chore: import upstream snapshot with attribution

tools/Polygraphy/tests/common/test_datatype.py

@@ -0,0 +1,183 @@
+#
+# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import numpy as np
+import onnx
+import pytest
+import tensorrt as trt
+import torch
+
+from polygraphy import mod, util
+from polygraphy.datatype import DataType, DataTypeEntry
+
+DATATYPES = DataType.__members__.values()
+
+
+class TestDataType:
+    def compare_names(self, name, expected_name, replace_map):
+        # Names may not match up exactly, so use the replace_map to make adjustments to the
+        # foreign type before comparing against the Polygraphy type
+        for old, new in replace_map.items():
+            if name == old:
+                name = new
+        assert name == expected_name
+
+    @pytest.mark.parametrize("dtype", DATATYPES, ids=str)
+    def test_numpy(self, dtype):
+        if dtype in [
+            DataType.BFLOAT16,
+            DataType.FLOAT8E4M3FN,
+            DataType.FLOAT8E4M3FNUZ,
+            DataType.FLOAT8E5M2,
+            DataType.FLOAT8E5M2FNUZ,
+            DataType.INT4,
+            DataType.FLOAT4,
+        ]:
+            pytest.xfail("Type not supported by NumPy")
+
+        np_type = dtype.numpy()
+        assert DataType.to_dtype(dtype, "numpy") == np_type
+        assert np_type.itemsize == dtype.itemsize
+        self.compare_names(np_type.name, dtype.name, {"str": "string"})
+
+        assert isinstance(np_type, np.dtype)
+
+        assert DataType.from_dtype(np_type) == dtype
+
+    @pytest.mark.parametrize("dtype", DATATYPES, ids=str)
+    def test_onnxrt(self, dtype):
+        if dtype in [
+            DataType.INT4,
+            DataType.FLOAT4,
+        ]:
+            pytest.skip("Type not supported by ONNX-RT")
+
+        onnxrt_type = DataType.to_dtype(dtype, "onnxruntime")
+        assert dtype.onnxruntime() == onnxrt_type
+
+        assert isinstance(onnxrt_type, str)
+
+        self.compare_names(
+            onnxrt_type.replace("tensor(", "").replace(")", ""),
+            dtype.name,
+            {
+                "double": "float64",
+                "float": "float32",
+            },
+        )
+
+        assert DataType.from_dtype(onnxrt_type) == dtype
+
+    @pytest.mark.parametrize("dtype", DATATYPES, ids=str)
+    def test_onnx(self, dtype):
+        if dtype in [
+            DataType.INT4,
+            DataType.FLOAT4,
+        ]:
+            pytest.skip("Type not supported by ONNX")
+
+        onnx_type = dtype.onnx()
+        assert DataType.to_dtype(dtype, "onnx") == onnx_type
+
+        assert isinstance(onnx_type, int)
+
+        onnx_type_map = util.invert_dict(dict(onnx.TensorProto.DataType.items()))
+        self.compare_names(
+            onnx_type_map[onnx_type].lower(),
+            dtype.name,
+            {
+                "double": "float64",
+                "float": "float32",
+            },
+        )
+
+        assert DataType.from_dtype(onnx_type) == dtype
+
+    @pytest.mark.skipif(
+        mod.version(trt.__version__) < mod.version("8.7"),
+        reason="Unsupported before TRT 8.7",
+    )
+    @pytest.mark.parametrize("dtype", DATATYPES, ids=str)
+    def test_tensorrt(self, dtype):
+        unsupported_types = [
+            DataType.FLOAT64,
+            DataType.INT16,
+            DataType.UINT16,
+            DataType.UINT32,
+            DataType.UINT64,
+            DataType.STRING,
+            DataType.INT64,
+            DataType.FLOAT8E4M3FNUZ,
+            DataType.FLOAT8E5M2,
+            DataType.FLOAT8E5M2FNUZ,
+        ]
+        if  mod.version(trt.__version__) < mod.version("10.8"):
+            unsupported_types.append(DataType.FLOAT4)
+        if dtype in unsupported_types:
+            pytest.xfail("Type not supported by TensorRT")
+
+        tensorrt_dtype = dtype.tensorrt()
+        assert DataType.to_dtype(dtype, "tensorrt") == tensorrt_dtype
+
+        assert isinstance(tensorrt_dtype, trt.DataType)
+
+        self.compare_names(
+            tensorrt_dtype.name.lower(),
+            dtype.name,
+            {
+                "double": "float64",
+                "float": "float32",
+                "half": "float16",
+                "fp8": "float8e4m3fn",
+                "fp4": "float4",
+                "bf16": "bfloat16",
+            },
+        )
+
+        assert DataType.from_dtype(tensorrt_dtype) == dtype
+
+    @pytest.mark.parametrize("trt_dtype", trt.DataType.__members__.values())
+    def test_all_tensorrt_types_supported(self, trt_dtype):
+        dtype = DataType.from_dtype(trt_dtype, "tensorrt")
+        assert isinstance(dtype, DataTypeEntry)
+
+        assert dtype.tensorrt() == trt_dtype
+
+    @pytest.mark.parametrize("dtype", DATATYPES, ids=str)
+    def test_torch(self, dtype):
+        if dtype in [
+            DataType.FLOAT8E4M3FN,
+            DataType.FLOAT8E4M3FNUZ,
+            DataType.FLOAT8E5M2,
+            DataType.FLOAT8E5M2FNUZ,
+            DataType.UINT16,
+            DataType.UINT32,
+            DataType.UINT64,
+            DataType.STRING,
+            DataType.INT4,
+            DataType.FLOAT4,
+        ]:
+            pytest.xfail("Type not supported by Torch")
+
+        torch_type = dtype.torch()
+        assert DataType.to_dtype(dtype, "torch") == torch_type
+
+        assert isinstance(torch_type, torch.dtype)
+
+        self.compare_names(str(torch_type).replace("torch.", ""), dtype.name, {})
+
+        assert DataType.from_dtype(torch_type) == dtype

tools/Polygraphy/tests/common/test_interface.py

@@ -0,0 +1,70 @@
+#
+# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import pytest
+
+from polygraphy.common.interface import TypedDict, TypedList
+from polygraphy.exception import PolygraphyException
+
+
+@pytest.fixture()
+def int_to_float():
+    class IntToFloat(TypedDict(lambda: int, lambda: float)):
+        pass
+
+    return IntToFloat()
+
+
+class TestTypedDict:
+    def test_wrong_type_set_item_value(self, int_to_float):
+        with pytest.raises(PolygraphyException, match="Unsupported value type"):
+            int_to_float[0] = "hi"
+
+    def test_wrong_type_set_item_key(self, int_to_float):
+        with pytest.raises(PolygraphyException, match="Unsupported key type"):
+            int_to_float["hi"] = 1.0
+
+    def test_wrong_type_update(self, int_to_float):
+        with pytest.raises(PolygraphyException, match="Unsupported key type"):
+            int_to_float.update({"hi": 1.0})
+
+
+@pytest.fixture()
+def ints():
+    class Ints(TypedList(lambda: int)):
+        pass
+
+    return Ints()
+
+
+class TestTypedList:
+    def test_wrong_type_append(self, ints):
+        with pytest.raises(PolygraphyException, match="Unsupported element type"):
+            ints.append(1.0)
+
+    def test_wrong_type_extend(self, ints):
+        with pytest.raises(PolygraphyException, match="Unsupported element type"):
+            ints.extend([0, 1, 2, 3, "surprise"])
+
+    def test_wrong_type_iadd(self, ints):
+        with pytest.raises(PolygraphyException, match="Unsupported element type"):
+            ints += [0, 1.0]
+
+    def test_wrong_type_setitem(self, ints):
+        ints.append(0)
+        with pytest.raises(PolygraphyException, match="Unsupported element type"):
+            ints[0] = 1.0

tools/Polygraphy/tests/common/test_struct.py

@@ -0,0 +1,36 @@
+#
+# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+from polygraphy.common import TensorMetadata
+from polygraphy.datatype import DataType
+
+
+class TestTensorMetadata:
+    def test_str(self):
+        meta = TensorMetadata().add("X", dtype=DataType.FLOAT32, shape=(64, 64))
+        assert str(meta) == "{X [dtype=float32, shape=(64, 64)]}"
+
+    def test_str_no_dtype(self):
+        meta = TensorMetadata().add("X", dtype=None, shape=(64, 64))
+        assert str(meta) == "{X [shape=(64, 64)]}"
+
+    def test_str_no_shape(self):
+        meta = TensorMetadata().add("X", dtype=DataType.FLOAT32, shape=None)
+        assert str(meta) == "{X [dtype=float32]}"
+
+    def test_str_no_meta(self):
+        meta = TensorMetadata().add("X", dtype=None, shape=None)
+        assert str(meta) == "{X}"