# # 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 constants, mod, util from polygraphy.backend.trt import util as trt_util from polygraphy.common.interface import TypedDict from polygraphy.logger import G_LOGGER, LogMode from fnmatch import fnmatch @mod.export() class ShapeTuple: """ Represents a set of shapes for a single binding in a profile. """ def __init__(self, min, opt, max): """ Args: min (Tuple[int]): The minimum shape that the profile will support. opt (Tuple[int]): The shape for which TensorRT will optimize the engine. max (Tuple[int]): The maximum shape that the profile will support. """ self.min = min self.opt = opt self.max = max def __str__(self): return f"(min={self.min}, opt={self.opt}, max={self.max})" def __repr__(self): return type(self).__name__ + self.__str__() def __iter__(self): yield from [self.min, self.opt, self.max] @mod.export() class Profile(TypedDict(lambda: str, lambda: ShapeTuple)): """ An ordered dictionary that represents a single optimization profile that can be used to build an engine. More specifically, it is an ``OrderedDict[str, ShapeTuple]`` which maps binding names to a set of min/opt/max shapes. """ def add(self, name, min, opt, max): """ A convenience function to add shapes for a single binding. Args: name (str): The name of the binding. min (Tuple[int]): The minimum shape that the profile will support. opt (Tuple[int]): The shape for which TensorRT will optimize the engine. max (Tuple[int]): The maximum shape that the profile will support. Returns: Profile: self, which allows this function to be easily chained to add multiple bindings, e.g., Profile().add(...).add(...) """ self[name] = ShapeTuple(min, opt, max) return self def __getitem__(self, key): """ Retrieves the shapes registered for a given input name. Returns: ShapeTuple: A named tuple including ``min``, ``opt``, and ``max`` members for the shapes corresponding to the input. """ if key not in self: G_LOGGER.critical( f"Binding: {key} does not have shapes set in this profile" ) return super().__getitem__(key) def fill_defaults(self, network, default_shape_value=None): """ Fill this profile with sane default values for any bindings whose shapes have not been set explicitly. Args: network (trt.INetworkDefinition): The TensorRT network this profile is meant for. This will be used to determine model inputs and their shapes. default_shape_value (int): The value to use to override dynamic dimensions. Returns: Profile: Self """ default_shape_value = util.default( default_shape_value, constants.DEFAULT_SHAPE_VALUE ) for idx in range(network.num_inputs): inp = network.get_input(idx) if any(fnmatch(inp.name, wc) for wc in self): continue with G_LOGGER.verbosity(G_LOGGER.CRITICAL): # WAR for spam from TRT is_shape_tensor = inp.is_shape_tensor if is_shape_tensor: rank = inp.shape[0] if len(inp.shape) > 0 else 1 shape = (default_shape_value,) * rank G_LOGGER.warning( f"{trt_util.str_from_tensor(inp, is_shape_tensor)} | No values provided; " f"Will use input values: {shape} for min/opt/max in profile.\n", mode=LogMode.ONCE, ) G_LOGGER.warning( "This will cause the shape-tensor to have static values. If this is incorrect, please " "set the range of values for this input shape-tensor.", mode=LogMode.ONCE, ) else: shape = util.override_dynamic_shape(inp.shape, default_shape_value) if shape != inp.shape: G_LOGGER.warning( f"{trt_util.str_from_tensor(inp, is_shape_tensor)} | No shapes provided; Will use shape: {shape} for min/opt/max in profile.\n", mode=LogMode.ONCE, ) G_LOGGER.warning( "This will cause the tensor to have a static shape. If this is incorrect, please " "set the range of shapes for this input tensor.", mode=LogMode.ONCE, ) self.add(inp.name, shape, shape, shape) return self def to_trt(self, builder, network): """ Creates a TensorRT IOptimizationProfile based on the values set in this Profile. Args: builder (trt.Builder): A TensorRT builder. This will be used to construct the IOptimizationProfile. network (trt.INetworkDefinition): The TensorRT network the profile applies to. Returns: trt.IOptimizationProfile: A TensorRT optimization profile. """ trt_profile = builder.create_optimization_profile() unused_keys = set(self.keys()) inp_names = [network.get_input(idx).name for idx in range(network.num_inputs)] name_to_key, unmatched_inps = util.match_keys(unused_keys, inp_names) if unmatched_inps: G_LOGGER.critical( f"Invalid inputs were provided to the optimization profile: {set(unmatched_inps)}\n" f"Note: Inputs available in the TensorRT network are: {set(inp_names)}" ) for idx in range(network.num_inputs): inp = network.get_input(idx) key = name_to_key[inp.name] if inp.name in name_to_key else None with G_LOGGER.verbosity(): # WAR for spam from TRT is_shape_tensor = inp.is_shape_tensor if is_shape_tensor: if key: shapes = self[key] trt_profile.set_shape_input( inp.name, shapes.min, shapes.opt, shapes.max ) G_LOGGER.verbose( f"{trt_util.str_from_tensor(inp, is_shape_tensor)} | Setting input shape-tensor value range to: {shapes}" ) else: G_LOGGER.warning( f"{trt_util.str_from_tensor(inp, is_shape_tensor)} | No values provided. Assuming this is not a dynamic shape-tensor.", mode=LogMode.ONCE, ) else: shapes = self[key if key else inp.name] trt_profile.set_shape(inp.name, shapes.min, shapes.opt, shapes.max) G_LOGGER.verbose( f"{trt_util.str_from_tensor(inp, is_shape_tensor)} | Setting input tensor shapes to: {shapes}" ) return trt_util.check_profile(trt_profile) def __repr__(self): ret = "Profile()" for name, (min, opt, max) in self.items(): ret += f".add('{name}', min={min}, opt={opt}, max={max})" return ret def __str__(self): elems = [] for name, (min, opt, max) in self.items(): elems.append(f"{name} [min={min}, opt={opt}, max={max}]") sep = ",\n " return "{" + sep.join(elems) + "}"