def my_add_control_inputs(wait_to_do_ops, inputs_to_do_before):
for op in wait_to_do_ops:
ci = [
i for i in inputs_to_do_before
if op.control_inputs is None or i not in op.control_inputs
]
ge.add_control_inputs(op, ci)
def my_add_control_inputs(wait_to_do_ops, inputs_to_do_before):
""" Add control inputs """
for tf_op in wait_to_do_ops:
ctl_inp = [
i for i in inputs_to_do_before
if tf_op.control_inputs is None or i not in tf_op.control_inputs
]
ge.add_control_inputs(tf_op, ctl_inp)
def add_control_dependency(all_ops, swapin_op, target_op):
for tensor in target_op.inputs:
if "_grad" in tensor.name:
#we need to find this tenor is which operation's output
for op in all_ops:
for i in range(len(op.outputs)):
if ((op.outputs[i].name == tensor.name)
and ("_grad" in op.name)):
print("swapin_op:", swapin_op, "op:", op)
ge.add_control_inputs(swapin_op, op)
def add_control_dependency(all_ops, swapin_op, target_op):
global added_control
for tensor in target_op.inputs:
if "_grad" in tensor.name:
#we need to find this tenor is which operation's output
for op in all_ops:
for i in range(len(op.outputs)):
if ((op.outputs[i].name == tensor.name)): #and ("shape" not in op.name)):
added_control = True
print("swapin_op:", swapin_op, "which op is added control_dependency:", op)
ge.add_control_inputs(swapin_op, op)
def _add_control_dependency(self, fw_op, bw_op, swapin_op):
"""Find and add a control dependency to the graph.
This method does an in-place modification to the graph.
Args:
fw_op: a `tf.Operation`.
bw_op: a `tf.Operation`.
swapin_op: a `tf.Operation`.
"""
# if lb is out of range, reset it to make sure
# that a control dependency op will be found
lb = self._lb
if (self._topo_sort.get_order(bw_op) - lb <=
self._topo_sort.get_order(fw_op)):
lb = 1
if fw_op in self._grad_ops:
re = self._do_direct_order(fw_op, bw_op, lb, self._ub)
elif self._ctrld_strategy is CTRLD_Strategy.CHAIN_RULE:
re = self._do_chain_rule(fw_op, bw_op, lb, self._ub)
elif self._ctrld_strategy is CTRLD_Strategy.DIRECT_ORDER:
re = self._do_direct_order(fw_op, bw_op, lb, self._ub)
else:
re = self._do_chain_rule(fw_op, bw_op, lb, self._ub)
ctrld_op = re[0]
ctrld_order = re[1]
if ctrld_op:
ge.add_control_inputs(swapin_op, ctrld_op)
self._log_info(
"Control dependency op {}, order: {}".format(
ctrld_op.name, ctrld_order), 1)
else:
self._log_info(
"No control dependency op needed for swap in of op {}.".format(
fw_op.name), 1)
def Quantize(graph,
weight_bits=8,
weight_narrow_range=False,
activation_bits=8,
ema_decay=0.999,
quant_delay=None,
vars_collection=ops.GraphKeys.MOVING_AVERAGE_VARIABLES,
is_training=True,
quantize_folded_weights_use_ema=False):
"""Updates graph with quantization operations.
Args:
graph: Graph to modify.
weight_bits: Number of bits to use for quantizing weights.
weight_narrow_range: Whether to use a more efficient narrow range for
weights quantization. With weight_narrow_range true, the range is
[1; 2^weight_bits - 1], with it false [0; 2^weight_bits - 1].
activation_bits: Number of bits to use for quantizing activations.
ema_decay: (Optional) Float, EMA decay parameter. EMA is used to update
quantization intervals for quantizing activations (see here about EMA:
https://en.wikipedia.org/wiki/Moving_average#Exponential_moving_average).
quant_delay: (Optional, default None) Int, count of global steps for which
to delay quantization. This helps weights stabilize at the start of
training.
vars_collection: (Optional) Collection where to store the variables for
quantization interval ends.
is_training: (Optional) Whether quantizing training graph or eval graph.
quantize_folded_weights_use_ema: (Optional, default False) Whether to
quantize weights after batchnorm-folding with exponential average
quantization.
Raises:
ValueError: When quantization fails.
"""
context = _QuantizeContext(graph, weight_bits, weight_narrow_range,
activation_bits, ema_decay, quant_delay,
vars_collection, is_training,
quantize_folded_weights_use_ema)
graph_ops = graph.get_operations()
# Filter out backprop and summary related operations, leave only interesting
# op types.
def _IsInterestingOpWithWeights(op):
return (op.type in _QUANTIZABLE_TYPES
and not op.name.startswith(common.SKIPPED_PREFIXES))
for op in (op for op in graph_ops if _IsInterestingOpWithWeights(op)):
if op.name.endswith('/depthwise'):
# Separable convolution may consist of 2 convolution nodes. If so, skip
# .../depthwise and only quantize the top one.
separable_conv = context.GetOperationByNameDontThrow(
op.name[:-len('/depthwise')])
if separable_conv and separable_conv.type == 'Conv2D':
continue
# Quantize add ops that come after Conv2D or DepthwiseConv2dNative.
if op.type in ['Conv2D', 'DepthwiseConv2dNative']:
add_context_re = re.search(r'^(.*)/[^/]+/', op.name)
if add_context_re is not None:
context.add_contexts.add(add_context_re.group(1))
if not op.name.endswith('_Fold'):
folded_op = context.GetOperationByNameDontThrow(op.name + '_Fold')
# Do nothing if found, it will be quantized when it is iterated over.
if not folded_op:
context.QuantizeOpWithWeights(op, folded=False)
else:
context.QuantizeOpWithWeights(op, folded=True)
context.QuantizeAddContexts()
# Once all quantization ops have been inserted in the graph, collect update
# ops for their variables and modify the TF Slim update barrier (see
# https://www.tensorflow.org/code/tensorflow/contrib/slim/python/slim/learning.py)
# to depend on them.
try:
update_barrier = graph.get_operation_by_name('update_barrier')
except KeyError:
# In evaluation graph, this barrier may not exist.
return None
update_quant_ops = graph.get_collection_ref(_UPDATE_QUANT_OPS)
graph_editor.add_control_inputs(update_barrier, update_quant_ops)
def Quantize(graph,
weight_bits=8,
weight_narrow_range=False,
activation_bits=8,
ema_decay=0.999,
quant_delay=None,
vars_collection=ops.GraphKeys.MOVING_AVERAGE_VARIABLES,
is_training=True,
quantize_folded_weights_use_ema=False):
"""Updates graph with quantization operations.
Args:
graph: Graph to modify.
weight_bits: Number of bits to use for quantizing weights.
weight_narrow_range: Whether to use a more efficient narrow range for
weights quantization. With weight_narrow_range true, the range is
[1; 2^weight_bits - 1], with it false [0; 2^weight_bits - 1].
activation_bits: Number of bits to use for quantizing activations.
ema_decay: (Optional) Float, EMA decay parameter. EMA is used to update
quantization intervals for quantizing activations (see here about EMA:
https://en.wikipedia.org/wiki/Moving_average#Exponential_moving_average).
quant_delay: (Optional, default None) Int, count of global steps for which
to delay quantization. This helps weights stabilize at the start of
training.
vars_collection: (Optional) Collection where to store the variables for
quantization interval ends.
is_training: (Optional) Whether quantizing training graph or eval graph.
quantize_folded_weights_use_ema: (Optional, default False) Whether to
quantize weights after batchnorm-folding with exponential average
quantization.
Raises:
ValueError: When quantization fails.
"""
context = _QuantizeContext(graph, weight_bits, weight_narrow_range,
activation_bits, ema_decay, quant_delay,
vars_collection, is_training,
quantize_folded_weights_use_ema)
graph_ops = graph.get_operations()
# Filter out backprop and summary related operations, leave only interesting
# op types.
def _IsInterestingOpWithWeights(op):
return (op.type in _QUANTIZABLE_TYPES and
not op.name.startswith(common.SKIPPED_PREFIXES))
for op in (op for op in graph_ops if _IsInterestingOpWithWeights(op)):
if op.name.endswith('/depthwise'):
# Separable convolution may consist of 2 convolution nodes. If so, skip
# .../depthwise and only quantize the top one.
separable_conv = context.GetOperationByNameDontThrow(
op.name[:-len('/depthwise')])
if separable_conv and separable_conv.type == 'Conv2D':
continue
# Quantize add ops that come after Conv2D or DepthwiseConv2dNative.
if op.type in ['Conv2D', 'DepthwiseConv2dNative']:
add_context_re = re.search(r'^(.*)/[^/]+/', op.name)
if add_context_re is not None:
context.add_contexts.add(add_context_re.group(1))
if not op.name.endswith('_Fold'):
folded_op = context.GetOperationByNameDontThrow(op.name + '_Fold')
# Do nothing if found, it will be quantized when it is iterated over.
if not folded_op:
context.QuantizeOpWithWeights(op, folded=False)
else:
context.QuantizeOpWithWeights(op, folded=True)
context.QuantizeAddContexts()
# Once all quantization ops have been inserted in the graph, collect update
# ops for their variables and modify the TF Slim update barrier (see
# https://www.tensorflow.org/code/tensorflow/contrib/slim/python/slim/learning.py)
# to depend on them.
try:
update_barrier = graph.get_operation_by_name('update_barrier')
except KeyError:
# In evaluation graph, this barrier may not exist.
return None
update_quant_ops = graph.get_collection_ref(_UPDATE_QUANT_OPS)
graph_editor.add_control_inputs(update_barrier, update_quant_ops)
def run_after(a_tensor, b_tensor):
"""Force a to run after b"""
a = graph.get_operation_by_name(a_tensor)
b = graph.get_operation_by_name(b_tensor)
ge.add_control_inputs(a, b)
def my_add_control_inputs(wait_to_do_ops, inputs_to_do_before):
""" Add control inputs """
for tf_op in wait_to_do_ops:
ctl_inp = [i for i in inputs_to_do_before
if tf_op.control_inputs is None or i not in tf_op.control_inputs]
ge.add_control_inputs(tf_op, ctl_inp)
def my_add_control_inputs(wait_to_do_ops, inputs_to_do_before):
for op in wait_to_do_ops:
ci = [i for i in inputs_to_do_before if op.control_inputs is None or i not in op.control_inputs]
ge.add_control_inputs(op, ci)