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authorTai Ly <tai.ly@arm.com>2023-05-05 18:04:36 +0000
committerTai Ly <tai.ly@arm.com>2023-05-06 21:49:39 +0000
commit04992e45ad982ce34edacea9f9275ca122e47a75 (patch)
tree6843f96a84d9e07f220117949bd9faff64bfd4c3 /third_party
parent8ffce6d63372ea93f3e060571137bce11d4735d8 (diff)
downloadtosa_mlir_translator-04992e45ad982ce34edacea9f9275ca122e47a75.tar.gz
Add BuildMlirFromTosaFile API
This BuildMlirFromTosaFile API deserializes a tosa fbs or json file and returns a mlir module as OwningOpRef<ModuleOp> This also refactors the existing deserialization passes to use the new API, and then copy the deserialized module's main function into the existing function, and copy all attributes of the new function and new module into the existing function and module. This allows testing of the new API by running deserialization passes. Here is an example showing the attributes on the deserialized module and functions: module attributes {tf_saved_model.semantics, tosa.description = "Tosa FBS Converted", tosa.fbs_version = "0.70.0d"} { func.func @main(%arg0: tensor<1x256x256x3xui8> {tf_saved_model.index_path = ["input_0"]}) -> (tensor<1x1x17x3xf32> {tf_saved_model.index_path = ["output_0"]}) attributes {tf.entry_function = {inputs = "tosa_de serialized_input_0:0", outputs = "tosa_deserialized_output_0:0"}, tf_saved_model.exported_names = ["tosa_deserialized"]} { Signed-off-by: Tai Ly <tai.ly@arm.com> Change-Id: Ia6c0202ef43ce5d37788cd459ed7c3f8424dd619
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