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# SPDX-License-Identifier: Apache-2.0
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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import contextlib
from collections import OrderedDict
from polygraphy import mod, util
from polygraphy.exception import PolygraphyException
from polygraphy.logger import G_LOGGER, LogMode
from polygraphy.backend.trt import util as trt_util
from polygraphy.backend.base import util as base_util
trt = mod.lazy_import("tensorrt>=8.5")
np = mod.lazy_import("numpy")
[docs]
@mod.export()
def Calibrator(
data_loader,
cache=None,
BaseClass=None,
batch_size=None,
quantile=None,
regression_cutoff=None,
algo=None,
):
"""
Supplies calibration data to TensorRT to calibrate the network for INT8 inference.
Args:
data_loader (Sequence[OrderedDict[str, Union[numpy.ndarray, DeviceView, torch.Tensor, int]]]):
A generator or iterable that yields a dictionary that maps input names to NumPy
arrays, Polygraphy DeviceViews, PyTorch tensors, or GPU pointers. If NumPy arrays,
DeviceViews, or PyTorch tensors are provided, the calibrator will check the data types
and shapes if possible to ensure that they match those expected by the model.
In case you don't know details about the inputs ahead of time, you can access the
`input_metadata` property in your data loader, which will be set to a ``TensorMetadata``
instance by Polygraphy APIs like ``CreateConfig`` and ``EngineFromNetwork``.
Note that this does not work for generators or lists.
The number of calibration batches is controlled by the number of items supplied
by the data loader.
cache (Union[str, file-like]):
Path or file-like object to save/load the calibration cache.
By default, the calibration cache is not saved.
BaseClass (type):
The type of calibrator to inherit from.
Defaults to ``trt.IInt8EntropyCalibrator2``.
batch_size (int):
[DEPRECATED] The size of each batch provided by the data loader.
quantile (float):
The quantile to use for ``trt.IInt8LegacyCalibrator``.
Has no effect for other calibrator types.
Defaults to 0.5.
regression_cutoff (float):
The regression cutoff to use for ``trt.IInt8LegacyCalibrator``.
Has no effect for other calibrator types.
Defaults to 0.5.
algo (trt.CalibrationAlgoType):
Calibration algorithm to use for ``trt.IInt8Calibrator``.
Has no effect for other calibrator types.
Defaults to ``trt.CalibrationAlgoType.MINMAX_CALIBRATION``.
"""
BaseClass = util.default(BaseClass, trt.IInt8EntropyCalibrator2)
class CalibratorClass(BaseClass):
"""
Calibrator that supplies calibration data to TensorRT to calibrate the network for INT8 inference.
"""
def __init__(self):
# Must explicitly initialize parent for any trampoline class! Will mysteriously segfault without this.
BaseClass.__init__(self) # type: ignore
self.data_loader = data_loader
self._cache = cache
self.device_buffers = OrderedDict()
self.input_metadata = None
self.reset()
G_LOGGER.verbose(f"Created calibrator [cache={self._cache}]")
self.batch_size = util.default(batch_size, 1)
self.is_polygraphy_calibrator = True
# The function that constructed this instance
self.make_func = Calibrator
def set_input_metadata(self, input_metadata):
"""
Sets the input metadata for the calibrator.
This is passed along to the data loader and is also used for
input data type and shape checks.
NOTE: This generally does not need to be called manually if the calibrator is being used
with Polygraphy's loaders, like ``CreateConfig`` or ``EngineFromNetwork``.
Args:
input_metadata (TensorMetadata):
Mapping of input names to their data types and shapes.
Passed along to the data loader if provided. This is required if
using Polygraphy's included `DataLoader` to provide calibration data,
or if data type and shape checking is desired.
"""
self.input_metadata = input_metadata
if input_metadata is not None:
with contextlib.suppress(AttributeError):
self.data_loader.input_metadata = input_metadata
def reset(self):
"""
Reset this calibrator for reuse.
The calibrator will clear any dynamic ranges cached from previous calibration runs, and will
attempt to rewind the data loader (note that generators cannot be rewound).
Typically, this is only required if the same calibrator is used for multiple different networks.
"""
# Attempt to reset data loader
self.data_loader_iter = iter(self.data_loader)
self.num_batches = 0
# Make sure calibrator will check the cache again when reset.
self.cache_contents = None
def get_batch_size(self):
return self.batch_size
def _get_batch_impl(self, names):
try:
buffers = next(self.data_loader_iter)
except StopIteration:
if not self.num_batches:
G_LOGGER.critical(
"Calibrator data loader provided no data.\nPossible reasons for this include:\n(1) data loader "
"has no data to provide\n(2) data loader was a generator, and the calibrator is being "
"used multiple times (generators cannot be rewound)"
)
return None
self.num_batches += 1
if self.input_metadata is not None:
base_util.check_inputs(buffers, self.input_metadata)
ptrs = []
for name in names:
buf = buffers[name]
if isinstance(buf, int):
ptrs.append(buf)
else:
ptrs.append(
trt_util._get_array_on_gpu(buf, name, self.device_buffers)
)
return ptrs
def get_batch(self, names):
ptrs = None
try:
ptrs = self._get_batch_impl(names)
except PolygraphyException:
pass
if ptrs is None:
self.free()
return ptrs
def read_calibration_cache(self):
def load_from_cache():
if self._cache is None or not util.get_file_size(self._cache):
return None
try:
return util.load_file(self._cache, description="calibration cache")
except Exception as err:
G_LOGGER.error(
f"Could not read from calibration cache: {self._cache}\nNote: Error was: {err}"
)
return None
if self.cache_contents is not None:
return self.cache_contents
self.cache_contents = load_from_cache()
if not self.cache_contents:
if self.cache_contents is not None:
G_LOGGER.warning(
"Calibration cache was provided, but is empty. "
"Will regenerate scales by running calibration.",
mode=LogMode.ONCE,
)
self.cache_contents = None
return self.cache_contents
def write_calibration_cache(self, cache):
self.cache_contents = cache.tobytes()
if self._cache is None:
return
try:
util.save_file(
contents=self.cache_contents,
dest=self._cache,
description="calibration cache",
)
except Exception as err:
G_LOGGER.error(
f"Could not write to calibration cache: {self._cache}.\nNote: Error was: {err}"
)
def free(self):
"""
Frees all device buffers associated with this calibrator
"""
for device_buffer in self.device_buffers.values():
device_buffer.free()
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
self.free()
# IInt8LegacyCalibrator methods
if BaseClass == trt.IInt8LegacyCalibrator:
def get_quantile(self):
return util.default(quantile, 0.5)
def get_regression_cutoff(self):
return util.default(regression_cutoff, 0.5)
def read_histogram_cache(self, length):
pass
def write_histogram_cache(self, ptr, length):
pass
# IInt8Calibrator methods
if BaseClass == trt.IInt8Calibrator:
def get_algorithm(self):
return util.default(algo, trt.CalibrationAlgoType.ENTROPY_CALIBRATION_2)
def __repr__(self):
return util.make_repr(
"Calibrator",
data_loader,
cache=cache,
BaseClass=BaseClass,
batch_size=batch_size,
quantile=quantile,
regression_cutoff=regression_cutoff,
algo=algo,
)[0]
return CalibratorClass()