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[GitHub] [tvm] zxybazh opened a new pull request #10079: [MetaSchedule][M4a] Add Tuning Scripts for TIR / TE / Relay
zxybazh opened a new pull request #10079:
URL: https://github.com/apache/tvm/pull/10079
This PR is part of the stage M4a of the meta schedule project (#8473).
The architecture is re-designed by Junru and Xiyou. In this PR we introduced the tuning scripts for tir, te & relay workloads with meta schedule.
Thanks to all co-authors for contributing!
Co-authored-by: Junru Shao <<j...@gmail.com>>
Co-authored-by: Bohan Hou <<3...@users.noreply.github.com>>
Co-authored-by: Ruihang Lai <<l...@qq.com>>
Co-authored-by: Hongyi Jin <<3...@qq.com>>
Co-authored-by: Wuwei Lin <<w...@apache.org>>
Co-authored-by: Siyuan Feng <<H...@sjtu.edu.cn>>
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[GitHub] [tvm] junrushao1994 commented on pull request #10079: [MetaSchedule][M4a] Add Tuning Scripts for TIR / TE / Relay
Posted by GitBox <gi...@apache.org>.
junrushao1994 commented on pull request #10079:
URL: https://github.com/apache/tvm/pull/10079#issuecomment-1022764860
Let's wait for the rest 3 rules to be upstreamed before merging this PR :-)
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[GitHub] [tvm] junrushao1994 commented on a change in pull request #10079: [MetaSchedule][M4a] User-API: Tune-TE/TIR/Relay
Posted by GitBox <gi...@apache.org>.
junrushao1994 commented on a change in pull request #10079:
URL: https://github.com/apache/tvm/pull/10079#discussion_r795100613
##########
File path: python/tvm/meta_schedule/testing/relay_workload.py
##########
@@ -15,13 +15,230 @@
# specific language governing permissions and limitations
# under the License.
"""Workloads in Relay IR"""
+from enum import Enum
from typing import Dict, Tuple
-import tvm.relay.testing # pylint: disable=unused-import
from tvm import relay
from tvm.ir import IRModule
from tvm.runtime import NDArray
+# Model types supported in Torchvision
+class MODEL_TYPE(Enum): # pylint: disable=invalid-name
Review comment:
Looks great!
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[GitHub] [tvm] zxybazh commented on a change in pull request #10079: [MetaSchedule][M4a] User-API: Tune-TE/TIR/Relay
Posted by GitBox <gi...@apache.org>.
zxybazh commented on a change in pull request #10079:
URL: https://github.com/apache/tvm/pull/10079#discussion_r796106818
##########
File path: tests/python/unittest/test_meta_schedule_tune_relay.py
##########
@@ -0,0 +1,151 @@
+# 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-docstring
+import logging
+import tempfile
+import pytest
+import numpy as np
+from typing import Tuple, List
+
+import tvm
+from tvm import relay
+from tvm.ir import IRModule
+from tvm.runtime.ndarray import cpu, cuda
+from tvm.target.target import Target
+from tvm.contrib import graph_executor
+from tvm.meta_schedule import ReplayTraceConfig
+from tvm.meta_schedule.database import PyDatabase, Workload, TuningRecord
+from tvm.meta_schedule.testing import MODEL_TYPE, MODEL_TYPES, get_torch_model
+from tvm.meta_schedule.tune import tune_relay
+
+logging.basicConfig()
+logging.getLogger("tvm.meta_schedule").setLevel(logging.DEBUG)
+
+
+class DummyDatabase(PyDatabase):
+ def __init__(self):
+ super().__init__()
+ self.records = []
+ self.workload_reg = []
+
+ def has_workload(self, mod: IRModule) -> Workload:
+ for workload in self.workload_reg:
+ if tvm.ir.structural_equal(workload.mod, mod):
+ return True
+ return False
+
+ def commit_tuning_record(self, record: TuningRecord) -> None:
+ self.records.append(record)
+
+ def commit_workload(self, mod: IRModule) -> Workload:
+ for workload in self.workload_reg:
+ if tvm.ir.structural_equal(workload.mod, mod):
+ return workload
+ workload = Workload(mod)
+ self.workload_reg.append(workload)
+ return workload
+
+ def get_top_k(self, workload: Workload, top_k: int) -> List[TuningRecord]:
+ return list(
+ filter(
+ lambda x: x.workload == workload,
+ sorted(self.records, key=lambda x: sum(x.run_secs) / len(x.run_secs)),
+ )
+ )[: int(top_k)]
+
+ def __len__(self) -> int:
+ return len(self.records)
+
+ def print_results(self) -> None:
+ print("\n".join([str(r) for r in self.records]))
+
+
+@pytest.mark.skip("Integration test")
+@pytest.mark.parametrize("model_name", ["resnet18", "mobilenet_v2", "bert_base"])
+@pytest.mark.parametrize("batch_size", [1])
+@pytest.mark.parametrize("target", ["llvm --num-cores=16", "nvidia/geforce-rtx-3070"])
+def test_meta_schedule_tune_relay(model_name: str, batch_size: int, target: str):
+ if model_name == "inception_v3" and batch_size == 1:
+ pytest.skip("inception_v3 does not handle batch_size of 1")
+
+ input_shape: Tuple[int, ...]
+ input_name = "input0"
+ dev = tvm.cpu() if str(target).startswith("llvm") else cuda()
+ if MODEL_TYPES[model_name] == MODEL_TYPE.TEXT_CLASSIFICATION:
+ seq_length = 128
+ input_name = "input_ids"
+ input_shape = (batch_size, seq_length)
+ data = tvm.nd.array(np.random.randint(0, 30521, size=input_shape), dev) # embedding size
+ else:
+ if MODEL_TYPES[model_name] == MODEL_TYPE.IMAGE_CLASSIFICATION:
+ input_shape = (batch_size, 3, 299, 299)
+ elif MODEL_TYPES[model_name] == MODEL_TYPE.SEGMENTATION:
+ input_shape = (batch_size, 3, 299, 299)
+ elif MODEL_TYPES[model_name] == MODEL_TYPE.OBJECT_DETECTION:
+ input_shape = (1, 3, 300, 300)
+ elif MODEL_TYPES[model_name] == MODEL_TYPE.VIDEO_CLASSIFICATION:
+ input_shape = (batch_size, 3, 3, 299, 299)
+ else:
+ raise ValueError("Unsupported model: " + model_name)
+ data = tvm.nd.array(np.random.randn(*input_shape).astype("float32"), dev)
+
+ output_shape: Tuple[int, int] = (batch_size, 1000)
+
+ mod, params = get_torch_model(
+ model_name=model_name,
+ input_shape=input_shape,
+ output_shape=output_shape,
+ dtype="float32",
+ )
+
+ with tempfile.TemporaryDirectory() as work_dir:
+ target = Target(target)
+ database = DummyDatabase()
+ rt_mod: tvm.module = tune_relay(
+ mod=mod,
+ params=params,
+ target=target,
+ config=ReplayTraceConfig(
+ num_trials_per_iter=32,
+ num_trials_total=32,
+ ),
+ work_dir=work_dir,
+ database=database,
+ )
+ # Compile without meta-scheduler for correctness check
+ with tvm.transform.PassContext(opt_level=0):
+ rt_mod2 = relay.build(mod, target=target, params=params)
+
+ def get_output(data, lib):
+ module = graph_executor.GraphModule(lib["default"](dev))
+ module.set_input(input_name, data)
+ module.run()
+ return module.get_output(0).numpy()
+
+ # Check correctness
+ actual_output = get_output(data, rt_mod)
+ expected_output = get_output(data, rt_mod2)
+ assert np.allclose(actual_output, expected_output, rtol=1e-4, atol=2e-4)
+
+
+if __name__ == """__main__""":
+ test_meta_schedule_tune_relay("resnet18", 1, "llvm --num-cores=16")
+ test_meta_schedule_tune_relay("resnet18", 1, "nvidia/geforce-rtx-3070")
+ test_meta_schedule_tune_relay("mobilenet_v2", 1, "llvm --num-cores=16")
+ test_meta_schedule_tune_relay("mobilenet_v2", 1, "nvidia/geforce-rtx-3070")
+ test_meta_schedule_tune_relay("bert_base", 1, "llvm --num-cores=16")
+ test_meta_schedule_tune_relay("bert_base", 1, "nvidia/geforce-rtx-3070")
Review comment:
Around 30 minutes.
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[GitHub] [tvm] zxybazh commented on a change in pull request #10079: [MetaSchedule][M4a] User-API: Tune-TE/TIR/Relay
Posted by GitBox <gi...@apache.org>.
zxybazh commented on a change in pull request #10079:
URL: https://github.com/apache/tvm/pull/10079#discussion_r794120941
##########
File path: python/tvm/meta_schedule/testing/relay_workload.py
##########
@@ -15,13 +15,230 @@
# specific language governing permissions and limitations
# under the License.
"""Workloads in Relay IR"""
+from enum import Enum
from typing import Dict, Tuple
-import tvm.relay.testing # pylint: disable=unused-import
from tvm import relay
from tvm.ir import IRModule
from tvm.runtime import NDArray
+# Model types supported in Torchvision
+class MODEL_TYPE(Enum): # pylint: disable=invalid-name
Review comment:
Hey, I simplied most of the script, does the script look good to you?
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[GitHub] [tvm] junrushao1994 commented on a change in pull request #10079: [MetaSchedule][M4a] User-API: Tune-TE/TIR/Relay
Posted by GitBox <gi...@apache.org>.
junrushao1994 commented on a change in pull request #10079:
URL: https://github.com/apache/tvm/pull/10079#discussion_r795100767
##########
File path: tests/python/unittest/test_meta_schedule_tune_relay.py
##########
@@ -0,0 +1,151 @@
+# 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-docstring
+import logging
+import tempfile
+import pytest
+import numpy as np
+from typing import Tuple, List
+
+import tvm
+from tvm import relay
+from tvm.ir import IRModule
+from tvm.runtime.ndarray import cpu, cuda
+from tvm.target.target import Target
+from tvm.contrib import graph_executor
+from tvm.meta_schedule import ReplayTraceConfig
+from tvm.meta_schedule.database import PyDatabase, Workload, TuningRecord
+from tvm.meta_schedule.testing import MODEL_TYPE, MODEL_TYPES, get_torch_model
+from tvm.meta_schedule.tune import tune_relay
+
+logging.basicConfig()
+logging.getLogger("tvm.meta_schedule").setLevel(logging.DEBUG)
+
+
+class DummyDatabase(PyDatabase):
+ def __init__(self):
+ super().__init__()
+ self.records = []
+ self.workload_reg = []
+
+ def has_workload(self, mod: IRModule) -> Workload:
+ for workload in self.workload_reg:
+ if tvm.ir.structural_equal(workload.mod, mod):
+ return True
+ return False
+
+ def commit_tuning_record(self, record: TuningRecord) -> None:
+ self.records.append(record)
+
+ def commit_workload(self, mod: IRModule) -> Workload:
+ for workload in self.workload_reg:
+ if tvm.ir.structural_equal(workload.mod, mod):
+ return workload
+ workload = Workload(mod)
+ self.workload_reg.append(workload)
+ return workload
+
+ def get_top_k(self, workload: Workload, top_k: int) -> List[TuningRecord]:
+ return list(
+ filter(
+ lambda x: x.workload == workload,
+ sorted(self.records, key=lambda x: sum(x.run_secs) / len(x.run_secs)),
+ )
+ )[: int(top_k)]
+
+ def __len__(self) -> int:
+ return len(self.records)
+
+ def print_results(self) -> None:
+ print("\n".join([str(r) for r in self.records]))
+
+
+@pytest.mark.skip("Integration test")
+@pytest.mark.parametrize("model_name", ["resnet18", "mobilenet_v2", "bert_base"])
+@pytest.mark.parametrize("batch_size", [1])
+@pytest.mark.parametrize("target", ["llvm --num-cores=16", "nvidia/geforce-rtx-3070"])
+def test_meta_schedule_tune_relay(model_name: str, batch_size: int, target: str):
+ if model_name == "inception_v3" and batch_size == 1:
+ pytest.skip("inception_v3 does not handle batch_size of 1")
+
+ input_shape: Tuple[int, ...]
+ input_name = "input0"
+ dev = tvm.cpu() if str(target).startswith("llvm") else cuda()
+ if MODEL_TYPES[model_name] == MODEL_TYPE.TEXT_CLASSIFICATION:
+ seq_length = 128
+ input_name = "input_ids"
+ input_shape = (batch_size, seq_length)
+ data = tvm.nd.array(np.random.randint(0, 30521, size=input_shape), dev) # embedding size
+ else:
+ if MODEL_TYPES[model_name] == MODEL_TYPE.IMAGE_CLASSIFICATION:
+ input_shape = (batch_size, 3, 299, 299)
+ elif MODEL_TYPES[model_name] == MODEL_TYPE.SEGMENTATION:
+ input_shape = (batch_size, 3, 299, 299)
+ elif MODEL_TYPES[model_name] == MODEL_TYPE.OBJECT_DETECTION:
+ input_shape = (1, 3, 300, 300)
+ elif MODEL_TYPES[model_name] == MODEL_TYPE.VIDEO_CLASSIFICATION:
+ input_shape = (batch_size, 3, 3, 299, 299)
+ else:
+ raise ValueError("Unsupported model: " + model_name)
+ data = tvm.nd.array(np.random.randn(*input_shape).astype("float32"), dev)
+
+ output_shape: Tuple[int, int] = (batch_size, 1000)
+
+ mod, params = get_torch_model(
+ model_name=model_name,
+ input_shape=input_shape,
+ output_shape=output_shape,
+ dtype="float32",
+ )
+
+ with tempfile.TemporaryDirectory() as work_dir:
+ target = Target(target)
+ database = DummyDatabase()
+ rt_mod: tvm.module = tune_relay(
+ mod=mod,
+ params=params,
+ target=target,
+ config=ReplayTraceConfig(
+ num_trials_per_iter=32,
+ num_trials_total=32,
+ ),
+ work_dir=work_dir,
+ database=database,
+ )
+ # Compile without meta-scheduler for correctness check
+ with tvm.transform.PassContext(opt_level=0):
+ rt_mod2 = relay.build(mod, target=target, params=params)
+
+ def get_output(data, lib):
+ module = graph_executor.GraphModule(lib["default"](dev))
+ module.set_input(input_name, data)
+ module.run()
+ return module.get_output(0).numpy()
+
+ # Check correctness
+ actual_output = get_output(data, rt_mod)
+ expected_output = get_output(data, rt_mod2)
+ assert np.allclose(actual_output, expected_output, rtol=1e-4, atol=2e-4)
+
+
+if __name__ == """__main__""":
+ test_meta_schedule_tune_relay("resnet18", 1, "llvm --num-cores=16")
+ test_meta_schedule_tune_relay("resnet18", 1, "nvidia/geforce-rtx-3070")
+ test_meta_schedule_tune_relay("mobilenet_v2", 1, "llvm --num-cores=16")
+ test_meta_schedule_tune_relay("mobilenet_v2", 1, "nvidia/geforce-rtx-3070")
+ test_meta_schedule_tune_relay("bert_base", 1, "llvm --num-cores=16")
+ test_meta_schedule_tune_relay("bert_base", 1, "nvidia/geforce-rtx-3070")
Review comment:
Wonder how long would these 6 unittests take in total?
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[GitHub] [tvm] junrushao1994 commented on a change in pull request #10079: [MetaSchedule][M4a] User-API: Tune-TE/TIR/Relay
Posted by GitBox <gi...@apache.org>.
junrushao1994 commented on a change in pull request #10079:
URL: https://github.com/apache/tvm/pull/10079#discussion_r793188970
##########
File path: python/tvm/meta_schedule/testing/relay_workload.py
##########
@@ -15,13 +15,230 @@
# specific language governing permissions and limitations
# under the License.
"""Workloads in Relay IR"""
+from enum import Enum
from typing import Dict, Tuple
-import tvm.relay.testing # pylint: disable=unused-import
from tvm import relay
from tvm.ir import IRModule
from tvm.runtime import NDArray
+# Model types supported in Torchvision
+class MODEL_TYPE(Enum): # pylint: disable=invalid-name
Review comment:
I don't think we should define this huge chunk of models on mainline, at least not for now :-) Let's find alternatives
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[GitHub] [tvm] zxybazh commented on a change in pull request #10079: [MetaSchedule][M4a] User-API: Tune-TE/TIR/Relay
Posted by GitBox <gi...@apache.org>.
zxybazh commented on a change in pull request #10079:
URL: https://github.com/apache/tvm/pull/10079#discussion_r819044834
##########
File path: src/meta_schedule/task_scheduler/task_scheduler.cc
##########
@@ -124,7 +124,7 @@ void TaskSchedulerNode::Tune() {
int running_tasks = tasks.size();
for (int task_id; (task_id = NextTaskId()) != -1;) {
- LOG(INFO) << "Scheduler picks Task #" << task_id << ": " << tasks[task_id]->task_name;
+ LOG(INFO) << "Scheduler picks Task #" << task_id + 1 << ": " << tasks[task_id]->task_name;
Review comment:
Hi, thanks for pointing that out. The reason for this change is previously we have `task 0/18` to `task 17/18` which seems confusing to me. I intend to make sure the log is consistent but I might missed a couple places. After second thoughts I think it might be better to keep the `task_id` the same way it is so that it would be easier for debugging later.
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[GitHub] [tvm] zxybazh commented on a change in pull request #10079: [MetaSchedule][M4a] User-API: Tune-TE/TIR/Relay
Posted by GitBox <gi...@apache.org>.
zxybazh commented on a change in pull request #10079:
URL: https://github.com/apache/tvm/pull/10079#discussion_r819044834
##########
File path: src/meta_schedule/task_scheduler/task_scheduler.cc
##########
@@ -124,7 +124,7 @@ void TaskSchedulerNode::Tune() {
int running_tasks = tasks.size();
for (int task_id; (task_id = NextTaskId()) != -1;) {
- LOG(INFO) << "Scheduler picks Task #" << task_id << ": " << tasks[task_id]->task_name;
+ LOG(INFO) << "Scheduler picks Task #" << task_id + 1 << ": " << tasks[task_id]->task_name;
Review comment:
Hi, thanks for pointing that out. The reason for this change is previously we have `task 0/18` to `task 17/18` which seems confusing to me. I intend to make sure the log is consistent but I might missed a couple places. After second thoughts I think it might be better to keep the `task_id` the same way it is so that it would be easier for debugging later. See #10478.
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[GitHub] [tvm] MasterJH5574 commented on a change in pull request #10079: [MetaSchedule][M4a] User-API: Tune-TE/TIR/Relay
Posted by GitBox <gi...@apache.org>.
MasterJH5574 commented on a change in pull request #10079:
URL: https://github.com/apache/tvm/pull/10079#discussion_r818673222
##########
File path: src/meta_schedule/task_scheduler/task_scheduler.cc
##########
@@ -124,7 +124,7 @@ void TaskSchedulerNode::Tune() {
int running_tasks = tasks.size();
for (int task_id; (task_id = NextTaskId()) != -1;) {
- LOG(INFO) << "Scheduler picks Task #" << task_id << ": " << tasks[task_id]->task_name;
+ LOG(INFO) << "Scheduler picks Task #" << task_id + 1 << ": " << tasks[task_id]->task_name;
Review comment:
Hi Xiyou! I'm curious about this change. After this change the printed ids at several places differ (here with `+ 1` while others without.)
https://github.com/apache/tvm/blob/0c836b73ffd9669bcc416515dce6436cbd7d7ebe/src/meta_schedule/task_scheduler/task_scheduler.cc#L96
https://github.com/apache/tvm/blob/0c836b73ffd9669bcc416515dce6436cbd7d7ebe/src/meta_schedule/measure_callback/echo_statistics.cc#L228
Perhaps it's better to keep them consistent :eyes:?
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[GitHub] [tvm] Hzfengsy merged pull request #10079: [MetaSchedule][M4a] User-API: Tune-TE/TIR/Relay
Posted by GitBox <gi...@apache.org>.
Hzfengsy merged pull request #10079:
URL: https://github.com/apache/tvm/pull/10079
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[GitHub] [tvm] zxybazh commented on a change in pull request #10079: [MetaSchedule][M4a] User-API: Tune-TE/TIR/Relay
Posted by GitBox <gi...@apache.org>.
zxybazh commented on a change in pull request #10079:
URL: https://github.com/apache/tvm/pull/10079#discussion_r819044834
##########
File path: src/meta_schedule/task_scheduler/task_scheduler.cc
##########
@@ -124,7 +124,7 @@ void TaskSchedulerNode::Tune() {
int running_tasks = tasks.size();
for (int task_id; (task_id = NextTaskId()) != -1;) {
- LOG(INFO) << "Scheduler picks Task #" << task_id << ": " << tasks[task_id]->task_name;
+ LOG(INFO) << "Scheduler picks Task #" << task_id + 1 << ": " << tasks[task_id]->task_name;
Review comment:
Hi, thanks for pointing that out. The reason for this change is previously we have `task 0/18` to `task 17/18` which seems confusing to me. I intend to make sure the log is consistent but I might missed a couple places. I'll make a thorough check to update that.
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