Support extra ops modes for LLM Models

backends/samsung/_passes/compose_rms_norm.py

@@ -0,0 +1,79 @@
+# Copyright (c) Qualcomm Innovation Center, Inc
+# Copyright (c) 2025 Samsung Electronics Co. LTD
+# All rights reserved
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+from executorch.exir.dialects._ops import ops as exir_ops
+from executorch.exir.pass_base import ExportPass, PassResult
+from torch.fx.passes.utils.source_matcher_utils import get_source_partitions
+
+
+class RecomposeRmsNorm(ExportPass):
+    """
+    Merge decomposed operators back to one super node.
+    """
+
+    def __init__(self):
+        super().__init__()
+
+    def _get_eps_node(self, nodes):
+        # eps: one of inputs of add node
+        add_node = [n for n in nodes if hasattr(n, "name") and "add" in n.name][0]
+        for a in add_node.args:
+            if isinstance(a, float) or a.op != "call_function":
+                return a
+
+    def _get_gamma_node(self, output_node):
+        # gamma: one of inputs of output node
+        for a in output_node.args:
+            if a.op != "call_function":
+                return a
+
+    def call(self, graph_module: torch.fx.GraphModule):
+        graph = graph_module.graph
+        partitions = get_source_partitions(graph, [torch.nn.RMSNorm])
+
+        for _, src_partitions in partitions.items():
+            for src_partition in src_partitions:
+                input_len = len(src_partition.input_nodes)
+                if input_len == 1:
+                    input_node = src_partition.input_nodes[0]
+                elif input_len == 2:
+                    inp_0, inp_1 = src_partition.input_nodes
+                    input_node = inp_0 if len(inp_0.users) == 2 else inp_1
+                else:
+                    raise RuntimeError(
+                        f"Found unsupported case of rms_node {src_partition}, "
+                        f"which has {input_len} inputs"
+                    )
+
+                output_node = src_partition.output_nodes[0]
+                eps_node = self._get_eps_node(src_partition.nodes)
+                gamma_node = self._get_gamma_node(output_node)
+
+                with graph.inserting_before(output_node):
+                    # args schema
+                    # (Tensor input, int[] normalized_shape, Tensor?
+                    # weight=None, float? eps=None) -> Tensor
+                    rms_node = graph.create_node(
+                        "call_function",
+                        exir_ops.edge.aten.rms_norm.default,
+                        (
+                            input_node,
+                            list(gamma_node.meta["val"].shape),
+                            gamma_node,
+                            eps_node,
+                        ),
+                    )
+                    users = output_node.users.copy()
+                    for user in users:
+                        user.replace_input_with(output_node, rms_node)
+                    # copy metadata
+                    rms_node.meta = output_node.meta
+
+        graph.eliminate_dead_code()
+        graph_module.recompile()
+        return PassResult(graph_module, True)

backends/samsung/aot/PyEnnWrapperAdaptor.h

@@ -53,6 +53,10 @@ class PyEnnWrapper {
       return py::array_t<char>();
     }
 
+    auto perf_mode = option_buf_->perf_mode();
+    graphgen_set_perf_mode(
+        graphgen_instance_, static_cast<PerformanceMode>(perf_mode));
+
     auto m_buf_info = model_buffer.request();
     auto* model_buf_ptr = reinterpret_cast<uint8_t*>(m_buf_info.ptr);
     NNCBuffer* nnc_buffer = nullptr;

backends/samsung/builders/__init__.py

@@ -14,18 +14,22 @@
     op_clamp,
     op_constant_pad_nd,
     op_conv2d,
+    op_cos,
     op_dequantize,
     op_div,
     op_embedding,
     op_expand_copy,
     op_gelu,
     op_getitem,
+    op_group_norm,
     op_hardsigmoid,
     op_hardswish,
     op_hardtanh,
+    op_index,
     op_layer_norm,
     op_leaky_relu,
     op_linear,
+    op_log,
     op_log_softmax,
     op_max_pool2d,
     op_maximum,
@@ -34,17 +38,25 @@
     op_mul,
     op_permute,
     op_pixel_shuffle,
+    op_pow,
     op_quantize,
     op_relu,
     op_reshape,
+    op_rms_norm,
     op_rsqrt,
     op_select,
+    op_sigmoid,
+    op_sin,
     op_slice_copy,
     op_softmax,
+    op_split_with_sizes_copy,
     op_sqrt,
     op_squeeze,
     op_sub,
+    op_sum_int_list,
+    op_tanh,
     op_to_copy,
+    op_topk,
     op_unsqueeze,
     op_upsample_bilinear2d,
     op_upsample_nearest2d,
@@ -60,37 +72,49 @@
     op_clamp,
     op_conv2d,
     op_constant_pad_nd,
+    op_cos,
     op_dequantize,
     op_div,
     op_embedding,
     op_expand_copy,
     op_gelu,
     op_getitem,
+    op_group_norm,
     op_hardswish,
     op_hardtanh,
     op_hardsigmoid,
+    op_index,
     op_layer_norm,
     op_leaky_relu,
     op_linear,
     op_log_softmax,
+    op_log,
     op_max_pool2d,
     op_maximum,
     op_mean_dim,
     op_minimum,
     op_mul,
     op_permute,
     op_pixel_shuffle,
+    op_pow,
     op_quantize,
     op_relu,
     op_reshape,
+    op_rms_norm,
     op_rsqrt,
     op_select,
+    op_sigmoid,
+    op_sin,
     op_slice_copy,
     op_softmax,
+    op_split_with_sizes_copy,
     op_sqrt,
     op_squeeze,
     op_sub,
+    op_sum_int_list,
+    op_tanh,
     op_to_copy,
+    op_topk,
     op_unsqueeze,
     op_upsample_bilinear2d,
     op_upsample_nearest2d,

backends/samsung/builders/op_cos.py

@@ -0,0 +1,31 @@
+# Copyright (c) 2025 Samsung Electronics Co. LTD
+# All rights reserved
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Dict
+
+import torch
+from executorch.backends.samsung.builders.node_visitor import (
+    NodeVisitor,
+    register_node_visitor,
+)
+from executorch.backends.samsung.serialization.enn_graph_schema import EnnGraph
+
+
+@register_node_visitor
+class CosVisitor(NodeVisitor):
+    target = "aten.cos.default"
+
+    def define_node(
+        self,
+        node: torch.fx.Node,
+        enn_graph: EnnGraph,
+        vals_to_ids: Dict[torch.Tensor, int],
+    ) -> None:
+        input_id = self.define_tensor(node.args[0], enn_graph, vals_to_ids)
+
+        output_id = self.define_tensor(node, enn_graph, vals_to_ids)
+
+        enn_graph.define_op(node.name, "Cos", [input_id], [output_id])

backends/samsung/builders/op_group_norm.py

@@ -0,0 +1,46 @@
+# Copyright (c) 2025 Samsung Electronics Co. LTD
+# All rights reserved
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import cast, Dict
+
+import torch
+from executorch.backends.samsung.builders.node_visitor import (
+    NodeVisitor,
+    register_node_visitor,
+)
+from executorch.backends.samsung.serialization.enn_graph_schema import EnnGraph
+
+
+@register_node_visitor
+class GroupNormVisitor(NodeVisitor):
+    target = "aten.native_group_norm.default"
+
+    def define_node(
+        self,
+        node: torch.fx.Node,
+        enn_graph: EnnGraph,
+        vals_to_ids: Dict[torch.Tensor, int],
+    ) -> None:
+        all_input_tensors = []
+        input_id = self.define_tensor(node.args[0], enn_graph, vals_to_ids)
+        all_input_tensors.append(input_id)
+
+        weight_node = node.args[1]
+        weight_id = self.define_tensor(weight_node, enn_graph, vals_to_ids)
+        all_input_tensors.append(weight_id)
+        bias_node = node.args[2]
+        bias_id = self.define_tensor(bias_node, enn_graph, vals_to_ids)
+        all_input_tensors.append(bias_id)
+
+        num_groups = cast(int, node.args[6])
+        epsilon = node.args[7]
+
+        params = {"num_groups": num_groups, "epsilon": epsilon}
+
+        output_id = self.define_tensor(node, enn_graph, vals_to_ids, output_idx=0)
+        enn_graph.define_op(
+            node.name, "GROUPNORM", all_input_tensors, [output_id], params
+        )

backends/samsung/builders/op_index.py

@@ -0,0 +1,49 @@
+# Copyright (c) 2025 Samsung Electronics Co. LTD
+# All rights reserved
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Dict
+
+import torch
+from executorch.backends.samsung.builders.node_visitor import (
+    NodeVisitor,
+    register_node_visitor,
+)
+from executorch.backends.samsung.serialization.enn_graph_schema import EnnGraph
+
+
+@register_node_visitor
+class IndexVisitor(NodeVisitor):
+    target = "aten.index.Tensor"
+
+    def define_node(
+        self,
+        node: torch.fx.Node,
+        enn_graph: EnnGraph,
+        vals_to_ids: Dict[torch.Tensor, int],
+    ) -> None:
+        input = node.args[0]
+        input_id = self.define_tensor(input, enn_graph, vals_to_ids)
+
+        axis = 0
+        valid_indices_node_count = 0
+        target_indices_node = None
+        for indices_node in node.args[1]:
+            if indices_node is not None:
+                target_indices_node = indices_node
+                valid_indices_node_count += 1
+                if valid_indices_node_count > 1:
+                    raise NotImplementedError("Not support multi indices node.")
+            if target_indices_node is None:
+                axis += 1
+
+        indices_id = self.define_tensor(target_indices_node, enn_graph, vals_to_ids)
+
+        output_id = self.define_tensor(node, enn_graph, vals_to_ids)
+
+        params = {"axis": axis, "input_type": "params"}
+        enn_graph.define_op(
+            node.name, "GATHER", [input_id, indices_id], [output_id], params
+        )

backends/samsung/builders/op_log.py

@@ -0,0 +1,32 @@
+# Copyright (c) 2025 Samsung Electronics Co. LTD
+# All rights reserved
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Dict
+
+import torch
+from executorch.backends.samsung.builders.node_visitor import (
+    NodeVisitor,
+    register_node_visitor,
+)
+from executorch.backends.samsung.serialization.enn_graph_schema import EnnGraph
+
+
+@register_node_visitor
+class LogVisitor(NodeVisitor):
+    target = "aten.log.default"
+
+    def define_node(
+        self,
+        node: torch.fx.Node,
+        enn_graph: EnnGraph,
+        vals_to_ids: Dict[torch.Tensor, int],
+    ) -> None:
+        input = node.args[0]
+        input_id = self.define_tensor(input, enn_graph, vals_to_ids)
+
+        output_id = self.define_tensor(node, enn_graph, vals_to_ids)
+
+        enn_graph.define_op(node.name, "LOG", [input_id], [output_id])

backends/samsung/builders/op_pow.py

@@ -0,0 +1,42 @@
+# Copyright (c) 2025 Samsung Electronics Co. LTD
+# All rights reserved
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Dict
+
+import torch
+from executorch.backends.samsung.builders.node_visitor import (
+    NodeVisitor,
+    register_node_visitor,
+)
+from executorch.backends.samsung.builders.utils import get_tensor
+from executorch.backends.samsung.serialization.enn_graph_schema import EnnGraph
+
+
+@register_node_visitor
+class PowVisitor(NodeVisitor):
+    target = "aten.pow.Tensor_Tensor"
+
+    def define_node(
+        self,
+        node: torch.fx.Node,
+        enn_graph: EnnGraph,
+        vals_to_ids: Dict[torch.Tensor, int],
+    ) -> None:
+        input1 = node.args[0]
+        input2 = node.args[1]
+        input_tensor_1 = get_tensor(self.exported_program, input1)
+        input_tensor_2 = get_tensor(self.exported_program, input2)
+        assert (
+            input_tensor_1.dtype == torch.float32
+            and input_tensor_2.dtype == torch.float32
+        ), "Requires the two inputs are all float type"
+
+        input_id_1 = self.define_tensor(input1, enn_graph, vals_to_ids)
+        input_id_2 = self.define_tensor(input2, enn_graph, vals_to_ids)
+
+        output_id = self.define_tensor(node, enn_graph, vals_to_ids)
+
+        enn_graph.define_op(node.name, "POW", [input_id_1, input_id_2], [output_id])