zhreshold commented on a change in pull request #15754: Add quantization
support for GluonCV
URL: https://github.com/apache/incubator-mxnet/pull/15754#discussion_r310777249
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File path: python/mxnet/contrib/quantization.py
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@@ -780,3 +820,170 @@ def calib_graph(qsym, arg_params, aux_params, collector,
qarg_params = _quantize_params(qsym, arg_params, th_dict)
return qsym, qarg_params, aux_params
+
+def quantize_net(network, quantized_dtype='auto', exclude_layers=None,
exclude_layers_match=None, calib_data=None,
+ data_shapes=None, calib_mode='none', num_calib_examples=None,
ctx=cpu(), logger=logging):
+ """User-level API for Gluon users to generate a quantized SymbolBlock from
a FP32 HybridBlock w/ or w/o calibration.
+ The backend quantized operators are only enabled for Linux systems. Please
do not run
+ inference using the quantized models on Windows for now.
+ The quantization implementation adopts the TensorFlow's approach:
+ https://www.tensorflow.org/performance/quantization.
+ The calibration implementation borrows the idea of Nvidia's 8-bit
Inference with TensorRT:
+
http://on-demand.gputechconf.com/gtc/2017/presentation/s7310-8-bit-inference-with-tensorrt.pdf
+ and adapts the method to MXNet.
+
+ Parameters
+ ----------
+ network : Gluon HybridBlock
+ Defines the structure of a neural network for FP32 data types.
+ quantized_dtype : str
+ The quantized destination type for input data. Currently support 'int8'
+ , 'uint8' and 'auto'. 'auto' means automatically select output type
according to calibration result.
+ Default value is 'int8'.
+ exclude_layers : list of strings
+ A list of strings representing the names of the symbols that users
want to excluding
+ exclude_layers_match : list of strings
+ A list of strings wildcard matching the names of the symbols that
users want to excluding
+ from being quantized.
+ calib_data : mx.io.DataIter or gluon.DataLoader
+ A iterable data loading object.
+ data_shapes : list
+ List of DataDesc, required if calib_data is not provided
+ calib_mode : str
+ If calib_mode='none', no calibration will be used and the thresholds
for
+ requantization after the corresponding layers will be calculated at
runtime by
+ calling min and max operators. The quantized models generated in this
+ mode are normally 10-20% slower than those with calibrations during
inference.
+ If calib_mode='naive', the min and max values of the layer outputs
from a calibration
+ dataset will be directly taken as the thresholds for quantization.
+ If calib_mode='entropy' (default mode), the thresholds for
quantization will be
+ derived such that the KL divergence between the distributions of FP32
layer outputs and
+ quantized layer outputs is minimized based upon the calibration
dataset.
+ calib_layer : function
+ Given a layer's output name in string, return True or False for
deciding whether to
+ calibrate this layer. If yes, the statistics of the layer's output
will be collected;
+ otherwise, no information of the layer's output will be collected. If
not provided,
+ all the layers' outputs that need requantization will be collected.
+ num_calib_examples : int or None
+ The maximum number of examples that user would like to use for
calibration. If not provided,
+ the whole calibration dataset will be used.
+ ctx : Context
+ Defines the device that users want to run forward propagation on the
calibration
+ dataset for collecting layer output statistics. Currently, only
supports single context.
+ logger : Object
+ A logging object for printing information during the process of
quantization.
+
+ Returns
+ -------
+ network : Gluon SymbolBlock
+ Defines the structure of a neural network for INT8 data types.
+ -------
+ """
+
+ logger.info('Export HybridBlock')
+ network.hybridize()
+ import mxnet as mx
+ if calib_data is not None:
+ if isinstance(calib_data, DataIter):
+ dshapes = calib_data.provide_data
+ else:
+ calib_data, dshapes = _as_data_iter(calib_data)
+ if not data_shapes:
+ data_shapes = dshapes
+ if not data_shapes:
+ raise ValueError('data_shapes required')
+ data_nd = []
+ for shape in data_shapes:
+ data_nd.append(mx.nd.zeros(shape.shape))
+ while True:
+ try:
+ network(*data_nd)
+ except TypeError:
+ del data_nd[-1]
+ del calib_data.provide_data[-1]
+ continue
+ else:
+ break
+
+ import tempfile
+ try:
+ from tempfile import TemporaryDirectory
+ except AttributeError:
+ # really simple implementation of TemporaryDirectory
+ class TemporaryDirectory(object):
+ def __init__(self, suffix='', prefix='', dir=''):
+ self._dirname = tempfile.mkdtemp(suffix, prefix, dir)
+
+ def __enter__(self):
+ return self._dirname
+
+ def __exit__(self, exc_type, exc_value, traceback):
+ shutil.rmtree(self._dirname)
+
+ with TemporaryDirectory() as tmpdirname:
+ prefix = os.path.join(tmpdirname, 'tmp')
+ network.export(prefix, epoch=0)
Review comment:
It's no mandatory and urgent now, but gluoncv also has the same
implementation
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