[bugfix]fix bug of oneflow backend be stuck (#10435)

python/oneflow/framework/infer_compiler/__init__.py

@@ -14,43 +14,14 @@
 limitations under the License.
 """
 
-import os
-
-import oneflow as flow
-from oneflow.framework.args_tree import ArgsTree
+try:
+    import torch
+except ImportError:
+    print("You should install torch also when use `oneflow.framework.infer_compiler`.")
 
 from .transform.custom_transform import register
 from .utils.patch_for_compiler import *
 from .with_fx_graph import fx_node_tranform
 from .with_fx_interpreter import OneFlowInterpreter
 from .with_oneflow_compile import compile_from_torch
-
-
-def oneflow_backend(gm, example_inputs, *args, **kwargs):
-    with_interp = os.getenv(
-        "ONEDIFF_INFER_COMPILER_USE_INTERPRETER", "False"
-    ).lower() in ("true", "1", "t",)
-    if not with_interp:
-        transformed_fn = fx_node_tranform(gm)
-
-    def wrapped_forward(*args, **kwargs):
-        def input_fn(value):
-            if isinstance(value, torch.Tensor):
-                return flow.utils.tensor.from_torch(value.contiguous())
-            else:
-                return value
-
-        args_tree = ArgsTree((args, kwargs), False, tensor_type=torch.Tensor)
-        out = args_tree.map_leaf(input_fn)
-        args = out[0]
-        if with_interp:
-            output = OneFlowInterpreter(gm, garbage_collect_values=False).run(
-                *args, **kwargs
-            )
-        else:
-            output = transformed_fn(*args, **kwargs)
-        if isinstance(output, tuple):
-            return tuple(flow.utils.tensor.to_torch(i) for i in output)
-        return flow.utils.tensor.to_torch(output)
-
-    return wrapped_forward
+from .with_oneflow_backend import oneflow_backend

python/oneflow/framework/infer_compiler/with_fx_graph.py

@@ -46,7 +46,7 @@ def fx_node_tranform(gm):
         os.environ.setdefault("ONEFLOW_MLIR_FUSE_FORWARD_OPS", "1")
         os.environ.setdefault("ONEFLOW_MLIR_FUSE_OPS_WITH_BACKWARD_IMPL", "1")
         os.environ.setdefault("ONEFLOW_MLIR_GROUP_MATMUL", "1")
-        os.environ.setdefault("ONEFLOW_MLIR_PREFER_NHWC", "1")
+        os.environ.setdefault("ONEFLOW_MLIR_PREFER_NHWC", "0")
         os.environ.setdefault("ONEFLOW_KERNEL_ENABLE_FUSED_CONV_BIAS", "1")
         os.environ.setdefault("ONEFLOW_KERNEL_ENABLE_FUSED_LINEAR", "1")
         os.environ.setdefault(

python/oneflow/framework/infer_compiler/with_oneflow_backend.py

@@ -0,0 +1,53 @@
+"""
+Copyright 2020 The OneFlow Authors. 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 os
+import torch
+
+import oneflow as flow
+from oneflow.framework.args_tree import ArgsTree
+from .with_fx_graph import fx_node_tranform
+from .with_fx_interpreter import OneFlowInterpreter
+
+
+def oneflow_backend(gm, example_inputs, *args, **kwargs):
+    with_interp = os.getenv(
+        "ONEDIFF_INFER_COMPILER_USE_INTERPRETER", "False"
+    ).lower() in ("true", "1", "t",)
+    if not with_interp:
+        transformed_fn = fx_node_tranform(gm)
+
+    def wrapped_forward(*args, **kwargs):
+        def input_fn(value):
+            if isinstance(value, torch.Tensor):
+                return flow.utils.tensor.from_torch(value.contiguous())
+            else:
+                return value
+
+        args_tree = ArgsTree((args, kwargs), False, tensor_type=torch.Tensor)
+        out = args_tree.map_leaf(input_fn)
+        args = out[0]
+        if with_interp:
+            output = OneFlowInterpreter(gm, garbage_collect_values=False).run(
+                *args, **kwargs
+            )
+        else:
+            output = transformed_fn(*args, **kwargs)
+        if isinstance(output, tuple):
+            return tuple(flow.utils.tensor.to_torch(i) for i in output)
+        return flow.utils.tensor.to_torch(output)
+
+    return wrapped_forward