, other, *, out=None):
     # copied from huggingface.utils.fx
     d1 = input.dim()

colossalai/fx/tracer/tracer.py

@@ -96,6 +96,9 @@ class ColoTracer(Tracer):
                 # fetch patched function
                 if meta_patched_function.has(target):
                     meta_target = meta_patched_function.get(target)
+                elif meta_patched_function.has(target.__name__):
+                    # use name for some builtin op like @ (matmul)
+                    meta_target = meta_patched_function.get(target.__name__)
                 else:
                     meta_target = target
 

tests/test_fx/test_pipeline/test_timm_model/test_timm.py

 import torch
-import pytest
-try:
-    import timm.models as tm
-except:
-    pass
+import timm.models as tm
 from timm_utils import split_model_and_compare_output
 
 

tests/test_fx/test_pipeline/test_torchvision/test_torchvision.py

 import torch
-try:
-    import torchvision.models as tm
-except:
-    pass
+import torchvision
+import torchvision.models as tm
 from colossalai.fx import ColoTracer
 from colossalai.fx.passes.adding_split_node_pass import split_with_split_nodes_pass, balanced_split_pass
 from torch.fx import GraphModule
-
+from packaging import version
 import random
 import numpy as np
 import inspect
-import pytest
 
 MANUAL_SEED = 0
 random.seed(MANUAL_SEED)
@@ -22,9 +19,12 @@ torch.backends.cudnn.deterministic = True
 def test_torchvision_models():
     MODEL_LIST = [
         tm.vgg11, tm.resnet18, tm.densenet121, tm.mobilenet_v3_small, tm.resnext50_32x4d, tm.wide_resnet50_2,
-        tm.regnet_x_16gf, tm.vit_b_16, tm.convnext_small, tm.efficientnet_b0, tm.mnasnet0_5
+        tm.regnet_x_16gf, tm.efficientnet_b0, tm.mnasnet0_5
     ]
 
+    if version.parse(torchvision.__version__) >= version.parse('0.12.0'):
+        MODEL_LIST.extend([tm.vit_b_16, tm.convnext_small])
+
     tracer = ColoTracer()
     data = torch.rand(2, 3, 224, 224)
 

tests/test_fx/test_tracer/test_timm_model/test_timm_model.py

 import torch
-import pytest
-try:
-    import timm.models as tm
-except:
-    pass
+import timm.models as tm
 from colossalai.fx import ColoTracer
 from torch.fx import GraphModule
 

tests/test_fx/test_tracer/test_torchvision_model/test_torchvision_model.py

 import torch
-import pytest
-try:
-    import torchvision.models as tm
-except:
-    pass
+import torchvision
+import torchvision.models as tm
+from packaging import version
 from colossalai.fx import ColoTracer
 from torch.fx import GraphModule
 
@@ -11,16 +9,22 @@ from torch.fx import GraphModule
 def test_torchvision_models():
     MODEL_LIST = [
         tm.vgg11, tm.resnet18, tm.densenet121, tm.mobilenet_v3_small, tm.resnext50_32x4d, tm.wide_resnet50_2,
-        tm.regnet_x_16gf, tm.vit_b_16, tm.convnext_small, tm.mnasnet0_5, tm.efficientnet_b0
+        tm.regnet_x_16gf, tm.mnasnet0_5, tm.efficientnet_b0
     ]
 
+    RANDOMIZED_MODELS = [tm.efficientnet_b0]
+
+    if version.parse(torchvision.__version__) >= version.parse('0.12.0'):
+        MODEL_LIST.extend([tm.vit_b_16, tm.convnext_small])
+        RANDOMIZED_MODELS.append(tm.convnext_small)
+
     torch.backends.cudnn.deterministic = True
 
     tracer = ColoTracer()
     data = torch.rand(2, 3, 224, 224)
 
     for model_cls in MODEL_LIST:
-        if model_cls in [tm.convnext_small, tm.efficientnet_b0]:
+        if model_cls in RANDOMIZED_MODELS:
             # remove the impact of randomicity
             model = model_cls(stochastic_depth_prob=0)
         else: