add multiply and remove subtract for dnnl json runtime (#9120)

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

@@ -64,7 +64,6 @@ def _func_wrapper(expr):
 _register_external_op_helper("nn.dense")
 _register_external_op_helper("nn.relu")
 _register_external_op_helper("add")
-_register_external_op_helper("subtract")
 _register_external_op_helper("multiply")
 
 

src/runtime/contrib/dnnl/dnnl_json_runtime.cc

@@ -113,7 +113,9 @@ class DNNLJSONRuntime : public JSONRuntimeBase {
         } else if ("nn.relu" == op_name) {
           Relu(nid);
         } else if ("add" == op_name) {
-          Add(nid);
+          Binary(nid, dnnl::algorithm::binary_add);
+        } else if ("multiply" == op_name) {
+          Binary(nid, dnnl::algorithm::binary_mul);
         } else {
           LOG(FATAL) << "Unsupported op: " << op_name;
         }
@@ -355,7 +357,7 @@ class DNNLJSONRuntime : public JSONRuntimeBase {
     net_args_.push_back({{DNNL_ARG_SRC, data_memory}, {DNNL_ARG_DST, out_memory}});
   }
 
-  void Add(const size_t& nid) {
+  void Binary(const size_t& nid, dnnl::algorithm algo) {
     auto node = nodes_[nid];
 
     // Memory and compute description.
@@ -377,11 +379,10 @@ class DNNLJSONRuntime : public JSONRuntimeBase {
     JSONGraphNodeEntry out_entry(nid, 0);
     auto out_memory = BindDNNLMemory(out_entry, out_md);
 
-    auto add_desc =
-        dnnl::binary::desc(dnnl::algorithm::binary_add, data_mds[0], data_mds[1], out_md);
-    auto add_prim_desc = dnnl::binary::primitive_desc(add_desc, engine_);
-    auto add = dnnl::binary(add_prim_desc);
-    net_.push_back(add);
+    auto binary_desc = dnnl::binary::desc(algo, data_mds[0], data_mds[1], out_md);
+    auto binary_prim_desc = dnnl::binary::primitive_desc(binary_desc, engine_);
+    auto binary = dnnl::binary(binary_prim_desc);
+    net_.push_back(binary);
 
     net_args_.push_back({{DNNL_ARG_SRC_0, data_memories[0]},
                          {DNNL_ARG_SRC_1, data_memories[1]},

tests/python/relay/test_json_runtime.py

@@ -216,6 +216,50 @@ def gen_add():
     check_result(mod, ref_mod, {"data0": data0, "data1": data1}, shape, tol=1e-5)
 
 
+def test_multiply():
+    """Test a subgraph with a single add operator."""
+    if not tvm.get_global_func("runtime.DNNLJSONRuntimeCreate", True):
+        print("skip because DNNL codegen is not available")
+        return
+
+    dtype = "float32"
+    shape = (10, 10)
+
+    def gen_multiply():
+        data0 = relay.var("data0", shape=shape, dtype=dtype)
+        data1 = relay.var("data1", shape=shape, dtype=dtype)
+        out = relay.multiply(data0, data1)
+
+        func = relay.Function([data0, data1], out)
+        func = set_func_attr(func, "dnnl", "tvmgen_default_dnnl_0")
+        glb_var = relay.GlobalVar("tvmgen_default_dnnl_0")
+        mod = tvm.IRModule()
+        mod[glb_var] = func
+        mod = transform.InferType()(mod)
+
+        data0 = relay.var("data0", shape=shape, dtype=dtype)
+        data1 = relay.var("data1", shape=shape, dtype=dtype)
+        main_f = relay.Function([data0, data1], glb_var(data0, data1))
+        mod["main"] = main_f
+        mod = transform.InferType()(mod)
+
+        data0 = relay.var("data0", shape=shape, dtype=dtype)
+        data1 = relay.var("data1", shape=shape, dtype=dtype)
+        out = relay.multiply(data0, data1)
+        main_f = relay.Function([data0, data1], out)
+        ref_mod = tvm.IRModule()
+        ref_mod["main"] = main_f
+        ref_mod = transform.InferType()(ref_mod)
+
+        return mod, ref_mod
+
+    mod, ref_mod = gen_multiply()
+
+    data0 = np.random.uniform(0, 1, shape).astype(dtype)
+    data1 = np.random.uniform(0, 1, shape).astype(dtype)
+    check_result(mod, ref_mod, {"data0": data0, "data1": data1}, shape, tol=1e-5)
+
+
 def test_relu():
     """Test a subgraph with a single ReLU operator."""
     if not tvm.get_global_func("runtime.DNNLJSONRuntimeCreate", True):
@@ -672,6 +716,7 @@ def test_partial_constant():
 if __name__ == "__main__":
     test_conv2d()
     test_add()
+    test_multiply()
     test_relu()
     test_dense()
     test_bn()