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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2022/07/14 14:18:33 UTC
[GitHub] [tvm] yelite commented on a diff in pull request #11911: TVM Vertical Integration with PyTorch
yelite commented on code in PR #11911:
URL: https://github.com/apache/tvm/pull/11911#discussion_r921207358
##########
python/tvm/contrib/torch/as_torch.py:
##########
@@ -0,0 +1,156 @@
+# pylint: disable=inconsistent-return-statements
+#!/usr/bin/env python
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you 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.
+# pylint: disable=missing-module-docstring
+# pylint: disable=missing-class-docstring
+# pylint: disable=missing-function-docstring
+"""
+as_torch: a decorator, which is used to wrap the TVMscript code to `torch.nn.module`.
+"""
+import tempfile
+from typing import Callable, List, Union
+
+import torch
+import torch.utils.dlpack
+
+import tvm
+from tvm.meta_schedule import default_config
+from tvm.meta_schedule.database.database import TuningRecord
+from tvm.meta_schedule.extracted_task import ExtractedTask
+from tvm.meta_schedule.tune import TuneConfig, tune_extracted_tasks
+from tvm.target.target import Target
+from tvm.tir.schedule.schedule import Schedule
+
+
+# python wrapper for OperatorModule
+class OperatorModuleWrapper(torch.nn.Module):
+ def __init__(
+ self,
+ module: Union[
+ tvm.ir.module.IRModule,
+ tvm.tir.function.PrimFunc,
+ ],
+ ):
+ super().__init__()
+ self.rt_module = None # runtime module
+ self.ir_module = module # IR modules
+
+ def tune_tir_auto(self, mod: Union[tvm.ir.module.IRModule, tvm.tir.function.PrimFunc]):
+ with tempfile.TemporaryDirectory() as work_dir:
+ sch: Schedule = self.tune_tir_inner(
+ mod=mod,
+ target=Target("llvm --num-cores=16"),
+ work_dir=work_dir,
+ )
+ return sch.mod
+
+ def tune_tir_inner(
+ self,
+ mod: Union[tvm.ir.module.IRModule, tvm.tir.function.PrimFunc],
+ target: Union[str, Target],
+ work_dir: str,
+ ):
+ """Tune a TIR IRModule with a given target.
+
+ Parameters
+ ----------
+ mod : Union[IRModule, PrimFunc]
+ The module to tune.
+ target : Union[str, Target]
+ The target to tune for.
+ work_dir : Optional[str]
+ The working directory to save intermediate results.
+
+ Returns
+ -------
+ sch : Optional[Schedule]
+ The tuned schedule.
+ """
+ mod = default_config.mod(mod)
+ target = default_config.target(target)
+
+ extracted_task = ExtractedTask(
+ task_name="main",
+ mod=mod,
+ dispatched=[mod],
+ target=target,
+ weight=1,
+ )
+ config = TuneConfig(
+ # Default setting
+ strategy="replay_trace",
+ num_trials_per_iter=32,
+ max_trials_per_task=32,
+ max_trials_global=32,
+ )
+ database = tune_extracted_tasks(
+ extracted_tasks=[extracted_task], config=config, work_dir=work_dir
+ )
+ bests: List[TuningRecord] = database.get_top_k(database.commit_workload(mod), top_k=1)
+ if not bests:
+ return None
+ assert len(bests) == 1
+ sch = Schedule(mod)
+ bests[0].trace.apply_to_schedule(sch, remove_postproc=False)
+
+ return sch
+
+ def build(self, target=None):
+ tuned_module = self.tune_tir_auto(self.ir_module)
Review Comment:
Can you decouple the tuning from building runtime module? It's okay to lazily build the module as `forward` is called but I don't feel it's reasonable to do the same thing with tuning due to the high turnaround time (more than 1 minute).
##########
python/tvm/contrib/torch/as_torch.py:
##########
@@ -0,0 +1,156 @@
+# pylint: disable=inconsistent-return-statements
+#!/usr/bin/env python
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you 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.
+# pylint: disable=missing-module-docstring
+# pylint: disable=missing-class-docstring
+# pylint: disable=missing-function-docstring
+"""
+as_torch: a decorator, which is used to wrap the TVMscript code to `torch.nn.module`.
+"""
+import tempfile
+from typing import Callable, List, Union
+
+import torch
+import torch.utils.dlpack
+
+import tvm
+from tvm.meta_schedule import default_config
+from tvm.meta_schedule.database.database import TuningRecord
+from tvm.meta_schedule.extracted_task import ExtractedTask
+from tvm.meta_schedule.tune import TuneConfig, tune_extracted_tasks
+from tvm.target.target import Target
+from tvm.tir.schedule.schedule import Schedule
+
+
+# python wrapper for OperatorModule
+class OperatorModuleWrapper(torch.nn.Module):
+ def __init__(
+ self,
+ module: Union[
+ tvm.ir.module.IRModule,
+ tvm.tir.function.PrimFunc,
+ ],
+ ):
+ super().__init__()
+ self.rt_module = None # runtime module
+ self.ir_module = module # IR modules
+
+ def tune_tir_auto(self, mod: Union[tvm.ir.module.IRModule, tvm.tir.function.PrimFunc]):
+ with tempfile.TemporaryDirectory() as work_dir:
+ sch: Schedule = self.tune_tir_inner(
+ mod=mod,
+ target=Target("llvm --num-cores=16"),
Review Comment:
Should this be passed through function parameter?
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