#
# 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.
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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
[docs]
@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]
[docs]
@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.
"""
[docs]
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
[docs]
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)
[docs]
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 inp.name 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
[docs]
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())
available_inputs = set()
for idx in range(network.num_inputs):
inp = network.get_input(idx)
if inp.name in unused_keys:
unused_keys.remove(inp.name)
available_inputs.add(inp.name)
with G_LOGGER.verbosity(): # WAR for spam from TRT
is_shape_tensor = inp.is_shape_tensor
if is_shape_tensor:
if inp.name in self:
shapes = self[inp.name]
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[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}"
)
if unused_keys:
G_LOGGER.critical(
f"Invalid inputs were provided to the optimization profile: {unused_keys}\n"
f"Note: Inputs available in the TensorRT network are: {available_inputs}"
)
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) + "}"