areusch commented on code in PR #13051:
URL: https://github.com/apache/tvm/pull/13051#discussion_r998244813
##########
python/tvm/topi/utils.py:
##########
@@ -431,13 +431,13 @@ def get_shape(src_shape, src_layout, dst_layout):
return get_const_tuple(tuple([src_shape[i.value] for i in dst_indices]))
-def change_constant_shape(src, src_layout, dst_layout):
- """Makes a copy of a Relay constant, reshaping it to a new data layout.
+def change_ndarray_layout(arr, src_layout, dst_layout):
Review Comment:
should this still live in prod if it's just a test util?
##########
tests/python/relay/strategy/arm_cpu/test_group_conv2d.py:
##########
@@ -14,121 +14,44 @@
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
-import numpy as np
-import tvm
-import tvm.testing
-from tvm import relay
-from tvm.testing.aot import AOTTestModel, compile_and_run, generate_ref_data
-from tvm.micro.testing.aot_test_utils import AOT_CORSTONE300_RUNNER
+"""Tests for arm_cpu schedules for grouped conv2d."""
+from test_generalized_conv2d import GeneralizedConv2dTests
+from tvm.testing import main, parameter, parameters
-class BasicGroupConv2dTests:
- @tvm.testing.requires_corstone300
- def test_conv2d(
- self,
- data_shape,
- data_layout,
- kernel_size,
- kernel_layout,
- num_filter,
- strides,
- padding,
- dilation,
- groups,
- dtype,
- schedule_name,
- ):
- """Test a subgraph with a single conv2d operator."""
- ishape = data_shape
- assert groups > 1, f"groups should be more than 1 to create a group
conv2d."
+class GroupConv2dTests(GeneralizedConv2dTests):
+ """Helper for constructing group Conv2ds. Sets the kernel layout to what
x86 code supports."""
Review Comment:
nit: sets the reference kernel layout...
##########
tests/python/relay/strategy/arm_cpu/test_generalized_conv2d.py:
##########
@@ -0,0 +1,122 @@
+# 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.
+"""Helper class for testing variations of 2D convolution. Should be used by
subclassing
+`GeneralizedConv2dTests`, and then setting the arguments using
tvm.testing.parameter(s)."""
+
+from numpy.random import randint
+import tvm
+import tvm.testing
+from tvm import relay
+from tvm.testing.aot import AOTTestModel, compile_and_run, generate_ref_data
+from tvm.micro.testing.aot_test_utils import AOT_CORSTONE300_RUNNER
+from tvm.topi.utils import change_ndarray_layout
+
+
+class GeneralizedConv2dTests:
+ """Superclass which can be used to test regular, depthwise, or grouped
conv2D. Cannot be used
+ for 5D data formats (NCHWc and such) as written, but could be extended.
Might also be worth
+ abstracting some of this logic into an even more general class that could
be used for other
+ operators.
+
+ Note that data_shape should always be a tuple of length four indicating
the data shape in NHWC
+ format (it will later be reshaped according to the given data_layout), and
kernel_size should be
+ a length two tuple giving the height and width of the kernel."""
+
+ @tvm.testing.requires_corstone300
+ def test_conv2d(
+ self,
+ data_shape,
+ kernel_size,
+ num_filter,
+ in_dtype,
+ strides,
+ padding,
+ groups,
+ dilation,
+ data_layout,
+ kernel_layout,
+ out_layout,
+ schedule_name,
+ ):
+ """Test a subgraph with a single conv2d operator."""
+
+ ref_input_data = randint(low=-128, high=127, size=data_shape,
dtype=in_dtype)
+ ref_input_var = relay.var("input", relay.TensorType(data_shape,
in_dtype)) # NHWC layout
+ kernel_shape = (*kernel_size, data_shape[-1] // groups, num_filter) #
HWIO layout
+ ref_kernel_data = randint(low=-10, high=10, size=kernel_shape,
dtype=in_dtype)
+
+ # Our x86 depthwise implementation only supports HWOI with NHWC, so we
need to change our
+ # kernel layout to work around this. We can't just change the whole
thing to HWIO or
+ # something else, as then group conv2d would not work. Eventually, we
should switch to using
+ # TensorFlow to create the reference output so we can ensure our
implementation is right.
Review Comment:
can you file a bug for this and link to it from this comment?
##########
tests/python/relay/strategy/arm_cpu/test_generalized_conv2d.py:
##########
@@ -0,0 +1,122 @@
+# 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.
+"""Helper class for testing variations of 2D convolution. Should be used by
subclassing
+`GeneralizedConv2dTests`, and then setting the arguments using
tvm.testing.parameter(s)."""
+
+from numpy.random import randint
+import tvm
+import tvm.testing
+from tvm import relay
+from tvm.testing.aot import AOTTestModel, compile_and_run, generate_ref_data
+from tvm.micro.testing.aot_test_utils import AOT_CORSTONE300_RUNNER
+from tvm.topi.utils import change_ndarray_layout
+
+
+class GeneralizedConv2dTests:
+ """Superclass which can be used to test regular, depthwise, or grouped
conv2D. Cannot be used
+ for 5D data formats (NCHWc and such) as written, but could be extended.
Might also be worth
+ abstracting some of this logic into an even more general class that could
be used for other
+ operators.
+
+ Note that data_shape should always be a tuple of length four indicating
the data shape in NHWC
+ format (it will later be reshaped according to the given data_layout), and
kernel_size should be
+ a length two tuple giving the height and width of the kernel."""
+
+ @tvm.testing.requires_corstone300
+ def test_conv2d(
Review Comment:
is this case prevented from being run because it takes undefined parameters?
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