chore: import upstream snapshot with attribution

tools/Polygraphy/polygraphy/common/__init__.py

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+from polygraphy.exception import *
+from polygraphy.common.struct import *

tools/Polygraphy/polygraphy/common/interface.py

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+#
+# 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 copy
+from collections import OrderedDict
+
+from polygraphy import util
+from polygraphy.logger import G_LOGGER
+
+#
+# NOTE: These classes intentionally don't inherit from the built-in collections (dict, list, etc.)
+# because doing so prevents us from providing custom JSON serialization methods, since the default
+# encoder implementation can handle most of the built-in collections and therefore doesn't dispatch
+# to custom implementations.
+#
+
+
+def TypedDict(key_type_func, value_type_func):
+    """
+    Returns a class (not an instance) that will provide a dictionary-like
+    interface with runtime type checks.
+
+    Note: The types are provided lazily via a callable to avoid unnecessary dependencies
+    on types from external packages at import-time.
+
+    Args:
+        key_type_func (Callable() -> type):
+                A callable that returns the expected key type.
+        value_type_func (Callable() -> type):
+                A callable that returns the expected value type.
+    """
+
+    class Interface:
+        def __init__(self, dct=None):
+            self.dct = OrderedDict(util.default(dct, {}))
+            self.key_type = key_type_func()
+            self.value_type = value_type_func()
+
+        def _check_types(self, key, val):
+            if not isinstance(key, self.key_type):
+                G_LOGGER.critical(
+                    f"Unsupported key type in {self}. Key: {repr(key)} is type `{type(key).__name__}` but {type(self).__name__} expects type `{self.key_type.__name__}`"
+                )
+            if not isinstance(val, self.value_type):
+                G_LOGGER.critical(
+                    f"Unsupported value type in {self}. Value: {repr(val)} for key: {repr(key)} is type `{type(val).__name__}` but {type(self).__name__} expects type `{self.value_type.__name__}`"
+                )
+
+        def keys(self):
+            return self.dct.keys()
+
+        def values(self):
+            return self.dct.values()
+
+        def items(self):
+            return self.dct.items()
+
+        def update(self, other):
+            for key, val in other.items():
+                self._check_types(key, val)
+            return self.dct.update(other)
+
+        def __contains__(self, key):
+            return key in self.dct
+
+        def __getitem__(self, key):
+            return self.dct[key]
+
+        def __setitem__(self, key, val):
+            self._check_types(key, val)
+            self.dct[key] = val
+
+        def __str__(self):
+            return str(self.dct)
+
+        def __repr__(self):
+            return repr(self.dct)
+
+        def __len__(self):
+            return len(self.dct)
+
+        def __eq__(self, other):
+            return self.dct == other.dct
+
+        def __iter__(self):
+            return self.dct.__iter__()
+
+        def __copy__(self):
+            new_dict = type(self)()
+            new_dict.__dict__.update(self.__dict__)
+            new_dict.dct = copy.copy(self.dct)
+            return new_dict
+
+        def __deepcopy__(self, memo):
+            new_dict = type(self)()
+            new_dict.__dict__.update(self.__dict__)
+            new_dict.dct = copy.deepcopy(self.dct)
+            return new_dict
+
+    return Interface
+
+
+def TypedList(elem_type_func):
+    """
+    Returns a class (not an instance) that will provide a list-like
+    interface with runtime type checks.
+
+    Note: The types are provided lazily via a callable to avoid unnecessary dependencies
+    on types from external packages at import-time.
+
+    Args:
+        elem_type_func (Callable() -> type):
+                A callable that returns the expected list-element type.
+    """
+
+    class Interface:
+        def __init__(self, lst=None):
+            self.lst = util.default(lst, [])
+            self.elem_type = elem_type_func()
+
+        def _check_type(self, elem):
+            if not isinstance(elem, self.elem_type):
+                G_LOGGER.critical(
+                    f"Unsupported element type type in {type(self).__name__}. Element: {repr(elem)} is type: {type(elem).__name__} but type: {self.elem_type.__name__} was expected"
+                )
+
+        def __contains__(self, key):
+            return key in self.lst
+
+        def __getitem__(self, index):
+            return self.lst[index]
+
+        def __setitem__(self, index, elem):
+            self._check_type(elem)
+            self.lst[index] = elem
+
+        def __str__(self):
+            return str(self.lst)
+
+        def __repr__(self):
+            return repr(self.lst)
+
+        def append(self, elem):
+            self._check_type(elem)
+            return self.lst.append(elem)
+
+        def extend(self, elems):
+            for elem in elems:
+                self._check_type(elem)
+            return self.lst.extend(elems)
+
+        def __iadd__(self, elems):
+            for elem in elems:
+                self._check_type(elem)
+            self.lst += elems
+
+        def __len__(self):
+            return len(self.lst)
+
+        def __eq__(self, other):
+            return self.lst == other.lst
+
+        def __iter__(self):
+            return self.lst.__iter__()
+
+        def __copy__(self):
+            new_list = type(self)()
+            new_list.__dict__.update(self.__dict__)
+            new_list.lst = copy.copy(self.lst)
+            return new_list
+
+        def __deepcopy__(self, memo):
+            new_list = type(self)()
+            new_list.__dict__.update(self.__dict__)
+            new_list.lst = copy.deepcopy(self.lst)
+            return new_list
+
+    return Interface

tools/Polygraphy/polygraphy/common/struct.py

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+#
+# 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 import mod, util
+from polygraphy.common.interface import TypedDict
+from polygraphy.datatype import DataType
+from polygraphy.json import Decoder, Encoder, add_json_methods
+
+
+class BoundedShape(list):
+    """
+    Represents a shape with min/max bounds.
+    """
+
+    def __init__(self, shape, min=None, max=None):
+        super().__init__(shape)
+        self.min = min
+        self.max = max
+
+    def __repr__(self):
+        return f"BoundedShape({list(self)}, min={self.min}, max={self.max})"
+
+
+class MetadataTuple:
+    def __init__(self, dtype, shape, docstring):
+        self.dtype = dtype
+        self.shape = shape
+        self.docstring = docstring
+
+    @property
+    def dtype(self):
+        return self._dtype
+
+    @dtype.setter
+    def dtype(self, new):
+        self._dtype = DataType.from_dtype(new) if new is not None else None
+
+    def __iter__(self):
+        yield from [self.dtype, self.shape]
+
+    def __repr__(self):
+        return f"MetadataTuple({self.dtype}, {self.shape}, {self.docstring})"
+
+    def __str__(self):
+        ret = ""
+        meta_items = []
+        if self.dtype is not None:
+            meta_items.append(f"dtype={self.dtype}")
+        if self.shape is not None:
+            meta_items.append(f"shape={tuple(self.shape)}")
+        if self.docstring:
+            meta_items.append(self.docstring)
+        if meta_items:
+            ret += "[" + ", ".join(meta_items) + "]"
+        return ret
+
+    def __eq__(self, other):
+        return self.shape == other.shape and self.dtype == other.dtype
+
+
+@mod.export()
+class TensorMetadata(TypedDict(lambda: str, lambda: MetadataTuple)):
+    """
+    An OrderedDict[str, MetadataTuple] that maps input names to their data types and shapes.
+
+    Shapes may include negative values, ``None``, or strings to indicate dynamic dimensions.
+
+    Example:
+    ::
+
+        shape = tensor_meta["input0"].shape
+        dtype = tensor_meta["input0"].dtype
+    """
+
+    @staticmethod
+    def from_feed_dict(feed_dict):
+        """
+        Constructs a new TensorMetadata using information from the provided feed_dict.
+
+        Args:
+            feed_dict (OrderedDict[str, Union[numpy.ndarray, torch.tensor]]):
+                    A mapping of input tensor names to corresponding input arrays.
+
+        Returns:
+            TensorMetadata
+        """
+        meta = TensorMetadata()
+        for name, arr in feed_dict.items():
+            meta.add(name, util.array.dtype(arr), util.array.shape(arr))
+        return meta
+
+    def add(self, name, dtype, shape, min_shape=None, max_shape=None, docstring=None):
+        """
+        Convenience function for adding entries.
+
+        Args:
+            name (str): The name of the input.
+            dtype (Any):
+                    The data type of the input.
+                    This can be any type that can be converted to a Polygraphy DataType.
+            shape (Sequence[Union[int, str]]]):
+                    The shape of the input. Dynamic dimensions may
+                    be indicated by negative values, ``None``, or a string.
+
+            min_shape (Sequence[int]):
+                    The minimum valid shape for the input.
+                    If provided, this shape should not include any dynamic dimensions.
+            max_shape (Sequence[int]):
+                    The maximum valid shape for the input.
+                    If provided, this shape should not include any dynamic dimensions.
+            docstring (str):
+                    Any additional information associated with a tensor.
+
+        Returns:
+            The newly added entry.
+        """
+        self[name] = MetadataTuple(
+            dtype,
+            (
+                BoundedShape(shape, min=min_shape, max=max_shape)
+                if shape is not None
+                else None
+            ),
+            docstring,
+        )
+        return self
+
+    def __repr__(self):
+        ret = "TensorMetadata()"
+        for name, metadata_tuple in self.items():
+            (dtype, shape) = metadata_tuple
+            ret += util.make_repr(
+                ".add",
+                name,
+                dtype,
+                list(shape),
+                min_shape=shape.min,
+                max_shape=shape.max,
+                docstring=metadata_tuple.docstring,
+            )[0]
+        return ret
+
+    def __str__(self):
+        sep = ",\n "
+        elems = [f"{name} {meta_tuple}".strip() for name, meta_tuple in self.items()]
+        return "{" + sep.join(elems) + "}"
+
+
+@mod.export()
+@add_json_methods("formatted array")
+class FormattedArray:
+    """
+    [EXPERIMENTAL, UNTESTED] This API is experimental and untested and may be significantly
+    modified in future releases. Use with caution!
+
+    Representes an array whose semantic shape differs from its physical size in memory.
+
+    For example, consider an ``NCHW`` tensor of shape ``(1, 3, 28, 28)``. If we use a vectorized format
+    like ``N(C/4)HW4``, then the physical size of the array would be ``(1, 1, 28, 28 * 4)`` since
+    the channel dimension would be padded to a multiple of 4. However, we still need a way to keep
+    track of the semantic shape for things like shape inference.
+
+    This class provides a mechanism to specify the shape of an array independently of
+    the underlying array.
+    """
+
+    def __init__(self, array, shape):
+        """
+        Args:
+            array (Union[np.ndarray, polygraphy.cuda.DeviceView]):
+                    The array. In most cases, this will be a raw byte-array.
+            shape (Sequence[int]):
+                    The semantic shape of the data.
+        """
+        self.array = array
+        self.shape = shape
+
+
+@Encoder.register(FormattedArray)
+def encode(farray):
+    return {
+        "array": farray.array,
+        "shape": farray.shape,
+    }
+
+
+@Decoder.register(FormattedArray)
+def decode(dct):
+    return FormattedArray(dct["array"], dct["shape"])