[OpenCLML] More ops and network coverage.

     * Enabled Resnet & InceptionV3
     * FP16 support added for CLML ops.

Co-authored-by: Krishna Raju <quic_kvegiraj@quicinc.com>
Co-authored-by: Shwetank Singh <quic_shwesing@quicinc.com>

python/tvm/relay/op/contrib/clml.py

@@ -23,7 +23,7 @@
 from tvm.relay import transform
 from tvm.relay.build_module import bind_params_by_name
 
-from ...dataflow_pattern import wildcard, is_op, is_constant, is_tuple_get_item
+from ...dataflow_pattern import wildcard, is_op, is_constant, is_tuple_get_item, is_tuple
 from .register import register_pattern_table
 from ..strategy.generic import is_depthwise_conv2d
 
@@ -135,13 +135,15 @@ def conv_pattern():
         """Create a convolution pattern."""
         pattern = is_op("nn.conv2d")(wildcard(), is_constant())
         pattern = pattern.optional(lambda x: is_op("nn.bias_add")(x, is_constant()))
+        pattern = pattern.optional(lambda x: is_op("add")(x, is_constant()))
         pattern = pattern.optional(
             lambda x: is_op("nn.batch_norm")(
                 x, is_constant(), is_constant(), is_constant(), is_constant()
             )
         )
         pattern = pattern.optional(is_tuple_get_item)
         pattern = pattern.optional(is_op("nn.relu"))
+        pattern = pattern.optional(is_op("clip"))
         return pattern
 
     def batch_norm_pattern():
@@ -152,10 +154,24 @@ def batch_norm_pattern():
         pattern = is_tuple_get_item(pattern)
         return pattern
 
+    def concat_pattern():
+        """Create a concat pattern.
+
+        Returns
+        -------
+        pattern : dataflow_pattern.AltPattern
+            Denotes the concat pattern.
+        """
+        pattern = is_tuple(None)
+        pattern = is_op("concatenate")(pattern)
+
+        return pattern
+
     def dense_pattern():
         """Create a dense pattern."""
         pattern = is_op("nn.dense")(wildcard(), is_constant())
         pattern = pattern.optional(lambda x: is_op("add")(x, is_constant()))
+        pattern = pattern.optional(lambda x: is_op("nn.bias_add")(x, is_constant()))
         return pattern
 
     def pad_pattern():
@@ -172,6 +188,13 @@ def check_conv(extract):
             call = call.args[0]
             if isinstance(call, tvm.relay.expr.TupleGetItem):
                 call = call.tuple_value
+        elif call.op.name == "clip":
+            if call.attrs["a_min"] != 0.0 or call.attrs["a_max"] != 6.0:
+                return False
+            call = call.args[0]
+            if isinstance(call, tvm.relay.expr.TupleGetItem):
+                call = call.tuple_value
+
         while call.op.name != "nn.conv2d":
             call = call.args[0]
         attrs, args = call.attrs, call.args
@@ -194,6 +217,7 @@ def check_conv(extract):
         ("clml.conv2d", conv_pattern(), check_conv),
         ("clml.dense", dense_pattern()),
         ("clml.pad", pad_pattern()),
+        ("clml.concat", concat_pattern()),
         ("clml.batch_norm", batch_norm_pattern()),
     ]
 
@@ -207,11 +231,18 @@ def _func_wrapper(expr):
 
 
 _register_external_op_helper("clip")
-_register_external_op_helper("relu")
+_register_external_op_helper("nn.relu")
 _register_external_op_helper("nn.global_avg_pool2d")
 _register_external_op_helper("nn.global_max_pool2d")
+_register_external_op_helper("nn.avg_pool2d")
+_register_external_op_helper("nn.max_pool2d")
 _register_external_op_helper("nn.softmax")
 _register_external_op_helper("reshape")
+_register_external_op_helper("add")
+_register_external_op_helper("subtract")
+_register_external_op_helper("multiply")
+_register_external_op_helper("minimum")
+_register_external_op_helper("maximum")
 
 
 class OpAttrContext(object):

src/relay/backend/contrib/clml/codegen.cc

@@ -91,6 +91,8 @@ class CLMLJSONSerializer : public backend::contrib::JSONSerializer {
       json_node = CreateDenseJSONNode(cn);
     } else if (name == "clml.pad") {
       json_node = CreatePadJSONNode(cn);
+    } else if (name == "clml.concat") {
+      json_node = CreateConcatJSONNode(cn);
     } else {
       LOG(FATAL) << "Unrecognized CLML  pattern: " << name;
     }
@@ -148,6 +150,15 @@ class CLMLJSONSerializer : public backend::contrib::JSONSerializer {
       } else {
         current_call = current_call->args[0].as<CallNode>();
       }
+    } else if (backend::IsOp(current_call, "clip")) {
+      nodes.activation = current_call;
+      nodes.act_type = "relu6";
+      if (current_call->args[0].as<TupleGetItemNode>()) {
+        auto tuple_item = current_call->args[0].as<TupleGetItemNode>();
+        current_call = tuple_item->tuple.as<CallNode>();
+      } else {
+        current_call = current_call->args[0].as<CallNode>();
+      }
     }
     if (backend::IsOp(current_call, "nn.batch_norm")) {
       nodes.bn = current_call;
@@ -279,6 +290,32 @@ class CLMLJSONSerializer : public backend::contrib::JSONSerializer {
     return json_node;
   }
 
+  /*!
+   * \brief Create a JSON representation of a Concat operator.
+   *
+   * \param cn The call to be represented.
+   * \return A JSON representation of a specific operator.
+   */
+  std::shared_ptr<JSONGraphNode> CreateConcatJSONNode(const CallNode* cn) {
+    const auto* fn = cn->op.as<FunctionNode>();
+    ICHECK(fn);
+    const auto* concat = fn->body.as<CallNode>();
+
+    ICHECK(backend::IsOp(concat, "concatenate"));
+    const auto* concat_op = concat->op.as<OpNode>();
+    ICHECK(concat_op);
+    const std::string name = concat_op->name;
+
+    std::vector<JSONGraphNodeEntry> inputs;
+    for (auto arg : cn->args) {
+      inputs.push_back(VisitExpr(arg)[0]);
+    }
+
+    auto json_node = std::make_shared<JSONGraphNode>(name, "kernel", inputs, 1);
+    SetCallNodeAttribute(json_node, concat);
+    return json_node;
+  }
+
   /*!
    * \brief Create a JSON representation of a Dense operator.
    *