mirror of https://github.com/llvm/torch-mlir
Bump Onnx Version to 1.16.1 (#3515)
This commit adds the support for new data types: uint4, and int4 and uint8 tensor protos. Also, it moves some tests from failing to crashing. Fixes https://github.com/llvm/torch-mlir/issues/3507 Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>pull/3517/head
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@ -2572,8 +2572,6 @@ ONNX_XFAIL_SET = {
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"SplitDimStaticModule_basic",
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"SqrtIntConstantModule_basic",
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"SqrtIntModule_basic",
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"StdCorrectionEmptyDimModule_basic",
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"StdDimEmptyDimModule_basic",
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"SubFloatModule_basic",
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"SubIntModule_basic",
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"TanhBackward_basic",
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@ -2627,8 +2625,6 @@ ONNX_XFAIL_SET = {
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"UpSampleNearest2dDynamicFactor_basic",
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"UpSampleNearest2dStaticFactor_basic",
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"UpSampleNearest2d_basic",
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"VarCorrectionEmptyDimModule_basic",
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"VarDimEmptyDimModule_basic",
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"ViewCollapseDynamicWithAtenSizeIntModule_basic",
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"ViewCollapseModule_basic",
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"ViewDynamicExpandCollapseModule_basic",
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@ -2797,6 +2793,10 @@ ONNX_CRASHING_SET = {
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# Runtime crash: mismatched size for broadcast
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"MaxPool2dWithIndicesAllNegativeValuesModule_basic",
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"MaxPool2dWithIndicesNonDefaultPaddingModule_basic",
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"StdDimEmptyDimModule_basic",
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"StdCorrectionEmptyDimModule_basic",
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"VarCorrectionEmptyDimModule_basic",
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"VarDimEmptyDimModule_basic",
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}
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FX_IMPORTER_TOSA_XFAIL_SET = {
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@ -1098,6 +1098,8 @@ ELEM_TYPE_TO_IR_TYPE_CB = {
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onnx.TensorProto.DataType.FLOAT8E5M2: lambda: Float8E5M2Type.get(),
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onnx.TensorProto.DataType.FLOAT8E5M2FNUZ: lambda: Float8E5M2FNUZType.get(),
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onnx.TensorProto.DataType.STRING: lambda: "!torch.str",
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onnx.TensorProto.DataType.UINT4: lambda: IntegerType.get_unsigned(4),
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onnx.TensorProto.DataType.INT4: lambda: IntegerType.get_signed(4),
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# Ommitted: STRING,
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}
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@ -1134,6 +1136,9 @@ ELEM_TYPE_INLINE_TENSOR_PROTO_CB = {
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),
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signless=False,
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),
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onnx.TensorProto.DataType.UINT8: lambda tp: DenseElementsAttr.get(
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np.asarray(tp.int32_data, dtype=np.uint8).reshape(tp.dims), signless=False
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),
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onnx.TensorProto.DataType.INT8: lambda tp: DenseElementsAttr.get(
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np.asarray(tp.int32_data, dtype=np.int8).reshape(tp.dims), signless=False
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),
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@ -84,7 +84,7 @@ def load_onnx_model(args: argparse.Namespace) -> onnx.ModelProto:
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raw_model = onnx.load(args.input_file)
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else:
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raw_model = onnx.load(args.input_file, load_external_data=False)
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onnx.load_external_data_for_model(raw_model, args.data_dir)
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onnx.load_external_data_for_model(raw_model, str(args.data_dir))
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if args.opset_version:
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raw_model = onnx.version_converter.convert_version(
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@ -1,5 +1,5 @@
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pillow
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dill
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multiprocess
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onnx==1.15.0
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onnx==1.16.1
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mpmath==1.3.0
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