def pass_remove_identities(graph: Graph) -> None:
"""Removes those nodes of a Graph that satisfies the condition node.op_type() == Identity.
Args:
graph (Graph): The input graph. It will be modified in-place.
"""
exec_list = [n for n in sort_graph(graph) if n.op_type == 'Identity']
to_be_removed = list()
for m in exec_list:
"""skip all identity."""
in_op = m.input_ops['input']
out_ops = m.output_ops['output']
for out_op in out_ops:
for k, v in out_op.input_ops.items():
if v == m:
# change the output's input to this identity's input
out_op.add_input(k, in_op)
# change the input's output to this identity's output
for k2, v2 in in_op.output_ops.items():
if m in v2:
v2.remove(m)
v2.append(out_op)
break
break
to_be_removed.append(m)
for op in to_be_removed:
graph.remove_op(op)
def pass_constant_folding(graph: Graph) -> None:
"""Given a node N, if the value of each input of N is known at compilation time then N will be executed.
The node N and its inputs will be replaced with a Constant node which holds the computed output of N.
Args:
graph (Graph): The input graph. It will be modified in-place.
processed_nodes (list): The list of the processed nodes so far.
"""
done = False
processed_nodes = []
while not done:
exec_list = sort_graph(graph)
processed_before_precompute = len(processed_nodes)
to_be_removed = []
for m in exec_list:
if m in processed_nodes:
continue
# We want operators with inputs
if not m.input_nodes:
continue
precomputable = True
for input_node in m.input_nodes:
if input_node.op_type != 'Constant':
precomputable = False
if not precomputable:
continue
processed_nodes += m.input_nodes
processed_nodes.append(m)
data = m.run_forward()
new_constant = Constant(m.name + '_new',
m.dtype,
data,
dimension_format=m.dimension)
graph.add_op(new_constant)
# get nodes to be removed after being disconnected
get_nodes_in_branch(m, None, to_be_removed)
new_constant.add_outputs({'output': m.output_op_list})
for output_name, consumer_list in m.output_ops.items():
for consumer_node in consumer_list:
for input_name, input_node in consumer_node.input_ops.items(
):
if input_node == m:
consumer_node.add_input(input_name, new_constant)
break
for op in to_be_removed:
graph.remove_op(op)
done = len(processed_nodes) == processed_before_precompute
def add_all_nodes(self, graph: Graph) -> None:
visited: Set[Any] = set()
added: Dict[str, Operator] = {}
nodes_to_remove = []
# fixme: default output format is NC/NHWC (ad-hoc workaround)
rank_to_format = {2: 'NC', 4: 'NHWC'}
self.add_node_to_graph_recursive(
self.out_lst[0], graph, visited, added,
rank_to_format[len(self.out_lst[0].get_shape())], nodes_to_remove)
for node in nodes_to_remove:
graph.remove_op(node)
def pass_simplify_batchnorm(graph: Graph) -> None:
"""Simplify BarchNorm operator.
"""
exec_list = [
x for x in sort_graph(graph) if x.op_type == 'BatchNormalization'
]
to_be_removed = []
for node in exec_list:
scale = node.input_ops['scale']
if scale.op_type != 'Constant':
raise RuntimeError('scale for BatchNormalization must be Constant')
B = node.input_ops['B']
if B.op_type != 'Constant':
raise RuntimeError('B for BatchNormalization must be Constant')
mean = node.input_ops['mean']
if mean.op_type != 'Constant':
raise RuntimeError('mean for BatchNormalization must be Constant')
var = node.input_ops['var']
if var.op_type != 'Constant':
raise RuntimeError('var for BatchNormalization must be Constant')
new_name = node.name + '_optimized'
new_scale_data = scale.data / np.sqrt(var.data + node.epsilon)
new_scale = Constant(new_name + '_scale',
scale.dtype,
new_scale_data,
dimension_format=scale.dimension)
new_bias_data = B.data - new_scale_data * mean.data
new_bias = Constant(new_name + '_bias',
B.dtype,
new_bias_data,
dimension_format=B.dimension)
new_op = BatchNormalizationOptimized(new_name,
node.shape,
node.dtype, {
'X': node.input_ops['X'],
'scale': new_scale,
'bias': new_bias
},
dimension_format=node.dimension)
new_scale.add_output('output', new_op)
new_bias.add_output('output', new_op)
input_op = node.input_ops['X']
update_key = None
new_outputs = [new_op]
for key, inout_ops in input_op.output_ops.items():
if node in inout_ops:
update_key = key
for op in inout_ops:
if op != node:
new_outputs.append(op)
if update_key is not None:
input_op.remove_output(update_key)
input_op.add_outputs({update_key: new_outputs})
out_ops = node.output_op_list
for op in out_ops:
update_key = None
for key, outin_op in op.input_ops.items():
if outin_op == node:
update_key = key
if update_key is not None:
op.add_input(update_key, new_op)
new_op.add_output('Y', op)
graph.add_op(new_scale)
graph.add_op(new_bias)
graph.add_op(new_op)
to_be_removed += [node, scale, B, mean, var]
for node in to_be_removed:
graph.remove_op(node)
def pass_lookup(graph: Graph) -> None:
"""Lookup.
Args:
graph (Graph): The input graph. It will be modified in-place.
"""
quantization_types = [
'BinaryMeanScalingQuantizer', 'LinearMidTreadHalfQuantizer',
'BinaryChannelWiseMeanScalingQuantizer'
]
to_be_removed = []
exec_list = [
n for n in sort_graph(graph) if n.op_type in quantization_types
]
placeholder = [n for n in sort_graph(graph) if n.op_type in 'Input']
for m in exec_list:
quantizer = m
p1 = quantizer.input_nodes[0]
if p1.op_type != 'Reshape':
continue
p2 = p1.input_nodes[0]
if p2.op_type != 'Reshape':
continue
p3 = p2.input_nodes[0]
if p3.op_type != 'Gather':
continue
p4 = p3.input_nodes[0]
if p4.op_type != 'Gather':
continue
gather_params = p4.input_nodes[0]
if gather_params.rank != 2 or gather_params.shape[0] != 256:
continue
params = gather_params.data
data = {'data': params}
qtz_data = quantizer.run(**data)['data']
word_size = 32
lu_bitwidth = quantizer.nbit
packer = Packer(lu_bitwidth, word_size)
lsb = np.zeros((256, ), np.uint32)
msb = np.zeros((256, ), np.uint32)
idx = 0
for p in qtz_data:
data = packer.run(p.astype(np.float32), p.shape).flatten()
lsb[idx] = data[0]
msb[idx] = data[1]
idx += 1
pe_lsb = Constant('pe_lsb_new',
QUANTIZED_PACKED_KERNEL(),
lsb,
dimension_format='TC',
packed=True,
actual_shape=[256, word_size])
pe_msb = Constant('pe_msb_new',
QUANTIZED_PACKED_KERNEL(),
msb,
dimension_format='TC',
packed=True,
actual_shape=[256, word_size])
n, h, w, c = quantizer.shape
shape = [1, h, w, 2, word_size]
pe = Lookup('Lookup',
shape,
QUANTIZED_PACKED(), {
'input': placeholder[0],
'lsb': pe_lsb,
'msb': pe_msb
},
dimension_format='ChHWBCl')
get_nodes_in_branch(quantizer, placeholder[0], to_be_removed)
reserved_placeholder_ops = [
out_op for out_op in placeholder[0].output_op_list
if out_op not in to_be_removed
]
placeholder[0].remove_output('output')
placeholder[0].add_outputs({'output': reserved_placeholder_ops})
pe.add_outputs(quantizer.output_ops)
output_op = quantizer.output_op_list[0]
target_input_name = 'X'
for input_name in output_op._input_names:
if quantizer.equals(output_op._input_ops[input_name]):
target_input_name = input_name
break
output_op.add_input(target_input_name, pe)
graph.add_op(pe_lsb)
graph.add_op(pe_msb)
graph.add_op(pe)
for op in to_be_removed:
graph.remove_op(op)
def pass_pack_weights(graph: Graph) -> None:
"""Given a Quantized convolution node C, it will pack the weights of C into 32 bit words.
If the node Q that apply quantization to the weights of C quantizes, for example, into 1 bit values
then one 32 bit word will contain 32 weights.
Args:
graph (Graph): The input graph. It will be modified in-place.
"""
exec_list = [n for n in sort_graph(graph) if n.op_type == 'Conv']
quantization_types = [
'BinaryMeanScalingQuantizer', 'LinearMidTreadHalfQuantizer',
'BinaryChannelWiseMeanScalingQuantizer'
]
word_size = 32
weight_bitwidth = 1
packer = Packer(weight_bitwidth, word_size)
to_be_removed = []
b = 32
for m in exec_list:
conv_node = m
# check if this is a quantized convolution
if not conv_node.quantizer or not conv_node.a_quantizer:
continue
# Check if we support this kind of quantizer
weight_quantizer = conv_node.quantizer
if weight_quantizer.op_type not in quantization_types:
continue
# Quantize the weights
weight_quantizer.run_forward()
def pad_to_multiple_of_b(tensor, axis, b):
shape = list(tensor.shape)
pad = (((shape[axis] + b - 1) // b) * b) - shape[axis]
shape[axis] = pad
return np.zeros(shape) if pad else None
padded_data = np.copy(weight_quantizer.data)
for axis in [0, 3]:
pad_tensor = pad_to_multiple_of_b(padded_data, axis, b)
if pad_tensor is not None:
padded_data = np.append(padded_data, pad_tensor, axis=axis)
tca_output = np.copy(padded_data)
oc, kh, kw, kd = padded_data.shape[:]
padded_data = padded_data.flatten()
tca_output = tca_output.flatten()
out_index = 0
for g in range(oc // b):
for p in range(kd // b):
for h in range(kh):
for w in range(kw):
for o in range(b):
for d in range(b):
idx = g * (kw * kh * kd * b) + p * b + h * (
kw * kd) + w * kd + o * (kw * kh * kd) + d
tca_output[out_index] = padded_data[idx]
out_index += 1
kn2row_output = np.zeros(oc * kh * kw * kd)
out_index = 0
for h in range(kh):
for w in range(kw):
for o in range(oc):
for i in range(kd):
idx = o * kh * kw * kd + h * kw * kd + w * kd + i
kn2row_output[out_index] = padded_data[idx]
out_index += 1
op_data = weight_quantizer.binarizer(padded_data)
data = packer.run(op_data.astype(np.float32),
weight_quantizer.dimension)
tca_binarized_data = weight_quantizer.binarizer(tca_output)
tca_packed_data = packer.run(tca_binarized_data.astype(np.float32),
weight_quantizer.dimension)
kn2row_binarized_data = weight_quantizer.binarizer(kn2row_output)
kn2row_data = packer.run(kn2row_binarized_data.astype(np.float32),
weight_quantizer.dimension)
shape = [oc, kh, kw, kd]
tca_shape = [oc // b, kd // b, kh, kw, b, b]
kn2row_shape = [kh, kw, oc, kd]
# Create the new constant with the quantized weights
quantized_constant = Constant(
weight_quantizer.name + '_new',
PackedUint32(),
data=np.vectorize(lambda k: (~k) & ((0x1 << 32) - 1))(data),
dimension_format="OHWI",
transposed_dimension_format="OhIhHWOlIl",
packed=True,
actual_shape=shape,
transposed_shape=tca_shape,
transposed_data=[(~k) & ((0x1 << 32) - 1)
for k in tca_packed_data.flatten()],
kn2row_data=[k for k in kn2row_data.flatten()],
kn2row_shape=kn2row_shape,
kn2row_dimension_format="HWOI")
# get nodes to be removed after being disconnected
get_nodes_in_branch(weight_quantizer, None, to_be_removed)
# Add the constant to the graph and connect the new constant
graph.add_op(quantized_constant)
quantized_constant.add_outputs(weight_quantizer.output_ops)
for output_name, consumer_list in weight_quantizer.output_ops.items():
for consumer_node in consumer_list:
for input_name, input_node in consumer_node.input_ops.items():
if input_node == weight_quantizer:
consumer_node.add_input(input_name, quantized_constant)
break
for op in to_be_removed:
graph.remove_op(op)
def pass_compute_thresholds(graph: Graph) -> None:
"""Given a Quantizer node Q:
- if there is a backward path between Q and a convolution node and,
- every node N of that path satisfies the condition N.is_monotonic and,
- the convolution node C (the end of this path) is a quantized convolution
then this pass construct an LUT per channel which maps a possible output value of the quantized convolution node
C to the corresponding output of the quantization node Q. This effectively compress the path C -> ... -> Q
into a list of LUTs that can be used during inference.
Args:
graph (Graph): The input graph. It will be modified in-place.
"""
exec_list = [
n for n in sort_graph(graph)
if n.op_type == 'LinearMidTreadHalfQuantizer'
]
to_be_removed = []
for m in exec_list:
# find a a backward path between the quantizer and the convolution ie. a path represented by a list [Q, ..., C]
p = [m]
ok = True
while p[-1].op_type != 'Conv':
non_variable_input = [
inode for inode in p[-1].input_nodes
if (inode.op_type != 'Constant' and inode.is_monotonic)
or inode.op_type == 'Conv'
]
if len(non_variable_input) != 1:
ok = False
break
p.append(non_variable_input[-1])
if len(p[-1].output_op_list) != 1:
ok = False
break
if not ok:
continue
activation_quantizer_node = p[0]
conv_node = p[-1]
# check if this is a quantized convolution
if not conv_node.quantizer or not conv_node.a_quantizer:
continue
quantizer_conv_weights = conv_node.quantizer
quantizer_conv_weights.run_forward_no_scaling_factor()
scaling_factor = quantizer_conv_weights.scaling_factor
# Getting the bit and max value
nbits = []
max_vs = []
for aqtz in conv_node.a_quantizer:
nbits.append(aqtz.nbit)
max_vs.append(aqtz.max_v)
if not (len(set(nbits)) == 1) and not (len(set(max_vs)) == 1):
raise ValueError(
f'bits {nbits} or max values {max_vs} are not consistent')
else:
nbit = nbits[0]
max_v = max_vs[0]
n = 2**nbit - 1
ch = conv_node.channels
# assume that the threshold values will be a 13-bit signed integer
max_th_value = 2**12 - 1
# The threshold_table is numpy array that holds the threshold values for all channels
threshold_table = np.empty([ch, n + 1], dtype=np.int32)
# Compute increasing order or decreasing order
is_inc_order = [True for i in range(ch)]
if quantizer_conv_weights.op_type == 'BinaryChannelWiseMeanScalingQuantizer':
for c in range(ch):
is_inc_order[c] = scaling_factor[c] > 0
else:
for c in range(ch):
is_inc_order[c] = scaling_factor > 0
for op in p[:-1]:
if op.op_type == 'BatchNormalization':
bn_scale = op.input_ops['scale'].data
for c in range(ch):
if bn_scale[c] < 0:
is_inc_order[c] = not is_inc_order[c]
for c in range(ch):
threshold_table[c, -1] = 1 \
if is_inc_order[c] else -1
# Compute threshold (t0, t1, t2)
th_val = [0.5 + i for i in range(n)]
for th_id, th_v in enumerate(th_val):
init_threshold = np.full(ch, th_v, dtype=np.float64)
# run calculation in reverse order, for example, q -> bn -> scaling
trans_th = {'data': init_threshold}
for op in p[:-1]:
trans_th = op.de_run(**trans_th)
threshold = (trans_th['data'] *
np.float64(n)) / (np.float64(max_v) * scaling_factor)
# take care of threshold values that are larger than 13-bit signed integer
threshold[threshold > max_th_value] = max_th_value
threshold[threshold < -max_th_value] = -max_th_value
for ch_id, th_per_ch in enumerate(threshold):
threshold_table[ch_id, th_id] = int(math.ceil(th_per_ch)) \
if is_inc_order[ch_id] else int(math.floor(th_per_ch))
bits_per_word = 32
rem = (bits_per_word - ch % bits_per_word) % bits_per_word
pad = np.ones((rem, n + 1), dtype=np.int32)
threshold_table = np.vstack((threshold_table, pad))
# Put the thresholds into list
conv_node.thresholds = threshold_table.flatten().tolist()
# get nodes to be removed after being disconnected
get_nodes_in_branch(activation_quantizer_node, conv_node,
to_be_removed)
# Disconnect the outputs of the quantizer
out_ops = activation_quantizer_node.output_ops['output']
for output_node in out_ops:
for input_name, input_node in output_node.input_ops.items():
if input_node == activation_quantizer_node:
output_node.add_input(input_name, conv_node)
# Disconnect the outputs of the conv
conv_node.remove_output('Y')
conv_node.add_outputs({'Y': out_ops})
for op in to_be_removed:
graph.remove_op(op)
