lhutton1 commented on a change in pull request #10801:
URL: https://github.com/apache/tvm/pull/10801#discussion_r838490715
##########
File path: python/tvm/relay/op/contrib/arm_compute_lib.py
##########
@@ -286,6 +286,7 @@ def _func_wrapper(expr):
_register_external_op_helper("reshape")
+_register_external_op_helper("concatenate")
Review comment:
Could we check a couple of conditions before offloading:
- The input data type and output data type are equal
- The concatenation axis is in (0, 1, 2, 3)
_These conditions come from:
https://arm-software.github.io/ComputeLibrary/v22.02/classarm__compute_1_1_n_e_concatenate_layer.xhtml#a97752e7404ffd5623774b26709fb4bc0_
##########
File path: tests/python/contrib/test_arm_compute_lib/test_concatenate.py
##########
@@ -0,0 +1,126 @@
+# 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 space_to_batch_nd tests."""
Review comment:
nit: s/space_to_batch_nd/concatenate
##########
File path: src/runtime/contrib/arm_compute_lib/acl_runtime.cc
##########
@@ -510,6 +539,30 @@ class ACLRuntime : public JSONRuntimeBase {
layer->function = f;
}
+ /*!
+ * \brief Create a Concatenate layer.
+ *
+ * \param layer The ACL layer to build. Containing inputs, outputs and the
ACL function.c
+ * \param node The JSON representation of the operator.
+ */
+ void CreateConcatenateLayer(CachedLayer* layer, const JSONGraphNode& node) {
+ std::vector<std::string> axis =
node.GetAttr<std::vector<std::string>>("axis");
+ std::vector<const arm_compute::ITensor*> inputs;
+ for (auto input : node.GetInputs()) {
+ layer->inputs.push_back(MakeACLTensorFromJSONEntry(input, nullptr,
nullptr, false));
+ layer->json_inputid_to_layer_inputid[std::pair<uint32_t,
uint32_t>(input.id_, input.index_)] =
+ layer->inputs.size() - 1;
+ }
+ for (size_t i = 0; i < layer->inputs.size(); i++) {
+ inputs.push_back(&layer->inputs[i]);
+ }
+ layer->outputs.push_back(MakeACLTensorFromJSONNode(node));
+ int dimNum = layer->inputs[0].info()->num_dimensions();
+ auto function = std::make_shared<arm_compute::NEConcatenateLayer>();
+ function->configure(inputs, &layer->outputs[0], dimNum -
std::stoi(axis[0]) - 1);
Review comment:
It might be worth adding a comment to explain why the axis here is
different
##########
File path: tests/python/contrib/test_arm_compute_lib/test_concatenate.py
##########
@@ -0,0 +1,126 @@
+# 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 space_to_batch_nd tests."""
+
+import numpy as np
+
+import tvm
+from tvm import relay
+from tvm import testing
+
+from test_arm_compute_lib.infrastructure import (
+ skip_runtime_test,
+ skip_codegen_test,
+ build_and_run,
+ verify,
+ verify_codegen,
+)
+from test_arm_compute_lib.infrastructure import Device
+
+
+def _get_model(input_shape_a, input_shape_b, input_shape_c, axis, dtype,
var_names):
+ """Return a model and any parameters it may have."""
+ a = relay.var(next(var_names), shape=input_shape_a, dtype=dtype)
+ b = relay.var(next(var_names), shape=input_shape_b, dtype=dtype)
+ c = relay.var(next(var_names), shape=input_shape_c, dtype=dtype)
+ out = relay.concatenate([a, b, c], axis)
+ return out
+
+
+def _get_expected_codegen(input_shape_a, input_shape_b, input_shape_c, axis,
dtype):
+ node = {
+ "op": "kernel",
+ "name": "concatenate",
+ "inputs": [
+ [0, 0, 0],
+ [0, 1, 0],
+ [0, 2, 0],
+ ],
+ "attrs": {
+ "num_outputs": "1",
+ "num_inputs": "3",
+ "dtype": [[dtype]],
+ "axis": [[str(axis)]],
+ "shape": [[[3, 234, 234, 256]]],
+ },
+ }
+
+ input = {
+ "op": "input",
+ "name": "",
+ "attrs": {
+ "shape": [[input_shape_a, input_shape_b, input_shape_c]],
+ "dtype": [[dtype, dtype, dtype]],
+ },
+ }
+
+ return [input, node]
+
+
+def test_concatenate():
+ Device.load("test_config.json")
+
+ if skip_runtime_test():
+ return
+
+ device = Device()
+ np.random.seed(0)
+
+ for input_shape_a, input_shape_b, input_shape_c, axis in [
+ ([1, 234, 234, 256], [1, 234, 234, 256], [1, 234, 234, 256], 0),
Review comment:
Is it possible to test concatenation of different shapes as well? e.g.
```
[(3, 2, 1), (3, 1, 1)] with axis 1
```
##########
File path: src/runtime/contrib/arm_compute_lib/acl_runtime.cc
##########
@@ -93,10 +94,19 @@ class ACLRuntime : public JSONRuntimeBase {
void Run() override {
for (size_t i = 0; i < input_nodes_.size(); ++i) {
auto nid = input_nodes_[i];
- uint32_t eid = EntryID(nid, 0);
if (nodes_[nid].GetOpType() == "input") {
- void* data = data_entry_[eid]->data;
- CheckACLError(layer_.inputs[i].allocator()->import_memory(data));
+ for (uint32_t index = 0; index < nodes_[nid].GetNumOutput(); index++) {
Review comment:
Would it be possible to rename `index` to something more meaningful e.g.
`eid_idx`? And similar for `i` above to e.g. `nid_idx`, just to help avoid
confusion
##########
File path: src/runtime/contrib/arm_compute_lib/acl_runtime.cc
##########
@@ -166,6 +178,7 @@ class ACLRuntime : public JSONRuntimeBase {
std::shared_ptr<arm_compute::IFunction> function;
std::vector<arm_compute::Tensor> inputs;
std::vector<arm_compute::Tensor> outputs;
+ std::map<std::pair<uint32_t, uint32_t>, uint32_t>
json_inputid_to_layer_inputid;
Review comment:
Perhaps add a comment to explain what this map is for, similar to the
commit message :) It would also be worth stating that using it is optional
(i.e.only when an operator uses the eid index)
##########
File path: tests/python/contrib/test_arm_compute_lib/test_concatenate.py
##########
@@ -0,0 +1,126 @@
+# 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 space_to_batch_nd tests."""
+
+import numpy as np
+
+import tvm
+from tvm import relay
+from tvm import testing
+
+from test_arm_compute_lib.infrastructure import (
+ skip_runtime_test,
+ skip_codegen_test,
+ build_and_run,
+ verify,
+ verify_codegen,
+)
+from test_arm_compute_lib.infrastructure import Device
+
+
+def _get_model(input_shape_a, input_shape_b, input_shape_c, axis, dtype,
var_names):
+ """Return a model and any parameters it may have."""
+ a = relay.var(next(var_names), shape=input_shape_a, dtype=dtype)
+ b = relay.var(next(var_names), shape=input_shape_b, dtype=dtype)
+ c = relay.var(next(var_names), shape=input_shape_c, dtype=dtype)
+ out = relay.concatenate([a, b, c], axis)
+ return out
+
+
+def _get_expected_codegen(input_shape_a, input_shape_b, input_shape_c, axis,
dtype):
+ node = {
+ "op": "kernel",
+ "name": "concatenate",
+ "inputs": [
+ [0, 0, 0],
+ [0, 1, 0],
+ [0, 2, 0],
+ ],
+ "attrs": {
+ "num_outputs": "1",
+ "num_inputs": "3",
+ "dtype": [[dtype]],
+ "axis": [[str(axis)]],
+ "shape": [[[3, 234, 234, 256]]],
+ },
+ }
+
+ input = {
+ "op": "input",
+ "name": "",
+ "attrs": {
+ "shape": [[input_shape_a, input_shape_b, input_shape_c]],
+ "dtype": [[dtype, dtype, dtype]],
+ },
+ }
+
+ return [input, node]
+
+
+def test_concatenate():
+ Device.load("test_config.json")
+
+ if skip_runtime_test():
+ return
+
+ device = Device()
+ np.random.seed(0)
+
+ for input_shape_a, input_shape_b, input_shape_c, axis in [
+ ([1, 234, 234, 256], [1, 234, 234, 256], [1, 234, 234, 256], 0),
+ ]:
+ dtype = "int32"
Review comment:
I'm slightly surprised to see `int32` is working as its not listed in
the supported datatypes here:
https://arm-software.github.io/ComputeLibrary/v22.02/classarm__compute_1_1_n_e_concatenate_layer.xhtml#a97752e7404ffd5623774b26709fb4bc0.
Could we test `float32` and `uint8`?
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