def testQCQuantize_CheckInvalidEncodingsGpu(self):
_log.info('running testQCQuantize_CheckInvalidEncodings')
for use_gpu in [True]:
_log.info('GPU mode is selected') if use_gpu else _log.info(
'CPU mode is selected')
with self.session(use_gpu=use_gpu):
bw = 8
# Instantiate DlQuantization object
comp_mode = libpymo.ComputationMode.COMP_MODE_GPU if use_gpu else libpymo.ComputationMode.COMP_MODE_CPU
libpytrext.InitQuantizer(
["conv1"], comp_mode, [],
libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED)
# Set encodings
enc_min_list = [-10.0, 0.5, 20]
enc_max_list = [100.0, 150.0, 200.0, 255.0]
output = self.qc_quantize_module.qc_quantize_deprecated(
op_name='conv1',
training_in_progress=False,
config=int(
libpytrext.config_type.CONFIG_TYPE_SET_ENCODING),
bitwidth=bw,
in_tensors=[[]],
fixed_enc_mins=enc_min_list,
fixed_enc_maxs=enc_max_list)
with self.assertRaises(errors_impl.InvalidArgumentError):
ops.convert_to_tensor(output[0]).eval()
libpytrext.ResetQuantizer()
def testQCQuantize_GetEncodingsGpu(self):
_log.info('running testQCQuantize_GetEncodings')
for use_gpu in [True]:
_log.info('GPU mode is selected') if use_gpu else _log.info(
'CPU mode is selected')
with self.session(use_gpu=use_gpu):
bw = 8
# Prepare activation tensors
ACT_MIN = -20.0
ACT_MAX = 25.0
actvn_1 = constant_op.constant([-10.0, -20.0, 25.0])
actvn_2 = constant_op.constant([8.0, -19.0, 30.0])
actvn_3 = constant_op.constant([12.0, -31.0, 35.4])
# Instantiate DlQuantization object
comp_mode = libpymo.ComputationMode.COMP_MODE_GPU if use_gpu else libpymo.ComputationMode.COMP_MODE_CPU
libpytrext.InitQuantizer(
["conv1"], comp_mode, [],
libpymo.QuantizationMode.QUANTIZATION_TF)
# Update stats
output_0 = self.qc_quantize_module.qc_quantize_deprecated(
op_name='conv1',
training_in_progress=False,
config=int(
libpytrext.config_type.CONFIG_TYPE_UPDATE_STATS),
bitwidth=bw,
in_tensors=[actvn_1, actvn_2, actvn_3],
fixed_enc_mins=[],
fixed_enc_maxs=[])
ops.convert_to_tensor(output_0[0]).eval()
# Get encodings
output_1 = self.qc_quantize_module.qc_quantize_deprecated(
op_name='conv1',
training_in_progress=False,
config=int(
libpytrext.config_type.CONFIG_TYPE_GET_ENCODING),
bitwidth=bw,
in_tensors=[[]],
fixed_enc_mins=[],
fixed_enc_maxs=[],
num_tensors=3)
enc_min = ops.convert_to_tensor(output_1[1]).eval()
enc_max = ops.convert_to_tensor(output_1[2]).eval()
true_encodings = self._compute_encodings(
enc_min[0], enc_max[0], bw)
expected_encodings = self._compute_encodings(
ACT_MIN, ACT_MAX, bw)
error_margin = 1e-5 # Use better heuristics
self.assertArrayNear(true_encodings, expected_encodings,
error_margin)
libpytrext.ResetQuantizer()
def testQCQuantize_SetEncodings(self):
_log.info('running testQCQuantize_SetEncodings')
for use_gpu in [False]:
_log.info('GPU mode is selected') if use_gpu else _log.info(
'CPU mode is selected')
with self.session(use_gpu=use_gpu):
bw = 8
# Instantiate DlQuantization object
comp_mode = libpymo.ComputationMode.COMP_MODE_CPU if use_gpu else libpymo.ComputationMode.COMP_MODE_GPU
libpytrext.InitQuantizer(
["conv1"], comp_mode, [],
libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED)
# Set encodings
# For the purpose of exact matches with expected encodings
# we choose to avoid ranges excluding 0 as the TF algorithm
# forces the encoding to include 0, thus differing from expected results.
enc_min_list = [-10.0, -0.5, 0]
enc_max_list = [100.0, 200.0, 160.0]
output_0 = self.qc_quantize_module.qc_quantize_deprecated(
op_name='conv1',
training_in_progress=False,
config=int(
libpytrext.config_type.CONFIG_TYPE_SET_ENCODING),
bitwidth=bw,
in_tensors=[[]],
fixed_enc_mins=enc_min_list,
fixed_enc_maxs=enc_max_list)
ops.convert_to_tensor(output_0[0]).eval()
# Retrieve encodings from op and validate
output_1 = self.qc_quantize_module.qc_quantize_deprecated(
op_name='conv1',
training_in_progress=False,
config=int(
libpytrext.config_type.CONFIG_TYPE_GET_ENCODING),
bitwidth=bw,
in_tensors=[[]],
fixed_enc_mins=[],
fixed_enc_maxs=[],
num_tensors=3)
get_enc_min = ops.convert_to_tensor(output_1[1]).eval()
get_enc_max = ops.convert_to_tensor(output_1[2]).eval()
for index in np.arange(0, len(enc_min_list)):
actual_encodings = self._compute_encodings(
get_enc_min[index], get_enc_max[index], bw)
expected_encodings = self._compute_encodings(
enc_min_list[index], enc_max_list[index], bw)
self.assertAllEqual(actual_encodings, expected_encodings)
libpytrext.ResetQuantizer()
def load_quantized_graph(meta_graph,
checkpoint,
encodings,
graph=None,
gpu=True):
"""
Function to call to setup the saved quantization encodings and model. When loading a quantized graph
from saved files the quantizer must first be initialized with the quantization op names
and the saved encodings.
:param meta_graph: Path to meta file
:param checkpoint: Path to checkpoint file
:param encodings: Path to encodings file
:param graph: Graph to load into
:param gpu: If True, use GPU ops
:return: Newly created tf.compat.v1.Session
"""
comp_mode = libpymo.ComputationMode.COMP_MODE_GPU if gpu else libpymo.ComputationMode.COMP_MODE_CPU
# Check to see if it's a file passed in and we need to process it, or if it's the
# actual map data
if isinstance(encodings, str):
with open(encodings, 'r') as f:
encodings = json.load(f)
quant_mode = encodings['quant_mode']
if quant_mode not in _QUANT_MODES:
raise ValueError('Invalid quantization mode: ' + quant_mode)
quant_mode = _QUANT_MODES[quant_mode]
libpytrext.ResetQuantizer()
libpytrext.InitQuantizer(list(encodings.keys()), comp_mode, [], quant_mode)
g = tf.Graph()
with g.as_default():
sess = tf.compat.v1.Session(graph=g)
_set_activation_encodings(sess, encodings, gpu=gpu)
# Use the provided graph, if it exists
if not graph:
graph = tf.Graph()
with graph.as_default():
sess, _ = _load_graph(graph, meta_graph, checkpoint)
return sess
def _prepare_graph_for_quantization(self, collect_stats=True):
"""
Inserts the appropriate quantization ops and prequantizes the params depending upon the
configuration parameters. Operations are inserted in the current default graph.
Raises:
RuntimeError: Thrown when there was an error inserting operations
:param collect_stats: If True, stats are collected
:return:
"""
# Get the op query module
query = core.OpQuery(self._sess.graph,
op_map=self._op_map,
ops_to_ignore=self._ops_to_ignore)
# Query the known op groups and insert quantization nodes after the ops
# Should we also be including quantization ops starting with labels? No for now...
activation_ops = query.get_known_ops(inputs=self._input_tensor_names)
# Query all ops with weights and quantize the input weights
weight_ops = query.get_weight_ops(skip_bias_op=self._skip_bias)
input_indices = query.get_weight_inputs(weight_ops)
# Instantiate DlQuantization object
quant_node_names = [
self._get_quantized_name(op.name) for op in activation_ops
]
libpytrext.ResetQuantizer()
libpytrext.InitQuantizer(quant_node_names, self._comp_mode, [],
self._quant_mode)
# Add quantization ops/data
self._insert_weight_quantization_ops(weight_ops, input_indices)
if not self._skip_output:
self._insert_activation_quantization_ops(activation_ops,
collect_stats)
def testQCQuantize_Params(self):
_log.info('running testQCQuantize_Params')
for use_gpu in [False, True]:
_log.info('GPU mode is selected') if use_gpu else _log.info(
'CPU mode is selected')
with self.session(use_gpu=use_gpu):
bw = 8
PARAM_MIN = -50.0
PARAM_MAX = 80.0
comp_mode = libpymo.ComputationMode.COMP_MODE_GPU if use_gpu else libpymo.ComputationMode.COMP_MODE_CPU
# Instantiate DlQuantization object
libpytrext.InitQuantizer(
["conv1"], comp_mode, [],
libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED)
weights = constant_op.constant(
[-40.0, -1.0, 0.0, 1.0, 2.0, -50.0, 80.0])
# Quantize and de-quantize params
test_output = self.qc_quantize_module.qc_quantize_deprecated(
op_name='conv1',
training_in_progress=False,
config=int(libpytrext.config_type.CONFIG_TYPE_Q_DQ_PARAMS),
bitwidth=bw,
in_tensors=[weights],
fixed_enc_mins=[],
fixed_enc_maxs=[],
num_tensors=1)
quantized_weights = ops.convert_to_tensor(
test_output[0]).eval()
self.assertAllClose(quantized_weights[0], weights.eval(), 1.0)
# Examine encodings of quantized params
out_enc_min = ops.convert_to_tensor(test_output[1]).eval()
out_enc_max = ops.convert_to_tensor(test_output[2]).eval()
true_encodings = self._compute_encodings(
out_enc_min[0], out_enc_max[0], bw)
expected_encodings = self._compute_encodings(
PARAM_MIN, PARAM_MAX, bw)
error_margin = 10 # Use better heuristics; ideally there should be 0 error margin
self.assertArrayNear(true_encodings, expected_encodings,
error_margin)
# Repeat test with training_in_progress == true
test_output = self.qc_quantize_module.qc_quantize_deprecated(
op_name='conv1',
training_in_progress=True,
config=int(libpytrext.config_type.CONFIG_TYPE_Q_DQ_PARAMS),
bitwidth=bw,
in_tensors=[weights],
fixed_enc_mins=[],
fixed_enc_maxs=[],
num_tensors=1)
quantized_weights = ops.convert_to_tensor(
test_output[0]).eval()
self.assertAllClose(quantized_weights[0], weights.eval(), 1.0)
# Examine encodings of quantized params
out_enc_min = ops.convert_to_tensor(test_output[1]).eval()
out_enc_max = ops.convert_to_tensor(test_output[2]).eval()
true_encodings = self._compute_encodings(
out_enc_min[0], out_enc_max[0], bw)
expected_encodings = self._compute_encodings(
PARAM_MIN, PARAM_MAX, bw)
error_margin = 10 # Use better heuristics; ideally there should be 0 error margin
self.assertArrayNear(true_encodings, expected_encodings,
error_margin)
libpytrext.ResetQuantizer()
def testQCQuantize_CheckZeroRepresentation(self):
_log.info('running testQCQuantize_CheckZeroRepresentation')
for use_gpu in [False]:
_log.info('GPU mode is selected') if use_gpu else _log.info(
'CPU mode is selected')
with self.session(use_gpu=use_gpu):
bw = 8
# Test all negative ranges
act_min = -8.0
act_max = -5.0
# Instantiate DlQuantization object
comp_mode = libpymo.ComputationMode.COMP_MODE_GPU if use_gpu else libpymo.ComputationMode.COMP_MODE_CPU
libpytrext.InitQuantizer(
["conv1"], comp_mode, [],
libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED)
# Set encodings
output_0 = self.qc_quantize_module.qc_quantize_deprecated(
op_name='conv1',
training_in_progress=False,
config=int(
libpytrext.config_type.CONFIG_TYPE_SET_ENCODING),
bitwidth=bw,
in_tensors=[[]],
fixed_enc_mins=[act_min],
fixed_enc_maxs=[act_max])
ops.convert_to_tensor(output_0[0]).eval()
# Get encodings from op and validate
output_1 = self.qc_quantize_module.qc_quantize_deprecated(
op_name='conv1',
training_in_progress=False,
config=int(
libpytrext.config_type.CONFIG_TYPE_GET_ENCODING),
bitwidth=bw,
in_tensors=[[]],
fixed_enc_mins=[],
fixed_enc_maxs=[],
num_tensors=1)
enc_max = ops.convert_to_tensor(output_1[2]).eval()
self.assertEqual(enc_max[0], 0.0)
# Test all positive ranges
act_min = 20.0
act_max = 100.0
# Set encodings
output_0 = self.qc_quantize_module.qc_quantize_deprecated(
op_name='conv1',
training_in_progress=False,
config=int(
libpytrext.config_type.CONFIG_TYPE_SET_ENCODING),
bitwidth=bw,
in_tensors=[[]],
fixed_enc_mins=[act_min],
fixed_enc_maxs=[act_max])
ops.convert_to_tensor(output_0[0]).eval()
# Get encodings from op and validate
output_1 = self.qc_quantize_module.qc_quantize_deprecated(
op_name='conv1',
training_in_progress=False,
config=int(
libpytrext.config_type.CONFIG_TYPE_GET_ENCODING),
bitwidth=bw,
in_tensors=[[]],
fixed_enc_mins=[],
fixed_enc_maxs=[],
num_tensors=1)
enc_min = ops.convert_to_tensor(output_1[1]).eval()
self.assertEqual(enc_min[0], 0.0)
libpytrext.ResetQuantizer()
def testQCQuantize_MultipleActivations(self):
_log.info('running testQCQuantize_MultipleActivations')
for use_gpu in [False]:
_log.info('GPU mode is selected') if use_gpu else _log.info(
'CPU mode is selected')
with self.session(use_gpu=use_gpu):
bw = 8
actvn_stats_0 = actvn_stats_1 = actvn_stats_2 = actvn_stats_3 = constant_op.constant(
np.arange(0, 100).astype(np.float32))
# Instantiate DlQuantization object
comp_mode = libpymo.ComputationMode.COMP_MODE_GPU if use_gpu else libpymo.ComputationMode.COMP_MODE_CPU
libpytrext.InitQuantizer(
["conv1"], comp_mode, [],
libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED)
# Update quantization stats
output_0 = self.qc_quantize_module.qc_quantize_deprecated(
op_name='conv1',
training_in_progress=False,
config=int(
libpytrext.config_type.CONFIG_TYPE_UPDATE_STATS),
bitwidth=bw,
in_tensors=[
actvn_stats_0, actvn_stats_1, actvn_stats_2,
actvn_stats_3
],
fixed_enc_mins=[],
fixed_enc_maxs=[])
ops.convert_to_tensor(output_0[0]).eval()
actvn_0 = constant_op.constant(
np.arange(0, 10).astype(np.float32))
actvn_1 = constant_op.constant(
np.arange(10, 20).astype(np.float32))
actvn_2 = constant_op.constant(
np.arange(20, 30).astype(np.float32))
actvn_3 = constant_op.constant(
np.arange(30, 40).astype(np.float32))
test_actvn = [actvn_0, actvn_1, actvn_2, actvn_3]
# Quantize and de-quantize activations
output_1 = self.qc_quantize_module.qc_quantize_deprecated(
op_name='conv1',
training_in_progress=False,
config=int(
libpytrext.config_type.CONFIG_TYPE_Q_DQ_ACTIVATIONS),
bitwidth=bw,
in_tensors=[actvn_0, actvn_1, actvn_2, actvn_3],
fixed_enc_mins=[],
fixed_enc_maxs=[],
num_tensors=4)
quantized_acts = ops.convert_to_tensor(output_1[0]).eval()
quantization_error_margin = 1.0
for index in np.arange(0, len(quantized_acts)):
self.assertArrayNear(
ops.convert_to_tensor(test_actvn[index]).eval(),
quantized_acts[index], quantization_error_margin)
# Test output encodings
enc_min = ops.convert_to_tensor(output_1[1]).eval()
enc_max = ops.convert_to_tensor(output_1[2]).eval()
# Compare against encodings obtained from get_encoding()
get_enc_tensor = self.qc_quantize_module.qc_quantize_deprecated(
op_name='conv1',
training_in_progress=False,
config=int(
libpytrext.config_type.CONFIG_TYPE_GET_ENCODING),
bitwidth=bw,
in_tensors=[[]],
fixed_enc_mins=[],
fixed_enc_maxs=[],
num_tensors=4)
exp_enc_min = ops.convert_to_tensor(get_enc_tensor[1]).eval()
exp_enc_max = ops.convert_to_tensor(get_enc_tensor[2]).eval()
for index in np.arange(0, len(quantized_acts)):
true_encodings = self._compute_encodings(
enc_min[index], enc_max[index], bw)
expected_encodings = self._compute_encodings(
exp_enc_min[index], exp_enc_max[index], bw)
error_margin = 1.0 # Not a fair test to compare TF with TF_ENHANCED, but works for now
self.assertAllEqual(true_encodings, expected_encodings)
libpytrext.ResetQuantizer()
def testQCQuantize_SingleActivation(self):
_log.info('running testQCQuantize_SingleActivation')
for use_gpu in [False, True]:
_log.info('GPU mode is selected') if use_gpu else _log.info(
'CPU mode is selected')
with self.session(use_gpu=use_gpu):
bw = 8
# Instantiate DlQuantization object
comp_mode = libpymo.ComputationMode.COMP_MODE_GPU if use_gpu else libpymo.ComputationMode.COMP_MODE_CPU
libpytrext.InitQuantizer(
["conv1"], comp_mode, [],
libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED)
actvn_0 = constant_op.constant(
np.arange(0, 20).astype(np.float32))
actvn_1 = constant_op.constant(
np.arange(0, 50).astype(np.float32))
actvn_2 = constant_op.constant(
np.arange(0, 100).astype(np.float32))
# Update quantization stats
output_0 = self.qc_quantize_module.qc_quantize_deprecated(
op_name='conv1',
training_in_progress=False,
config=int(
libpytrext.config_type.CONFIG_TYPE_UPDATE_STATS),
bitwidth=bw,
in_tensors=[actvn_0],
fixed_enc_mins=[],
fixed_enc_maxs=[])
ops.convert_to_tensor(output_0[0]).eval()
output_1 = self.qc_quantize_module.qc_quantize_deprecated(
op_name='conv1',
training_in_progress=False,
config=int(
libpytrext.config_type.CONFIG_TYPE_UPDATE_STATS),
bitwidth=bw,
in_tensors=[actvn_1],
fixed_enc_mins=[],
fixed_enc_maxs=[])
ops.convert_to_tensor(output_1[0]).eval()
output_2 = self.qc_quantize_module.qc_quantize_deprecated(
op_name='conv1',
training_in_progress=False,
config=int(
libpytrext.config_type.CONFIG_TYPE_UPDATE_STATS),
bitwidth=bw,
in_tensors=[actvn_2],
fixed_enc_mins=[],
fixed_enc_maxs=[])
ops.convert_to_tensor(output_2[0]).eval()
ACT_MIN = 0.0
ACT_MAX = 16.0
test_actvn = constant_op.constant(
[ACT_MAX]) # Single input activation
# Quantize and de-quantize activations
test_output = self.qc_quantize_module.qc_quantize_deprecated(
op_name='conv1',
training_in_progress=False,
config=int(
libpytrext.config_type.CONFIG_TYPE_Q_DQ_ACTIVATIONS),
bitwidth=bw,
in_tensors=[test_actvn],
fixed_enc_mins=[],
fixed_enc_maxs=[],
num_tensors=1)
quantized_acts = ops.convert_to_tensor(test_output[0]).eval()
# Test output activations
self.assertAllClose(quantized_acts[0], test_actvn.eval(), 1.0)
# Test output encodings from quantizing activations.
enc_min = ops.convert_to_tensor(test_output[1]).eval()
enc_max = ops.convert_to_tensor(test_output[2]).eval()
true_encodings = self._compute_encodings(
enc_min[0], enc_max[0], bw)
# Compare against encodings obtained from get_encoding()
get_enc_tensor = self.qc_quantize_module.qc_quantize_deprecated(
op_name='conv1',
training_in_progress=False,
config=int(
libpytrext.config_type.CONFIG_TYPE_GET_ENCODING),
bitwidth=bw,
in_tensors=[[]],
fixed_enc_mins=[],
fixed_enc_maxs=[],
num_tensors=1)
exp_enc_min = ops.convert_to_tensor(get_enc_tensor[1]).eval()
exp_enc_max = ops.convert_to_tensor(get_enc_tensor[2]).eval()
expected_encodings = self._compute_encodings(
exp_enc_min[0], exp_enc_max[0], bw)
self.assertAllEqual(true_encodings, expected_encodings)
libpytrext.ResetQuantizer()
