wehub-resource-sync
216 lines
8.2 KiB
Python
216 lines
8.2 KiB
Python
#
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# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from polygraphy import constants, mod, util
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from polygraphy.backend.trt import util as trt_util
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from polygraphy.common.interface import TypedDict
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from polygraphy.logger import G_LOGGER, LogMode
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from fnmatch import fnmatch
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@mod.export()
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class ShapeTuple:
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"""
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Represents a set of shapes for a single binding in a profile.
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"""
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def __init__(self, min, opt, max):
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"""
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Args:
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min (Tuple[int]): The minimum shape that the profile will support.
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opt (Tuple[int]): The shape for which TensorRT will optimize the engine.
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max (Tuple[int]): The maximum shape that the profile will support.
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"""
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self.min = min
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self.opt = opt
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self.max = max
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def __str__(self):
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return f"(min={self.min}, opt={self.opt}, max={self.max})"
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def __repr__(self):
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return type(self).__name__ + self.__str__()
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def __iter__(self):
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yield from [self.min, self.opt, self.max]
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@mod.export()
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class Profile(TypedDict(lambda: str, lambda: ShapeTuple)):
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"""
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An ordered dictionary that represents a single optimization profile that
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can be used to build an engine.
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More specifically, it is an ``OrderedDict[str, ShapeTuple]`` which maps binding
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names to a set of min/opt/max shapes.
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"""
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def add(self, name, min, opt, max):
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"""
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A convenience function to add shapes for a single binding.
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Args:
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name (str): The name of the binding.
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min (Tuple[int]): The minimum shape that the profile will support.
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opt (Tuple[int]): The shape for which TensorRT will optimize the engine.
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max (Tuple[int]): The maximum shape that the profile will support.
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Returns:
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Profile:
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self, which allows this function to be easily chained to add multiple bindings,
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e.g., Profile().add(...).add(...)
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"""
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self[name] = ShapeTuple(min, opt, max)
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return self
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def __getitem__(self, key):
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"""
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Retrieves the shapes registered for a given input name.
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Returns:
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ShapeTuple:
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A named tuple including ``min``, ``opt``, and ``max`` members for the shapes
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corresponding to the input.
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"""
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if key not in self:
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G_LOGGER.critical(
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f"Binding: {key} does not have shapes set in this profile"
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)
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return super().__getitem__(key)
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def fill_defaults(self, network, default_shape_value=None):
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"""
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Fill this profile with sane default values for any bindings whose
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shapes have not been set explicitly.
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Args:
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network (trt.INetworkDefinition):
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The TensorRT network this profile is meant for.
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This will be used to determine model inputs and their shapes.
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default_shape_value (int):
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The value to use to override dynamic dimensions.
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Returns:
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Profile: Self
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"""
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default_shape_value = util.default(
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default_shape_value, constants.DEFAULT_SHAPE_VALUE
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)
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for idx in range(network.num_inputs):
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inp = network.get_input(idx)
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if any(fnmatch(inp.name, wc) for wc in self):
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continue
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with G_LOGGER.verbosity(G_LOGGER.CRITICAL): # WAR for spam from TRT
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is_shape_tensor = inp.is_shape_tensor
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if is_shape_tensor:
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rank = inp.shape[0] if len(inp.shape) > 0 else 1
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shape = (default_shape_value,) * rank
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G_LOGGER.warning(
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f"{trt_util.str_from_tensor(inp, is_shape_tensor)} | No values provided; "
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f"Will use input values: {shape} for min/opt/max in profile.\n",
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mode=LogMode.ONCE,
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)
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G_LOGGER.warning(
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"This will cause the shape-tensor to have static values. If this is incorrect, please "
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"set the range of values for this input shape-tensor.",
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mode=LogMode.ONCE,
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)
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else:
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shape = util.override_dynamic_shape(inp.shape, default_shape_value)
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if shape != inp.shape:
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G_LOGGER.warning(
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f"{trt_util.str_from_tensor(inp, is_shape_tensor)} | No shapes provided; Will use shape: {shape} for min/opt/max in profile.\n",
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mode=LogMode.ONCE,
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)
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G_LOGGER.warning(
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"This will cause the tensor to have a static shape. If this is incorrect, please "
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"set the range of shapes for this input tensor.",
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mode=LogMode.ONCE,
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)
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self.add(inp.name, shape, shape, shape)
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return self
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def to_trt(self, builder, network):
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"""
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Creates a TensorRT IOptimizationProfile based on the values set in this Profile.
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Args:
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builder (trt.Builder):
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A TensorRT builder. This will be used to construct the IOptimizationProfile.
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network (trt.INetworkDefinition):
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The TensorRT network the profile applies to.
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Returns:
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trt.IOptimizationProfile: A TensorRT optimization profile.
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"""
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trt_profile = builder.create_optimization_profile()
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unused_keys = set(self.keys())
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inp_names = [network.get_input(idx).name for idx in range(network.num_inputs)]
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name_to_key, unmatched_inps = util.match_keys(unused_keys, inp_names)
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if unmatched_inps:
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G_LOGGER.critical(
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f"Invalid inputs were provided to the optimization profile: {set(unmatched_inps)}\n"
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f"Note: Inputs available in the TensorRT network are: {set(inp_names)}"
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)
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for idx in range(network.num_inputs):
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inp = network.get_input(idx)
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key = name_to_key[inp.name] if inp.name in name_to_key else None
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with G_LOGGER.verbosity(): # WAR for spam from TRT
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is_shape_tensor = inp.is_shape_tensor
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if is_shape_tensor:
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if key:
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shapes = self[key]
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trt_profile.set_shape_input(
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inp.name, shapes.min, shapes.opt, shapes.max
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)
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G_LOGGER.verbose(
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f"{trt_util.str_from_tensor(inp, is_shape_tensor)} | Setting input shape-tensor value range to: {shapes}"
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)
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else:
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G_LOGGER.warning(
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f"{trt_util.str_from_tensor(inp, is_shape_tensor)} | No values provided. Assuming this is not a dynamic shape-tensor.",
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mode=LogMode.ONCE,
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)
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else:
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shapes = self[key if key else inp.name]
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trt_profile.set_shape(inp.name, shapes.min, shapes.opt, shapes.max)
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G_LOGGER.verbose(
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f"{trt_util.str_from_tensor(inp, is_shape_tensor)} | Setting input tensor shapes to: {shapes}"
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)
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return trt_util.check_profile(trt_profile)
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def __repr__(self):
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ret = "Profile()"
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for name, (min, opt, max) in self.items():
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ret += f".add('{name}', min={min}, opt={opt}, max={max})"
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return ret
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def __str__(self):
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elems = []
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for name, (min, opt, max) in self.items():
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elems.append(f"{name} [min={min}, opt={opt}, max={max}]")
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sep = ",\n "
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return "{" + sep.join(elems) + "}"
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