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 #3507

Signed-Off By: Vivek Khandelwal <[email protected]>

Author: vivekkhandelwal1

projects/pt1/e2e_testing/xfail_sets.py

@@ -2572,8 +2572,6 @@
     "SplitDimStaticModule_basic",
     "SqrtIntConstantModule_basic",
     "SqrtIntModule_basic",
-    "StdCorrectionEmptyDimModule_basic",
-    "StdDimEmptyDimModule_basic",
     "SubFloatModule_basic",
     "SubIntModule_basic",
     "TanhBackward_basic",
@@ -2627,8 +2625,6 @@
     "UpSampleNearest2dDynamicFactor_basic",
     "UpSampleNearest2dStaticFactor_basic",
     "UpSampleNearest2d_basic",
-    "VarCorrectionEmptyDimModule_basic",
-    "VarDimEmptyDimModule_basic",
     "ViewCollapseDynamicWithAtenSizeIntModule_basic",
     "ViewCollapseModule_basic",
     "ViewDynamicExpandCollapseModule_basic",
@@ -2797,6 +2793,10 @@
     # Runtime crash: mismatched size for broadcast
     "MaxPool2dWithIndicesAllNegativeValuesModule_basic",
     "MaxPool2dWithIndicesNonDefaultPaddingModule_basic",
+    "StdDimEmptyDimModule_basic",
+    "StdCorrectionEmptyDimModule_basic",
+    "VarCorrectionEmptyDimModule_basic",
+    "VarDimEmptyDimModule_basic",
 }
 
 FX_IMPORTER_TOSA_XFAIL_SET = {

python/torch_mlir/extras/onnx_importer.py

@@ -1098,6 +1098,8 @@ def get_operator_function(
     onnx.TensorProto.DataType.FLOAT8E5M2: lambda: Float8E5M2Type.get(),
     onnx.TensorProto.DataType.FLOAT8E5M2FNUZ: lambda: Float8E5M2FNUZType.get(),
     onnx.TensorProto.DataType.STRING: lambda: "!torch.str",
+    onnx.TensorProto.DataType.UINT4: lambda: IntegerType.get_unsigned(4),
+    onnx.TensorProto.DataType.INT4: lambda: IntegerType.get_signed(4),
     # Ommitted: STRING,
 }
 
@@ -1134,6 +1136,9 @@ def get_operator_function(
         ),
         signless=False,
     ),
+    onnx.TensorProto.DataType.UINT8: lambda tp: DenseElementsAttr.get(
+        np.asarray(tp.int32_data, dtype=np.uint8).reshape(tp.dims), signless=False
+    ),
     onnx.TensorProto.DataType.INT8: lambda tp: DenseElementsAttr.get(
         np.asarray(tp.int32_data, dtype=np.int8).reshape(tp.dims), signless=False
     ),

python/torch_mlir/tools/import_onnx/__main__.py

@@ -84,7 +84,7 @@ def load_onnx_model(args: argparse.Namespace) -> onnx.ModelProto:
         raw_model = onnx.load(args.input_file)
     else:
         raw_model = onnx.load(args.input_file, load_external_data=False)
-        onnx.load_external_data_for_model(raw_model, args.data_dir)
+        onnx.load_external_data_for_model(raw_model, str(args.data_dir))
 
     if args.opset_version:
         raw_model = onnx.version_converter.convert_version(

test-requirements.txt

@@ -1,5 +1,5 @@
 pillow
 dill
 multiprocess
-onnx==1.15.0
+onnx==1.16.1
 mpmath==1.3.0