#
# 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 ctypes
import time
from polygraphy import constants, mod, util
from polygraphy.backend.base import BaseLoader
from polygraphy.backend.trt import util as trt_util
from polygraphy.backend.trt.config import CreateConfig
from polygraphy.datatype import DataType
from polygraphy.logger import G_LOGGER
trt = mod.lazy_import("tensorrt>=8.5")
gs = mod.lazy_import("onnx_graphsurgeon")
np = mod.lazy_import("numpy")
[docs]
@mod.export(funcify=True)
class LoadPlugins(BaseLoader):
"""
A passthrough loader that loads plugins from the specified paths.
Passthrough here means that it can be used to wrap any other loader. The purpose of wrapping
another loader is that you can control the order of execution when lazily evaluating.
For immediate evaluation, use `load_plugins` instead:
::
load_plugins(plugins=["/path/to/my/plugin.so", "/path/to/my/other_plugin.so"])
"""
def __init__(self, plugins=None, obj=None):
"""
Loads plugins from the specified paths.
Args:
plugins (List[str]):
A list of paths to plugin libraries to load before inference.
obj :
An object or callable to return or call respectively.
If ``obj`` is callable, extra parameters will be forwarded to ``obj``.
If ``obj`` is not callable, it will be returned.
"""
self.plugins = util.default(plugins, [])
self.obj = obj
[docs]
@util.check_called_by("__call__")
def call_impl(self, *args, **kwargs):
"""
Returns:
object:
The provided ``obj`` argument, or its return value if it is
callable. Returns ``None`` if ``obj`` was not set.
"""
for plugin in self.plugins:
G_LOGGER.info(f"Loading plugin library: {plugin}")
ctypes.CDLL(plugin)
ret, _ = util.invoke_if_callable(self.obj, *args, **kwargs)
return ret
[docs]
@mod.export(funcify=True)
class CreateNetwork(BaseLoader):
"""
Functor that creates an empty TensorRT network.
"""
def __init__(self, explicit_batch=None, strongly_typed=None):
"""
Creates an empty TensorRT network.
Args:
explicit_batch (bool):
Whether to create the network with explicit batch mode.
Defaults to True.
strongly_typed (bool):
Whether to mark the network as being strongly typed.
Defaults to False.
"""
self.explicit_batch = util.default(
explicit_batch,
True if mod.version(trt.__version__) < mod.version("10.0") else None,
)
self.strongly_typed = util.default(strongly_typed, False)
[docs]
@util.check_called_by("__call__")
def call_impl(self):
"""
Returns:
(trt.Builder, trt.INetworkDefinition): The builder and empty network.
"""
builder = trt.Builder(trt_util.get_trt_logger())
network_flags = 0
if self.explicit_batch:
try:
network_flags |= 1 << int(
trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH
)
except AttributeError:
trt_util.fail_unavailable("explicit_batch")
if self.strongly_typed:
try:
network_flags |= 1 << int(
trt.NetworkDefinitionCreationFlag.STRONGLY_TYPED
)
except AttributeError:
trt_util.fail_unavailable("strongly_typed")
network = builder.create_network(flags=network_flags)
if network is None:
G_LOGGER.critical("Invalid network. See logging output above for details.")
return builder, network
class BaseNetworkFromOnnx(BaseLoader):
def __init__(self, flags=None, plugin_instancenorm=None, strongly_typed=None):
"""
Args:
flags (List[trt.OnnxParserFlag]):
A list of ``OnnxParserFlag`` s to modify the default parsing
behavior of the ONNX parser.
Defaults to None.
plugin_instancenorm (bool):
Whether to force usage of the plugin implementation of ONNX
InstanceNorm by clearing the NATIVE_INSTANCENORM flag in the parser.
Defaults to False
strongly_typed (bool):
Whether to mark the network as being strongly typed.
Defaults to False.
"""
self.flags = flags
self.plugin_instancenorm = util.default(plugin_instancenorm, False)
self.strongly_typed = util.default(strongly_typed, False)
@util.check_called_by("__call__")
def call_impl(self):
builder, network = create_network(strongly_typed=self.strongly_typed)
# Initialize plugin library for the parser.
trt.init_libnvinfer_plugins(trt_util.get_trt_logger(), "")
parser = trt.OnnxParser(network, trt_util.get_trt_logger())
# Set flags if applicable.
if mod.version(trt.__version__) >= mod.version("8.6"):
if self.flags:
masked_flags = 0
for f in self.flags:
masked_flags |= 1 << int(f)
parser.flags = masked_flags
if self.plugin_instancenorm:
parser.clear_flag(trt.OnnxParserFlag.NATIVE_INSTANCENORM)
return builder, network, parser
[docs]
@mod.export(funcify=True)
class NetworkFromOnnxBytes(BaseNetworkFromOnnx):
"""
Functor that parses an ONNX model to create a trt.INetworkDefinition.
"""
def __init__(
self, model_bytes, flags=None, plugin_instancenorm=None, strongly_typed=None
):
"""
Parses an ONNX model.
Args:
model_bytes (Union[bytes, Callable() -> bytes]):
A serialized ONNX model or a callable that returns one.
flags (List[trt.OnnxParserFlag])
A list of ``OnnxParserFlag`` s to modify the default parsing
behavior of the ONNX parser.
Defaults to None.
plugin_instancenorm (bool):
Whether to force usage of the plugin implementation of ONNX
InstanceNorm by clearing the NATIVE_INSTANCENORM flag in the parser.
Defaults to False
strongly_typed (bool):
Whether to mark the network as being strongly typed.
Defaults to False.
"""
super().__init__(
flags=flags,
plugin_instancenorm=plugin_instancenorm,
strongly_typed=strongly_typed,
)
self._model_bytes = model_bytes
[docs]
@util.check_called_by("__call__")
def call_impl(self):
"""
Returns:
(trt.IBuilder, trt.INetworkDefinition, trt.OnnxParser):
A TensorRT network, as well as the builder used to create it, and the parser
used to populate it.
"""
builder, network, parser = super().call_impl()
success = parser.parse(util.invoke_if_callable(self._model_bytes)[0])
trt_util.check_onnx_parser_errors(parser, success)
return builder, network, parser
[docs]
@mod.export(funcify=True)
class NetworkFromOnnxPath(BaseNetworkFromOnnx):
"""
Functor that parses an ONNX model to create a trt.INetworkDefinition.
This loader supports models with weights stored in an external location.
"""
def __init__(self, path, flags=None, plugin_instancenorm=None, strongly_typed=None):
"""
Parses an ONNX model from a file.
Args:
path (str): The path from which to load the model.
flags (List[trt.OnnxParserFlag]):
A list of ``OnnxParserFlag`` s to modify the default parsing
behavior of the ONNX parser.
Defaults to None.
plugin_instancenorm (bool):
Whether to force usage of the plugin implementation of ONNX
InstanceNorm by clearing the NATIVE_INSTANCENORM flag in the parser.
Defaults to False
strongly_typed (bool):
Whether to mark the network as being strongly typed.
Defaults to False.
"""
super().__init__(
flags=flags,
plugin_instancenorm=plugin_instancenorm,
strongly_typed=strongly_typed,
)
self.path = path
[docs]
@util.check_called_by("__call__")
def call_impl(self):
"""
Returns:
(trt.IBuilder, trt.INetworkDefinition, trt.OnnxParser):
A TensorRT network, as well as the builder used to create it, and the parser
used to populate it.
"""
path = util.invoke_if_callable(self.path)[0]
builder, network, parser = super().call_impl()
# We need to use parse_from_file for the ONNX parser to keep track of the location of the ONNX file for
# potentially parsing any external weights.
success = parser.parse_from_file(path)
trt_util.check_onnx_parser_errors(parser, success)
return builder, network, parser
[docs]
@mod.export(funcify=True)
class PostprocessNetwork(BaseLoader):
"""
[EXPERIMENTAL] Functor that applies a given post-processing function to a TensorRT ``INetworkDefinition``.
"""
def __init__(self, network, func, name=None):
"""
Applies a given post-processing function to a TensorRT ``INetworkDefinition``.
Args:
network (Union[Tuple[trt.Builder, trt.INetworkDefinition, Optional[parser]], Callable() -> Tuple[trt.Builder, trt.INetworkDefinition, Optional[parser]]):
A tuple containing a TensorRT builder, network and optionally parser or a callable that returns one.
To omit the parser, return a tuple containing just the builder and network.
func (Callable[[trt.INetworkDefinition], None])
A callable which accepts a named `network` argument. `PostprocessNetwork` will pass in the parsed network via this argument, which can then be modified by the callable.
name (Optional[str])
The name of this postprocessing step, used for logging purposes.
"""
self._network = network
# Sanity-check that the function passed in is callable
if not callable(func):
G_LOGGER.critical(
f"Object {func} (of type {type(func)}) is not a callable."
)
try:
func_name = func.__name__
except:
func_name = str(func)
self._func = func
self.name = util.default(name, func_name)
[docs]
@util.check_called_by("__call__")
def call_impl(self):
"""
Returns:
Tuple[trt.Builder, trt.INetworkDefinition, Optional[parser]]:
The modified network along with the builder and parser if provided.
"""
ret, _ = util.invoke_if_callable(self._network)
builder, network, parser = util.unpack_args(ret, num=3)
G_LOGGER.verbose(f"Executing postprocessing step [{self.name}]")
self._func(network=network)
if parser is None:
return builder, network
return builder, network, parser
[docs]
@mod.export(funcify=True)
class ModifyNetworkOutputs(PostprocessNetwork):
"""
Functor that modifies outputs in a TensorRT ``INetworkDefinition``.
"""
@staticmethod
def _apply(network, outputs, exclude_outputs):
if outputs == constants.MARK_ALL:
trt_util.mark_layerwise(network)
elif outputs is not None:
trt_util.mark_outputs(network, outputs)
if exclude_outputs is not None:
trt_util.unmark_outputs(network, exclude_outputs)
return network
def __init__(self, network, outputs=None, exclude_outputs=None):
"""
Modifies outputs in a TensorRT ``INetworkDefinition``.
Args:
network (Union[Tuple[trt.Builder, trt.INetworkDefinition, Optional[parser]], Callable() -> Tuple[trt.Builder, trt.INetworkDefinition, Optional[parser]]):
A tuple containing a TensorRT builder, network and optionally parser or a callable that returns one.
To omit the parser, return a tuple containing just the builder and network.
outputs (Sequence[str]):
Names of tensors to mark as outputs. If provided, this will override the outputs
already marked in the network.
If a value of `constants.MARK_ALL` is used instead of a list, all tensors in the network are marked.
exclude_outputs (Sequence[str]):
Names of tensors to exclude as outputs. This can be useful in conjunction with
``outputs=constants.MARK_ALL`` to omit outputs.
"""
func = lambda network: ModifyNetworkOutputs._apply(
network, outputs, exclude_outputs
)
super().__init__(network, func, "ModifyNetworkOutputs")
[docs]
@mod.export(funcify=True)
class SetLayerPrecisions(PostprocessNetwork):
"""
Functor that sets layer precisions in a TensorRT ``INetworkDefinition``.
"""
@staticmethod
def _apply(network, layer_precisions):
util.check_sequence_contains(
[layer.name for layer in network],
layer_precisions.keys(),
name="the network",
items_name="layers",
check_extra=False,
log_func=G_LOGGER.warning,
)
for layer in network:
if layer.name in layer_precisions:
layer.precision = layer_precisions[layer.name]
return network
def __init__(self, network, layer_precisions):
"""
Sets layer precisions in a TensorRT ``INetworkDefinition``.
Args:
network (Union[Tuple[trt.Builder, trt.INetworkDefinition, Optional[parser]], Callable() -> Tuple[trt.Builder, trt.INetworkDefinition, Optional[parser]]):
A tuple containing a TensorRT builder, network and optionally parser or a callable that returns one.
To omit the parser, return a tuple containing just the builder and network.
layer_precisions (Dict[str, trt.DataType]):
A mapping of layer names to their desired compute precision.
"""
func = lambda network: SetLayerPrecisions._apply(network, layer_precisions)
super().__init__(network, func, "SetLayerPrecisions")
[docs]
@mod.export(funcify=True)
class SetTensorDatatypes(PostprocessNetwork):
"""
Functor that sets tensor datatypes for network I/O tensors in a TensorRT ``INetworkDefinition``.
"""
@staticmethod
def _apply(network, tensor_datatypes):
tensor_map = trt_util.get_all_tensors(network)
util.check_sequence_contains(
tensor_map.keys(),
tensor_datatypes.keys(),
name="the network",
items_name="tensors",
check_extra=False,
log_func=G_LOGGER.warning,
)
for name, dtype in tensor_datatypes.items():
tensor_map[name].dtype = dtype
return network
def __init__(self, network, tensor_datatypes):
"""
Sets network I/O tensor datatypes in a TensorRT ``INetworkDefinition``.
Args:
network (Union[Tuple[trt.Builder, trt.INetworkDefinition, Optional[parser]], Callable() -> Tuple[trt.Builder, trt.INetworkDefinition, Optional[parser]]):
A tuple containing a TensorRT builder, network and optionally parser or a callable that returns one.
To omit the parser, return a tuple containing just the builder and network.
tensor_datatypes (Dict[str, trt.DataType]):
A mapping of tensor names to their desired data types.
"""
func = lambda network: SetTensorDatatypes._apply(network, tensor_datatypes)
super().__init__(network, func, "SetTensorDatatypes")
[docs]
@mod.export(funcify=True)
class LoadRuntime(BaseLoader):
"""
Functor that loads a TensorRT ``IRuntime``.
"""
def __init__(self, path):
"""
Loads a TensorRT ``IRuntime``.
The loaded runtime can be used to execute a version compatible engine
that excludes the lean runtime.
Args:
path (str): The path to a shared library from which to load the runtime.
"""
self.path = path
[docs]
@util.check_called_by("__call__")
def call_impl(self):
"""
Returns:
trt.Runtime: The runtime that was loaded.
"""
bootstrap_runtime = trt.Runtime(trt_util.get_trt_logger())
G_LOGGER.info(f"Loading TensorRT runtime from: {self.path}")
return bootstrap_runtime.load_runtime(self.path)
[docs]
@mod.export(funcify=True)
class EngineBytesFromNetwork(BaseLoader):
"""
Functor that uses a TensorRT ``INetworkDefinition`` to build a serialized engine.
"""
def __init__(self, network, config=None, save_timing_cache=None):
"""
Builds and serializes TensorRT engine.
Args:
network (Union[Tuple[trt.Builder, trt.INetworkDefinition, Optional[parser]], Callable() -> Tuple[trt.Builder, trt.INetworkDefinition, Optional[parser]]):
A tuple containing a TensorRT builder, network and optionally parser or a callable that returns one.
To omit the parser, return a tuple containing just the builder and network.
config (Callable(trt.Builder, trt.INetworkDefinition) -> trt.IBuilderConfig):
A TensorRT builder configuration or a callable that returns one. If not supplied,
a `CreateConfig` instance with default parameters is used.
save_timing_cache (Union[str, file-like]):
A path or file-like object at which to save a tactic timing cache.
Any existing cache will be appended to.
If a path is provided, the file will be locked for exclusive access to prevent
multiple processes from attempting to update the timing cache at the same time.
"""
self._network = network
self._config = util.default(config, CreateConfig())
self.timing_cache_path = save_timing_cache
[docs]
@util.check_called_by("__call__")
def call_impl(self):
"""
Returns:
bytes: The serialized engine that was created.
"""
# If network is a callable, then we own its return value
ret, _ = util.invoke_if_callable(self._network)
builder, network, _ = util.unpack_args(ret, num=3)
if builder is None or network is None:
G_LOGGER.critical(
f"Expected to recevie a (builder, network) tuple for the `network` parameter, but received: ({builder}, {network})"
)
config, _ = util.invoke_if_callable(self._config, builder, network)
trt_util.try_setup_polygraphy_calibrator(config, network)
G_LOGGER.super_verbose(
lambda: (
"Displaying TensorRT Network:\n"
+ trt_util.str_from_network(
network,
show_layers=True,
show_attrs=True,
show_weights=G_LOGGER.module_severity.get(
G_LOGGER.module_path(__file__)
)
<= G_LOGGER.ULTRA_VERBOSE,
)
)
)
G_LOGGER.start(
f"Building engine with configuration:\n{trt_util.str_from_config(config)}"
)
start_time = time.time()
try:
engine_bytes = builder.build_serialized_network(network, config)
except AttributeError:
engine = builder.build_engine(network, config)
if not engine:
G_LOGGER.critical(
"Invalid Engine. Please ensure the engine was built correctly"
)
engine_bytes = engine.serialize()
end_time = time.time()
if not engine_bytes:
G_LOGGER.critical(
"Invalid Engine. Please ensure the engine was built correctly"
)
G_LOGGER.finish(
f"Finished engine building in {end_time - start_time:.3f} seconds"
)
if self.timing_cache_path:
try:
timing_cache = config.get_timing_cache()
except AttributeError:
trt_util.fail_unavailable("save_timing_cache in EngineBytesFromNetwork")
with util.LockFile(self.timing_cache_path):
try:
prev_cache = config.create_timing_cache(
util.load_file(self.timing_cache_path)
)
except:
prev_cache = None
if timing_cache:
if prev_cache is not None:
combine_success = timing_cache.combine(
prev_cache, ignore_mismatch=True
)
if not combine_success:
G_LOGGER.warning(
"Could not combine old timing cache into current timing cache"
)
with timing_cache.serialize() as buffer:
util.save_file(
buffer,
self.timing_cache_path,
description="tactic timing cache",
)
return engine_bytes
[docs]
@mod.export(funcify=True)
class EngineFromNetwork(EngineBytesFromNetwork):
"""
Functor similar to EngineBytesFromNetwork, but deserializes the engine before returning.
"""
def __init__(self, network, config=None, save_timing_cache=None, runtime=None):
"""
Builds a TensorRT serialized engine and then deserializes it.
Args:
network (Union[Tuple[trt.Builder, trt.INetworkDefinition, Optional[parser]], Callable() -> Tuple[trt.Builder, trt.INetworkDefinition, Optional[parser]]):
A tuple containing a TensorRT builder, network and optionally parser or a callable that returns one.
To omit the parser, return a tuple containing just the builder and network.
config (Callable(trt.Builder, trt.INetworkDefinition) -> trt.IBuilderConfig):
A TensorRT builder configuration or a callable that returns one. If not supplied,
a `CreateConfig` instance with default parameters is used.
save_timing_cache (Union[str, file-like]):
A path or file-like object at which to save a tactic timing cache.
Any existing cache will be appended to.
If a path is provided, the file will be locked for exclusive access to prevent
multiple processes from attempting to update the timing cache at the same time.
runtime (Union[trt.Runtime, Callable() -> trt.Runtime]):
The runtime to use when deserializing the engine or a callable that returns one.
If no runtime is provided, one will be created.
"""
super().__init__(network, config, save_timing_cache)
self._runtime = runtime
[docs]
@util.check_called_by("__call__")
def call_impl(self):
"""
Returns:
trt.ICudaEngine: The engine that was created.
"""
# We do not invoke super().call_impl here because we would otherwise be responsible
# for freeing it's return values.
return engine_from_bytes(super().call_impl, runtime=self._runtime)
[docs]
@mod.export(funcify=True)
class EngineFromBytes(BaseLoader):
"""
Functor that deserializes an engine from a buffer.
"""
def __init__(self, serialized_engine, runtime=None):
"""
Deserializes an engine from a buffer.
Args:
serialized_engine (Union[Union[str, bytes], Callable() -> Union[str, bytes]]):
The serialized engine bytes or a callable that returns them.
runtime (Union[trt.Runtime, Callable() -> trt.Runtime]):
The runtime to use when deserializing the engine or a callable that returns one.
If no runtime is provided, one will be created.
"""
self._serialized_engine = serialized_engine
self._runtime = util.default(
runtime, lambda: trt.Runtime(trt_util.get_trt_logger())
)
[docs]
@util.check_called_by("__call__")
def call_impl(self):
"""
Returns:
trt.ICudaEngine: The deserialized engine.
"""
buffer, _ = util.invoke_if_callable(self._serialized_engine)
runtime, _ = util.invoke_if_callable(self._runtime)
trt.init_libnvinfer_plugins(trt_util.get_trt_logger(), "")
try:
# To deserialize version compatible engines, we must signal the runtime that host code is allowed
runtime.engine_host_code_allowed = True
except AttributeError:
pass
engine = runtime.deserialize_cuda_engine(buffer)
if not engine:
G_LOGGER.critical("Could not deserialize engine. See log for details.")
return engine
[docs]
@mod.export(funcify=True)
class BytesFromEngine(BaseLoader):
"""
Functor that serializes an engine.
"""
def __init__(self, engine):
"""
Serializes an engine.
Args:
engine (Union[trt.ICudaEngine, Callable() -> trt.ICudaEngine]):
An engine or a callable that returns one.
"""
self._engine = engine
[docs]
@util.check_called_by("__call__")
def call_impl(self):
"""
Returns:
bytes: The serialized engine.
"""
engine, _ = util.invoke_if_callable(self._engine)
return bytes(engine.serialize())
[docs]
@mod.export(funcify=True)
class SaveEngine(BaseLoader):
"""
Functor that saves an engine to the provided path.
"""
def __init__(self, engine, path):
"""
Saves an engine to the provided path.
Args:
engine (Union[trt.ICudaEngine, Callable() -> trt.ICudaEngine]):
An engine or a callable that returns one.
path (str): The path at which to save the engine.
"""
self._engine = engine
self.path = path
[docs]
@util.check_called_by("__call__")
def call_impl(self):
"""
Returns:
trt.ICudaEngine: The engine that was saved.
"""
engine, _ = util.invoke_if_callable(self._engine)
util.save_file(
contents=bytes_from_engine(engine), dest=self.path, description="engine"
)
return engine
[docs]
@mod.export(funcify=True)
class OnnxLikeFromNetwork(BaseLoader):
"""
Functor that creates an ONNX-like, but **not** valid ONNX, model based on a TensorRT network.
"""
def __init__(self, network) -> None:
"""
[HIGHLY EXPERIMENTAL] Creates an ONNX-like, but **not** valid ONNX, model from a TensorRT network.
This uses the ONNX format, but generates nodes that are **not** valid ONNX operators.
Hence, this should be used **only** for visualization or debugging purposes.
The resulting model does **not** include enough information to faithfully reconstruct the TensorRT network,
but does preserve the structure of the network and many of the layer parameters.
Args:
network (Union[Tuple[trt.Builder, trt.INetworkDefinition, Optional[parser]], Callable() -> Tuple[trt.Builder, trt.INetworkDefinition, Optional[parser]]):
A tuple containing a TensorRT builder, network and optionally parser or a callable that returns one.
To omit the parser, return a tuple containing just the builder and network.
"""
self._network = network
[docs]
@util.check_called_by("__call__")
def call_impl(self):
"""
Returns:
onnx.ModelProto: The ONNX-like, but **not** valid ONNX, representation of the TensorRT network.
"""
ret, _ = util.invoke_if_callable(self._network)
builder, network, _ = util.unpack_args(ret, num=3)
if builder is None or network is None:
G_LOGGER.critical(
f"Expected to recevie a (builder, network) tuple for the `network` parameter, but received: ({builder}, {network})"
)
tensor_map = {}
def tensors_from_names_meta(names, meta):
nonlocal tensor_map
tensors = []
for name in names:
if name not in tensor_map:
dtype, shape = meta[name]
tensor_map[name] = gs.Variable(
name=name, dtype=DataType.to_dtype(dtype, "onnx"), shape=shape
)
tensors.append(tensor_map[name])
return tensors
nodes = []
graph_inputs = tensors_from_names_meta(
*trt_util.get_network_input_names_meta(network)
)
graph_outputs = tensors_from_names_meta(
*trt_util.get_network_output_names_meta(network)
)
LAYER_TYPE_CLASS_MAPPING = trt_util.get_layer_class_mapping()
for layer in network:
op_name = layer.type.name
if layer.type in LAYER_TYPE_CLASS_MAPPING:
layer.__class__ = LAYER_TYPE_CLASS_MAPPING[layer.type]
node_inputs = tensors_from_names_meta(
*trt_util.get_layer_input_names_meta(layer)
)
node_outputs = tensors_from_names_meta(
*trt_util.get_layer_output_names_meta(layer)
)
attrs = {}
attr_names = trt_util.get_layer_attribute_names(layer)
for name in attr_names:
with G_LOGGER.verbosity():
try:
attr = getattr(layer, name)
except Exception as err:
attr = f"<Error: could not retrieve layer attribute: {name}. Note: Error was: {err}>"
if util.is_sequence(attr) or any(
isinstance(attr, cls) for cls in [trt.Dims, trt.Permutation]
):
try:
attr = list(attr)
except ValueError: # Invalid dims
attr = []
if hasattr(attr, "__entries"): # TensorRT Enums
attr = attr.name
if isinstance(attr, trt.ILoop):
attr = attr.name
VALID_TYPES = [np.ndarray, list, int, str, bool, float]
if not any(isinstance(attr, cls) for cls in VALID_TYPES):
G_LOGGER.internal_error(
f"Unknown type: {type(attr)} for layer attribute: {attr}.\nNote: Layer was: {layer}"
)
try:
attr = str(attr)
except:
attr = "<error during conversion>"
attrs[name] = attr
nodes.append(
gs.Node(
name=layer.name,
op=op_name,
attrs=attrs,
inputs=node_inputs,
outputs=node_outputs,
)
)
graph = gs.Graph(
name=network.name, inputs=graph_inputs, outputs=graph_outputs, nodes=nodes
)
return gs.export_onnx(graph)
[docs]
@mod.export(funcify=True)
class MarkDebug(PostprocessNetwork):
"""
Functor that mark tensors as debug tensors in a TensorRT ``INetworkDefinition``.
"""
@staticmethod
def _apply(network, mark_debug):
tensor_map = trt_util.get_all_tensors(network)
util.check_sequence_contains(
tensor_map.keys(),
mark_debug,
name="the network",
items_name="tensors",
check_extra=False,
log_func=G_LOGGER.warning,
)
for name in mark_debug:
network.mark_debug(tensor_map[name])
return network
def __init__(self, network, mark_debug):
"""
Mark tensors as debug tensors in a TensorRT ``INetworkDefinition``.
Args:
network (Union[Tuple[trt.Builder, trt.INetworkDefinition, Optional[parser]], Callable() -> Tuple[trt.Builder, trt.INetworkDefinition, Optional[parser]]):
A tuple containing a TensorRT builder, network and optionally parser or a callable that returns one.
To omit the parser, return a tuple containing just the builder and network.
mark_debug (List[str]):
List of tensor names to mark as debug tensors.
"""
func = lambda network: MarkDebug._apply(network, mark_debug)
super().__init__(network, func, "MarkDebug")