Add more example code for other quantized models

Models contain: dlv3/edsr/iv3/iv4/mv3/resnet50/vit/w2l

Co-authored-by:  chen.zhao <[email protected]>
Co-authored-by:  sangsoo.Ko <[email protected]>

Author: 3 people

backends/samsung/_passes/conv1d_to_conv2d.py

@@ -12,10 +12,6 @@
 
 
 class Conv1dToConv2d(ExportPass):
-    """
-    Convert conv1d to conv2d because exynos doesn't support conv1d
-    """
-
     def __init__(self, edge_program: ExportedProgram):
         super().__init__()
         self.edge_program = edge_program

backends/samsung/_passes/fuse_conv_act.py

@@ -17,7 +17,6 @@ def map_hardtan_relux(tanhnode: torch.fx.node.Node) -> Optional[str]:
     assert (
         tanhnode.target == exir_ops.edge.aten.hardtanh.default
     ), "Must be a hardtanh node"
-    # Not support ReLU1 now
     if not tanhnode.args[1] == 0.0:
         return None
     if tanhnode.args[2] == 6.0:

backends/samsung/builders/__init__.py

@@ -22,6 +22,7 @@
     op_getitem,
     op_hardswish,
     op_hardtanh,
+    op_hardsigmoid,
     op_layer_norm,
     op_leaky_relu,
     op_linear,
@@ -67,6 +68,7 @@
     op_getitem,
     op_hardswish,
     op_hardtanh,
+    op_hardsigmoid,
     op_layer_norm,
     op_leaky_relu,
     op_linear,

backends/samsung/builders/op_avg_pool2d.py

@@ -54,8 +54,7 @@ def define_node(
 
         if len(node.args) > 4:
             ceil_mode = cast(bool, node.args[4])
-            if ceil_mode:
-                raise AssertionError("Not support ceil_mode = True.")
+            assert not ceil_mode, "Not support ceil_mode = True."
 
         if len(node.args) > 5:
             params["count_include_pad"] = cast(bool, node.args[5])
@@ -64,12 +63,8 @@ def define_node(
 
         if len(node.args) > 6:
             divisor_override = cast(int, node.args[6])
-            if divisor_override != kernel_size[0] * kernel_size[1]:
-                raise AssertionError(
-                    "Not supported divisor_override which is not equal to pooling region."
-                )
-
+            assert (
+                divisor_override == kernel_size[0] * kernel_size[1]
+            ), "Not supported divisor_override which is not equal to pooling region."
         output_id = self.define_tensor(node, enn_graph, vals_to_ids)
-        vals_to_ids[node] = output_id
-
         enn_graph.define_op(node.name, "AVGPOOL2D", [input_id], [output_id], params)

backends/samsung/builders/op_clamp.py

@@ -39,10 +39,8 @@ def define_node(
         if len(node.args) > 2 and node.args[2] is not None:
             output_max = cast(float, node.args[2])
 
-        params = {}
+        params = {"minimum": output_min, "maximum": output_max}
         self._update_params_qdtype(node, params)
-        params["minimum"] = output_min
-        params["maximum"] = output_max
 
         output_id = self.define_tensor(node, enn_graph, vals_to_ids)
 

backends/samsung/builders/op_hardsigmoid.py

@@ -0,0 +1,35 @@
+# 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 HardSigmoidVisitor(NodeVisitor):
+    target = "aten.hardsigmoid.default"
+
+    def __init__(self, *args) -> None:
+        super().__init__(*args)
+
+    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)
+        params = {}
+        self._update_params_qdtype(node, params)
+        enn_graph.define_op(node.name, "HardSigmoid", [input_id], [output_id], params)

backends/samsung/builders/op_hardtanh.py

@@ -29,18 +29,13 @@ def define_node(
     ) -> None:
         input = node.args[0]
         input_id = self.define_tensor(input, enn_graph, vals_to_ids)
-        params = {}
-        self._update_params_qdtype(node, params)
+
         # default value of output_min and output_max
-        output_min = -1
-        output_max = 1
-        if len(node.args) > 1:
-            output_min = cast(float, node.args[1])
-        if len(node.args) > 2:
-            output_max = cast(float, node.args[2])
-
-        params["minimum"] = output_min
-        params["maximum"] = output_max
+        output_min = cast(float, node.args[1]) if len(node.args) > 1 else -1
+        output_max = cast(float, node.args[2]) if len(node.args) > 2 else 1
+
+        params = {"minimum": output_min, "maximum": output_max}
+        self._update_params_qdtype(node, params)
 
         output_id = self.define_tensor(node, enn_graph, vals_to_ids)
 

backends/samsung/builders/op_layer_norm.py

@@ -44,10 +44,7 @@ def define_node(
         bias_id = self.define_tensor(bias_node, enn_graph, vals_to_ids)
         all_input_tensors.append(bias_id)
 
-        if len(node.args) > 4:
-            epsilon = node.args[4]
-        else:
-            epsilon = 1e-5
+        epsilon = node.args[4] if len(node.args) > 4 else 1e-5
         params = {"epsilon": epsilon}
         self._update_params_qdtype(node, params)
         output_id = self.define_tensor(node, enn_graph, vals_to_ids)

backends/samsung/builders/op_squeeze.py

@@ -32,29 +32,6 @@ def define_node(
 
         # output
         output_id = self.define_tensor(node, enn_graph, vals_to_ids)
-        params = {}
-        params["new_shape"] = [*node.meta["val"].shape]
-        enn_graph.define_op(node.name, "RESHAPE", [input_id], [output_id], params)
-
-
-@register_node_visitor
-class UnsqueezeVisitor(NodeVisitor):
-    target = "aten.unsqueeze_copy.default"
-
-    def __init__(self, *args) -> None:
-        super().__init__(*args)
 
-    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
-        output_id = self.define_tensor(node, enn_graph, vals_to_ids)
         params = {"new_shape": [*node.meta["val"].shape]}
-
         enn_graph.define_op(node.name, "RESHAPE", [input_id], [output_id], params)

backends/samsung/builders/op_unsqueeze.py

@@ -31,4 +31,5 @@ def define_node(
 
         output_id = self.define_tensor(node, enn_graph, vals_to_ids)
 
-        enn_graph.define_op(node.name, "RESHAPE", [input_id], [output_id])
+        params = {"new_shape": [*node.meta["val"].shape]}
+        enn_graph.define_op(node.name, "RESHAPE", [input_id], [output_id], params)