Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

[TVMC] Apply constant folding when converting layout #13216

Merged
merged 1 commit into from
Nov 2, 2022
Merged

[TVMC] Apply constant folding when converting layout #13216

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
1 change: 1 addition & 0 deletions python/tvm/driver/tvmc/transform.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -50,6 +50,7 @@ def convert_graph_layout(mod, desired_layout):
[
relay.transform.RemoveUnusedFunctions(),
relay.transform.ConvertLayout(desired_layouts),
relay.transform.FoldConstant(),
]
)

Expand Down
57 changes: 57 additions & 0 deletions tests/python/driver/tvmc/test_transform.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,57 @@
# 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.
import pytest
import numpy as np

import tvm
from tvm import relay
from tvm.driver.tvmc.transform import convert_graph_layout


def test_layout_transform():
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

This seems to be re-testing the existing passes? Instead we could just test that it's using them? I don't know if there's any off the shelf infrastructure to get a list of passes but we could do something like:

mock_sequence = MagicMock()
mock_pass_runner = MagicMock()
mock_sequence.return_value = mock_pass_runner
monkeypatch.setattr(transform, 'Sequential', mock_sequence);

convert_graph_layout(mod, "NHWC")

# Might need to mock out a bunch of passes here with some
mock_sequence.assert_called_once_with([
 relay.transform.RemoveUnusedFunctions(),
 relay.transform.ConvertLayout({
        "nn.conv2d": ["NHWC", "default"],
        "nn.conv2d_transpose": ["NHWC", "default"],
        "qnn.conv2d": ["NHWC", "default"],
 }),
 relay.transform.FoldConstant(),
])
mock_pass_runner.assert_called_once_with(mod);

Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

The test in the patch makes sense to me, it checks that the constant has been folded which wouldn't happen unless we have run the FoldConstant pass. I suppose mocking would work as well, but don't we then have the problem of having to keep the dict of ops and preferred layouts in the codebase manually in sync with the same dict in the test?

Copy link
Contributor Author

@lhutton1 lhutton1 Oct 28, 2022 •
edited
Loading

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

I think the issue with the current test is that it checks more than it really needs to and might become a maintenance burden going forwards. Ideally I'd like to make use of pass instruments to check that ConvertLayout and FoldConstant are run in the correct order, which means that we aren't tied to the implementation details of the passes. However, the use of PassContext here overwrites any context set by the user (and therefore by the test that adds the instrumentation) so I think we should remove this and set the context from the callee(s) - given the test works for now, could we do this in a follow up?

Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Yes, I agree, that testing the implementation details of the passes themselves in not ideal. I'd be fine with merging the current patch as it is and improving the test in a follow up, once we have cleared up the nested PassContexts if @Mousius agrees?

Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Agree, this will fail if we disable constant folding so that'll work for now 😸

"""
Test layout is correctly transformed and constant folding is applied.
"""
dtype = "int8"
iinfo = np.iinfo(dtype)
data_min = iinfo.min
data_max = iinfo.max

x = relay.var("x", shape=(1, 4, 2, 2), dtype=dtype)
weight = relay.const(
np.random.randint(data_min, data_max, size=(2, 4, 2, 2), dtype=dtype), dtype=dtype
)
x = relay.nn.conv2d(x, weight)
func = relay.Function(relay.analysis.free_vars(x), x)
mod = tvm.IRModule.from_expr(func)

desired_layout = "NHWC"
mod = convert_graph_layout(mod, desired_layout)

main_expr = mod["main"].body
conv = main_expr.args[0]
assert conv.op.name == "nn.conv2d"
assert conv.attrs["data_layout"] == "NHWC"
assert conv.attrs["kernel_layout"] == "HWIO"

# Ensure transform has been folded into the constant
weights = conv.args[1]
assert isinstance(weights, relay.expr.Constant)


if __name__ == "__main__":
tvm.testing.main()