Source code for polygraphy.backend.trt.calibrator

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# SPDX-License-Identifier: Apache-2.0
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import contextlib
from collections import OrderedDict

from polygraphy import cuda, mod, util
from polygraphy.exception import PolygraphyException
from polygraphy.logger import G_LOGGER, LogMode

trt = mod.lazy_import("tensorrt")
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, int]]]): A generator or iterable that yields a dictionary that maps input names to NumPy arrays, Polygraphy DeviceViews, or GPU pointers. 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 an ``TensorMetadata`` instance. 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) self.data_loader = data_loader self._cache = cache self.device_buffers = OrderedDict() self.reset() G_LOGGER.verbose(f"Created calibrator [cache={self._cache}]") self.batch_size = util.default(batch_size, 1) # The function that constructed this instance self.make_func = Calibrator def reset(self, input_metadata=None): """ 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). Args: input_metadata (TensorMetadata): Mapping of input names to their data types and shapes. Passed along to the data loader if provided. Generally should not be required unless using Polygraphy's included `DataLoader` for this calibrator. """ self.input_metadata = input_metadata if input_metadata is not None: with contextlib.suppress(AttributeError): self.data_loader.input_metadata = input_metadata # 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 self.has_cached_scales = False 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 else: self.num_batches += 1 util.check_dict_contains(buffers, names, dict_name="calibration data", log_func=G_LOGGER.critical) def check_buffer(name, buffer): if self.input_metadata is None: return expected_dtype, expected_shape = self.input_metadata[name] err_prefix = "Received an unexpected input from the data loader during calibration. " if buffer.dtype != expected_dtype: G_LOGGER.critical( err_prefix + f"For input: '{name}', expected data type: {expected_dtype}, but received: {buffer.dtype}" ) if not util.is_valid_shape_override(buffer.shape, expected_shape): G_LOGGER.critical( err_prefix + f"For input: '{name}', expected a shape compatible with: {expected_shape}, but received: {buffer.shape}" ) ptrs = [] for name in names: buf = buffers[name] if isinstance(buf, cuda.DeviceView): check_buffer(name, buf) ptrs.append(buf.ptr) elif isinstance(buf, np.ndarray): check_buffer(name, buf) if name not in self.device_buffers: self.device_buffers[name] = cuda.DeviceArray(shape=buf.shape, dtype=buf.dtype) G_LOGGER.verbose(f"Allocated: {self.device_buffers[name]}") ptrs.append(self.device_buffers[name].copy_from(buf).ptr) elif isinstance(buf, int): ptrs.append(buf) else: G_LOGGER.critical( f"Calibration data loader provided an unrecognized type: {type(buf).__name__} for input: {name}.\nPlease provide either a NumPy array, Polygraphy DeviceView, or GPU pointer. " ) return ptrs def get_batch(self, names): ptrs = None try: ptrs = self._get_batch_impl(names) except PolygraphyException: pass if ptrs is None: 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 # Only attempt to read from the cache once. if self.has_cached_scales: 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 else: self.has_cached_scales = True return self.cache_contents def write_calibration_cache(self, cache): self.cache_contents = cache.tobytes() self.has_cached_scales = True 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(): def __enter__(self): return self def __exit__(self, exc_type, exc_value, traceback): # 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()