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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2020/09/17 14:45:32 UTC
[GitHub] [incubator-tvm] lhutton1 commented on a change in pull request #6506: ACL integration: add maximum support for float32.
lhutton1 commented on a change in pull request #6506:
URL: https://github.com/apache/incubator-tvm/pull/6506#discussion_r490290547
##########
File path: tests/python/contrib/test_arm_compute_lib/test_maximum.py
##########
@@ -0,0 +1,104 @@
+# 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.
+"""Arm Compute Library integration reshape tests."""
+
+import numpy as np
+
+import tvm
+from tvm import relay
+
+from .infrastructure import (
+ skip_runtime_test,
+ skip_codegen_test,
+ build_and_run,
+ verify,
+ verify_codegen,
+)
+from .infrastructure import Device
+
+
+def _get_model(input_shape, dtype, var_names):
+ """Return a model and any parameters it may have."""
+ a = relay.var(next(var_names), shape=input_shape, dtype=dtype)
+ b = relay.var(next(var_names), shape=input_shape, dtype=dtype)
+ max = relay.maximum(a, b)
+ return max
+
+
+def _get_expected_codegen(shape, dtype):
+ node = {
+ "op": "kernel",
+ "name": "maximum",
+ "inputs": [[0, 0, 0], [1, 0, 0]],
+ "attrs": {
+ "num_inputs": "2",
+ "num_outputs": "1",
+ "shape": [[list(shape)]],
+ "dtype": [[dtype]],
+ },
+ }
+
+ inputs = [
+ {"op": "input", "name": "", "attrs": {"shape": [[list(shape)]], "dtype": [[dtype]]}},
+ {"op": "input", "name": "", "attrs": {"shape": [[list(shape)]], "dtype": [[dtype]]}},
+ ]
+ inputs.append(node)
+ return inputs
+
+
+def test_maximum():
+ Device.load("test_config.json")
+
+ if skip_runtime_test():
+ return
+
+ device = Device()
+ np.random.seed(0)
+
+ for dtype, low, high, atol, rtol in [
+ ("float32", -127, 128, 0.001, 0.001),
+ ("float32", -1, 1, 0.001, 0.001),
Review comment:
Curious: why check different data ranges here? is it a check for precision?
##########
File path: tests/python/contrib/test_arm_compute_lib/test_maximum.py
##########
@@ -0,0 +1,104 @@
+# 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.
+"""Arm Compute Library integration reshape tests."""
+
+import numpy as np
+
+import tvm
+from tvm import relay
+
+from .infrastructure import (
+ skip_runtime_test,
+ skip_codegen_test,
+ build_and_run,
+ verify,
+ verify_codegen,
+)
+from .infrastructure import Device
+
+
+def _get_model(input_shape, dtype, var_names):
+ """Return a model and any parameters it may have."""
+ a = relay.var(next(var_names), shape=input_shape, dtype=dtype)
+ b = relay.var(next(var_names), shape=input_shape, dtype=dtype)
+ max = relay.maximum(a, b)
+ return max
+
+
+def _get_expected_codegen(shape, dtype):
+ node = {
+ "op": "kernel",
+ "name": "maximum",
+ "inputs": [[0, 0, 0], [1, 0, 0]],
+ "attrs": {
+ "num_inputs": "2",
+ "num_outputs": "1",
+ "shape": [[list(shape)]],
+ "dtype": [[dtype]],
+ },
+ }
+
+ inputs = [
+ {"op": "input", "name": "", "attrs": {"shape": [[list(shape)]], "dtype": [[dtype]]}},
+ {"op": "input", "name": "", "attrs": {"shape": [[list(shape)]], "dtype": [[dtype]]}},
+ ]
+ inputs.append(node)
+ return inputs
+
+
+def test_maximum():
+ Device.load("test_config.json")
+
+ if skip_runtime_test():
+ return
+
+ device = Device()
+ np.random.seed(0)
+
+ for dtype, low, high, atol, rtol in [
+ ("float32", -127, 128, 0.001, 0.001),
+ ("float32", -1, 1, 0.001, 0.001),
+ ]:
+ inputs = {
+ "a": tvm.nd.array(np.random.uniform(low, high, (100, 100)).astype(dtype)),
+ "b": tvm.nd.array(np.random.uniform(low, high, (100, 100)).astype(dtype)),
+ }
+ outputs = []
+ func = _get_model(inputs["a"].shape, dtype, iter(inputs))
+
+ for acl in [False, True]:
+ outputs.append(build_and_run(func, inputs, 1, None, device, enable_acl=acl)[0])
+
+ verify(outputs, atol=1e-7, rtol=1e-7)
+
+
+def test_codegen_maximum():
+ if skip_codegen_test():
+ return
+
+ shape = (100, 100)
Review comment:
Same as above
##########
File path: tests/python/contrib/test_arm_compute_lib/test_maximum.py
##########
@@ -0,0 +1,104 @@
+# 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.
+"""Arm Compute Library integration reshape tests."""
+
+import numpy as np
+
+import tvm
+from tvm import relay
+
+from .infrastructure import (
+ skip_runtime_test,
+ skip_codegen_test,
+ build_and_run,
+ verify,
+ verify_codegen,
+)
+from .infrastructure import Device
+
+
+def _get_model(input_shape, dtype, var_names):
+ """Return a model and any parameters it may have."""
+ a = relay.var(next(var_names), shape=input_shape, dtype=dtype)
+ b = relay.var(next(var_names), shape=input_shape, dtype=dtype)
+ max = relay.maximum(a, b)
+ return max
+
+
+def _get_expected_codegen(shape, dtype):
+ node = {
+ "op": "kernel",
+ "name": "maximum",
+ "inputs": [[0, 0, 0], [1, 0, 0]],
+ "attrs": {
+ "num_inputs": "2",
+ "num_outputs": "1",
+ "shape": [[list(shape)]],
+ "dtype": [[dtype]],
+ },
+ }
+
+ inputs = [
+ {"op": "input", "name": "", "attrs": {"shape": [[list(shape)]], "dtype": [[dtype]]}},
+ {"op": "input", "name": "", "attrs": {"shape": [[list(shape)]], "dtype": [[dtype]]}},
+ ]
+ inputs.append(node)
+ return inputs
+
+
+def test_maximum():
+ Device.load("test_config.json")
+
+ if skip_runtime_test():
+ return
+
+ device = Device()
+ np.random.seed(0)
+
+ for dtype, low, high, atol, rtol in [
+ ("float32", -127, 128, 0.001, 0.001),
+ ("float32", -1, 1, 0.001, 0.001),
+ ]:
+ inputs = {
+ "a": tvm.nd.array(np.random.uniform(low, high, (100, 100)).astype(dtype)),
Review comment:
Would be useful to include a test for an asymmetric shape as well i.e. (10, 100). Just to check the ordering of inputs remains the same
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