[GitHub] [incubator-tvm] manupa-arm commented on a change in pull request #5915: [BYOC][Contrib] Arm Compute Library integration

[GitBox <gi...@apache.org>]

manupa-arm commented on a change in pull request #5915:
URL: https://github.com/apache/incubator-tvm/pull/5915#discussion_r456314032



##########
File path: src/relay/backend/contrib/arm_compute_lib/codegen.cc
##########
@@ -0,0 +1,186 @@
+/*
+ * 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.
+ */
+
+/*!
+ * \file src/relay/backend/contrib/arm_compute_lib/codegen_acl.cc
+ * \brief Implementation of the Relay -> ACL JSON serializer.
+ */
+#include <tvm/ir/module.h>
+#include <tvm/relay/attrs/nn.h>
+#include <tvm/relay/type.h>
+
+#include "../../utils.h"
+#include "codegen_acl.h"
+
+namespace tvm {
+namespace relay {
+namespace contrib {
+namespace arm_compute_lib {
+
+using JSONGraphNode = tvm::runtime::json::JSONGraphNode;
+using JSONGraphNodeEntry = tvm::runtime::json::JSONGraphNodeEntry;
+
+std::vector<JSONGraphNodeEntry> ACLJSONSerializer::VisitExpr_(const CallNode* cn) {
+  Expr expr = GetRef<Expr>(cn);
+  std::string name;
+  std::shared_ptr<JSONGraphNode> json_node;
+
+  if (cn->op.as<OpNode>()) {
+    json_node = CreateOpJSONNode(cn);
+  } else if (const auto* fn = cn->op.as<FunctionNode>()) {
+    auto comp = fn->GetAttr<String>(attr::kComposite);
+    CHECK(comp.defined()) << "Arm Compute Library JSON runtime only supports composite functions.";

Review comment:
       Minor comment : Is there a downside going into nested function hierarchies that are not composite functions. What if just use JSONSerializer::VisiExpr_(cn) for every other case if its not a composite function?

##########
File path: python/tvm/relay/op/contrib/arm_compute_lib.py
##########
@@ -0,0 +1,119 @@
+# 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=invalid-name, unused-argument
+"""ACL library supported operators."""
+import tvm
+from tvm.relay import transform
+from tvm.relay.build_module import bind_params_by_name
+
+from ...dataflow_pattern import wildcard, is_op, is_constant
+from .register import register_pattern_table
+
+
+def is_arm_compute_runtime_enabled():
+    """Check if the ACL graph runtime is present.
+
+    Returns
+    -------
+    ret: bool
+        True if present, False if not.
+    """
+    return tvm.get_global_func("relay.op.is_arm_compute_runtime_enabled", True)
+
+
+def partition_for_arm_compute_lib(mod, params=None):
+    """Partition the graph greedily offloading supported
+    operators to Arm Compute Library.
+
+    Parameters
+    ----------
+    mod : Module
+        The module to run passes on.
+    params : Optional[Dict[str, NDArray]]
+        Constant input parameters.
+
+    Returns
+    -------
+    ret : annotated and partitioned module.
+    """
+    if params:
+        mod['main'] = bind_params_by_name(mod['main'], params)
+
+    seq = tvm.transform.Sequential([transform.MergeComposite(arm_compute_lib_pattern_table()),
+                                    transform.AnnotateTarget('arm_compute_lib'),
+                                    transform.PartitionGraph()])
+
+    return seq(mod)
+
+
+@register_pattern_table("arm_compute_lib")
+def arm_compute_lib_pattern_table():
+    """Get the ACL pattern table."""
+
+    def conv_pattern():
+        """Create a convolution pattern.
+
+        Returns
+        -------
+        pattern : dataflow_pattern.AltPattern
+            Denotes the convolution pattern.
+        """
+        pattern = is_op('nn.pad')(wildcard()) | wildcard()
+        pattern = is_op('nn.conv2d')(pattern, is_constant())
+        pattern = pattern.optional(lambda x: is_op('nn.bias_add')(x, is_constant()))
+        pattern = pattern.optional(is_op('nn.relu'))

Review comment:
       Aren't all Relu variants are lowered to clip in Relay? Thus, not sure this one will ever get matched?




----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.

For queries about this service, please contact Infrastructure at:
users@infra.apache.org