Merge branch 'dev' into allowNoInputProducer

aidge_export_cpp/export.py

@@ -100,13 +100,18 @@ def export(export_folder_name, graphview, scheduler, mem_wrapping=False):
             list_outputs_name.append((export_type, f"{node.name()}_output_0"))
 
     # Generate forward file
+    # TODO: for now the mem type is bound for all intermediate results, should change.
+    # Note that we may have all inputs constants, hence select output type
+    assert len(list_outputs_name) >= 1, f"TODO: requires some output to determine mem type"
+    mem_ctype = list_outputs_name[0][0]
     generate_file(
         str(dnn_folder / "src" / "forward.cpp"),
         str(ROOT / "templates" / "network" / "network_forward.jinja"),
         headers=set(list_configs),
         actions=list_actions,
         inputs= list_inputs_name,
-        outputs=list_outputs_name
+        outputs=list_outputs_name,
+        mem_ctype=mem_ctype,
     )
 
     # Generate dnn API

aidge_export_cpp/operators.py

@@ -186,6 +186,8 @@ class AddCPP(ExportNode):
 
         copyfile(str(ROOT / "kernels" / "elemwise.hpp"),
                  str(export_folder / "include" / "kernels"))
+        copyfile(str(ROOT / "kernels" / "activation.hpp"),
+                 str(export_folder / "include" / "kernels"))
 
         generate_file(
             str(export_folder / "layers" / f"{self.attributes['name']}.h"),
@@ -214,6 +216,9 @@ class SubCPP(ExportNode):
         list_configs.append("kernels/elemwise.hpp")
         copyfile(str(ROOT / "kernels" / "elemwise.hpp"),
                  str(export_folder / "include" / "kernels"))
+        copyfile(str(ROOT / "kernels" / "activation.hpp"),
+                 str(export_folder / "include" / "kernels"))
+
         generate_file(
             str(export_folder / "layers" / f"{self.attributes['name']}.h"),
             str(ROOT / "templates" / "configuration" / "elemwise_config.jinja"),
@@ -231,6 +236,40 @@ class SubCPP(ExportNode):
         ))
         return list_actions
 
+@operator_register("Mul")
+class MulCPP(ExportNode):
+    def __init__(self, node):
+        super().__init__(node)
+
+    def export(self, export_folder:str, list_configs:list):
+        list_configs.append(f"layers/{self.name}.h")
+        list_configs.append("kernels/elemwise.hpp")
+        copyfile(str(ROOT / "kernels" / "elemwise.hpp"),
+                 str(export_folder / "include" / "kernels"))
+        copyfile(str(ROOT / "kernels" / "activation.hpp"),
+                 str(export_folder / "include" / "kernels"))
+
+        generate_file(
+            str(export_folder / "layers" / f"{self.name}.h"),
+            str(ROOT / "templates" / "configuration" / "elemwise_config.jinja"),
+            name=self.name,
+            nb_elts=np.prod(self.inputs_dims[0]),
+            activation="Linear",
+            elemwise_op="Mul",
+            rescaling="NoScaling")
+
+        return list_configs
+
+    def forward(self, list_actions:list):
+        if not self.is_last:
+            list_actions.append(set_up_output(self.name, "float"))
+
+        list_actions.append(generate_str(
+            str(ROOT / "templates" / "kernel_forward" / "elemwise_forward.jinja"),
+            **self.attributes
+        ))
+        return list_actions
+
 
 @operator_register("PaddedMaxPooling")
 class PaddedMaxPoolCPP(ExportNode):

version.txt

-0.1.1
\ No newline at end of file
+0.1.2
\ No newline at end of file