def __init__(self,
node_name,
cname,
pool_params,
act_params,
gen_ctrl=None,
at_ver=3):
if gen_ctrl is None:
self.gen_ctrl = GenCtrl(None, cname=cname)
else:
gen_ctrl.cname = cname
self.gen_ctrl = gen_ctrl
if act_params is not None:
self.at_act_params = gen_active_at_params(act_params,
force_relu=True)
else:
self.at_act_params = NO_ACTIVATION
self.at_globalpool_params = gen_globalpool_at_params(pool_params)
self.in_dim = pool_params.in_dims[0]
self.out_dim = pool_params.out_dims[0]
self.cname = cname
self.node_name = node_name
self.at_ver = at_ver
def __init__(self,
node_name,
cname,
pool_params,
act_params,
gen_ctrl=None,
at_ver=3,
force_relu=True):
if gen_ctrl is None:
self.gen_ctrl = GenCtrl(None, cname=cname)
else:
gen_ctrl.cname = cname
self.gen_ctrl = gen_ctrl
if act_params is not None:
self.at_act_params = gen_active_at_params(act_params,
force_relu=force_relu)
else:
self.at_act_params = NO_ACTIVATION
self.at_globalpool_params = gen_globalpool_at_params(pool_params)
in_dim = pool_params.in_dims[0]
reduce_sz = reduce(lambda x, y: x * y,
(sz for idx, sz in enumerate(in_dim.shape)
if idx not in pool_params.axis), 1)
#self.c = in_dim.size()/reduce_sz
self.c = reduce_sz
(self.h, self.w) = balanced_divisors(in_dim.size() / reduce_sz)
self.cname = cname
self.node_name = node_name
self.at_ver = at_ver
def __init__(self,
node_name,
cname,
matrixadd_params,
act_params,
act_q=None,
at_ver=3,
gen_ctrl=None,
force_relu=True):
if gen_ctrl is None:
self.gen_ctrl = gen_ctrl = GenCtrl(None, cname=cname)
else:
gen_ctrl.cname = cname
self.gen_ctrl = gen_ctrl
self.cname = cname
self.node_name = node_name
self.at_ver = at_ver
if act_params is not None:
self.at_act_params = gen_active_at_params(
act_params,
force_relu=force_relu,
asymmetric=act_q.in_qs[0].zero_point != 0)
else:
self.at_act_params = NO_ACTIVATION
self.matrixadd_params = matrixadd_params
dimensions = make_three_dims(matrixadd_params.in_dims[0])
self.feat_dim = dimensions[0]
self.width = dimensions[1]
self.height = dimensions[2]
def __init__(self,
node_name,
cname,
tens_vect_mul_params,
act_params,
at_ver=3,
gen_ctrl=None,
force_relu=True):
if gen_ctrl is None:
self.gen_ctrl = gen_ctrl = GenCtrl(None, cname=cname)
else:
gen_ctrl.cname = cname
self.gen_ctrl = gen_ctrl
self.cname = cname
self.node_name = node_name
self.at_ver = at_ver
if act_params is not None:
self.at_act_params = gen_active_at_params(act_params,
force_relu=force_relu)
else:
self.at_act_params = NO_ACTIVATION
self.tens_vect_mul_params = tens_vect_mul_params
dimensions = tens_vect_mul_params.in_dims[0]
self.feat_dim = dimensions[0]
self.width = dimensions[1]
self.height = dimensions[2]
def __init__(self, node_name, cname, tens_vect_mul_params, act_params, at_ver=3, gen_ctrl=None, force_relu=True):
if gen_ctrl is None:
self.gen_ctrl = gen_ctrl = GenCtrl(None, cname=cname)
else:
gen_ctrl.cname = cname
self.gen_ctrl = gen_ctrl
self.cname = cname
self.node_name = node_name
self.at_ver = at_ver
if act_params is not None:
self.at_act_params = gen_active_at_params(
act_params, force_relu=force_relu)
else:
self.at_act_params = NO_ACTIVATION
self.tens_vect_mul_params = tens_vect_mul_params
if tens_vect_mul_params.in_dims[0] == tens_vect_mul_params.in_dims[1]:
# broadcast matrix * matrix
self.feat_dim = tens_vect_mul_params.in_dims[0].size()
self.height = 1
self.width = 1
else:
# probably need more logic here to handle
dimensions = tens_vect_mul_params.in_dims[0]
self.feat_dim = dimensions[0]
self.width = dimensions[1]
self.height = dimensions[2]
def __init__(self,
node_name,
cname,
linear_params,
linear_q,
act_params,
act_q,
at_ver=3,
gen_ctrl=None,
force_relu=True):
if gen_ctrl is None:
self.gen_ctrl = GenCtrl(None, cname=cname)
else:
gen_ctrl.cname = cname
self.gen_ctrl = gen_ctrl
assert linear_params is not None, "linear should always be included"
at_linear_params = gen_linear_at_params(linear_params)
in_dim = linear_params.in_dims[0]
out_dim = linear_params.out_dims[0]
filter_q = linear_q.in_qs[1]
in_q = linear_q.in_qs[0]
out_q = linear_q.out_qs[0]
bias_q = linear_q.in_qs[2]
if act_params is not None:
at_act_params = gen_active_at_params(act_params,
force_relu=force_relu)
if in_dim is None:
in_dim = act_params.in_dims[0]
if out_dim is None:
out_dim = act_params.out_dims[0]
if in_q is None:
in_q = act_q.in_qs[0]
out_q = act_q.out_qs[0]
else:
at_act_params = NO_ACTIVATION
self.at_linear_params = at_linear_params
self.in_dim = in_dim.size()
self.out_dim = out_dim.size()
self.in_q = in_q
self.bias_q = bias_q
self.out_q = out_q
self.filter_q = filter_q
self.at_act_params = at_act_params
self.cname = cname
self.node_name = node_name
self.at_ver = at_ver
def __init__(self,
node_name,
cname,
pool_params,
act_params,
qrec,
act_q=None,
gen_ctrl=None,
at_ver=3,
force_relu=True):
if gen_ctrl is None:
self.gen_ctrl = GenCtrl(None, cname=cname)
else:
gen_ctrl.cname = cname
self.gen_ctrl = gen_ctrl
if pool_params.ker_in_order and pool_params.ker_in_order[0] == [
"h", "w", "c"
]:
self.gen_ctrl.hwc = 1
if not qrec.out_qs[0].signed:
self.gen_ctrl.output_datasize = -qrec.out_qs[0].dtype_bits // 8
if not qrec.in_qs[0].signed:
self.gen_ctrl.input_datasize = -qrec.in_qs[0].dtype_bits // 8
if act_params is not None:
self.at_act_params = gen_active_at_params(
act_params,
force_relu=force_relu,
asymmetric=act_q.in_qs[0].zero_point != 0)
else:
self.at_act_params = NO_ACTIVATION
pad_compatibilities = []
self.at_pool_params = gen_pool_at_params(pool_params,
pad_compatibilities)
self.in_dim = pool_params.in_dims[0]
self.out_dim = pool_params.out_dims[0]
self.cname = cname
self.node_name = node_name
self.at_ver = at_ver
def __init__(self,
node_name,
cname,
matadd_params,
act_params=None,
add_q=None,
act_q=None,
force_relu=True,
gen_ctrl=None):
if gen_ctrl is None:
gen_ctrl = GenCtrl(None, cname=cname)
else:
gen_ctrl.cname = cname
if not add_q.out_qs[0].signed:
gen_ctrl.output_datasize = -add_q.out_qs[0].dtype_bits // 8
if not add_q.in_qs[0].signed:
gen_ctrl.input_datasize = -add_q.in_qs[0].dtype_bits // 8
if act_params is not None:
at_act_params = gen_active_at_params(
act_params,
force_relu=force_relu,
asymmetric=act_q.in_qs[0].zero_point != 0)
else:
at_act_params = NO_ACTIVATION
dimensions = make_three_dims(matadd_params.in_dims[0])
attrs = {
'feat': dimensions[0],
'width': dimensions[1],
'height': dimensions[2],
'act_oper': at_act_params.ReLUOper
}
extra_attrs = {'cname': cname, 'node_name': node_name}
super().__init__(attrs, extra_attrs, gen_ctrl=gen_ctrl)
def __init__(self,
node_name,
cname,
pool_params,
act_params,
act_q=None,
gen_ctrl=None,
at_ver=3,
force_relu=True):
if gen_ctrl is None:
self.gen_ctrl = GenCtrl(None, cname=cname)
else:
gen_ctrl.cname = cname
self.gen_ctrl = gen_ctrl
if pool_params.ker_in_order and pool_params.ker_in_order[0] == [
"h", "w", "c"
]:
self.gen_ctrl.hwc = 1
if act_params is not None:
self.at_act_params = gen_active_at_params(
act_params,
force_relu=force_relu,
asymmetric=act_q.in_qs[0].zero_point != 0)
else:
self.at_act_params = NO_ACTIVATION
self.at_globalpool_params = gen_globalpool_at_params(pool_params)
in_dim = pool_params.in_dims[0]
reduce_sz = reduce(lambda x, y: x * y,
(sz for idx, sz in enumerate(in_dim.shape)
if idx not in pool_params.axis), 1)
#self.c = in_dim.size()/reduce_sz
self.c = reduce_sz
(self.h, self.w) = balanced_divisors(in_dim.size() / reduce_sz)
self.cname = cname
self.node_name = node_name
self.at_ver = at_ver
def __init__(self,
node_name,
cname,
conv_params,
conv_q,
pool_params,
pool_q,
act_params,
act_q,
at_ver=3,
gen_ctrl=None):
self.ne16 = False
if gen_ctrl is None:
self.gen_ctrl = gen_ctrl = GenCtrl(None, cname=cname)
else:
gen_ctrl.cname = cname
self.gen_ctrl = gen_ctrl
in_q = filter_q = out_q = bias_q = mul_biases_q = None
in_dim = out_dim = None
pad_compatibilities = []
if conv_params is not None:
if conv_params.ker_in_order and conv_params.ker_in_order[0] == [
"h", "w", "c"
]:
self.hwc = True
self.gen_ctrl.hwc = 1
at_conv_params = gen_conv_at_params(conv_params,
pad_compatibilities)
in_dim = conv_params.in_dims[0]
out_dim = conv_params.out_dims[0]
# Set ENABLEIM2COL on 1x1 filters by default
if conv_params.filter.h == 1 and conv_params.filter.w == 1 and gen_ctrl.enableim2col is None:
gen_ctrl.enableim2col = 1
filter_q = conv_q.in_qs[1]
in_q = conv_q.in_qs[0]
out_q = conv_q.out_qs[0]
bias_q = conv_q.in_qs[2]
if conv_params.has_mul_bias:
mul_biases_q = conv_q.mul_biases_q
self.ne16 = conv_q.cache.get('ne16')
else:
at_conv_params = NO_CONV
if pool_params is not None:
if pool_params.ker_in_order and pool_params.ker_in_order[0] == [
"h", "w", "c"
]:
self.hwc = True
self.gen_ctrl.hwc = 1
at_pool_params = gen_pool_at_params(pool_params,
pad_compatibilities)
if in_dim is None:
in_dim = pool_params.in_dims[0]
out_dim = pool_params.out_dims[0]
if in_q is None:
in_q = pool_q.in_qs[0]
out_q = pool_q.out_qs[0]
else:
at_pool_params = NO_POOL
if act_params is not None:
if act_params.ker_in_order and act_params.ker_in_order[0] == [
"h", "w", "c"
]:
self.hwc = True
self.gen_ctrl.hwc = 1
if in_q is None:
in_q = act_q.in_qs[0]
at_act_params = gen_active_at_params(
act_params,
force_relu=False,
asymmetric=act_q.in_qs[0].zero_point != 0)
if isinstance(act_params,
ReluActivationParameters) and act_params.upper_bound:
self.gen_ctrl.relun = act_params.upper_bound
if in_dim is None:
in_dim = act_params.in_dims[0].expand_to_chw()
if out_dim is None:
out_dim = act_params.out_dims[0].expand_to_chw()
out_q = act_q.out_qs[0]
else:
at_act_params = NO_ACTIVATION
if pad_compatibilities:
reduction = PadDim.pad_compatibility_reduce(
*pad_compatibilities,
"convolution padding is not compatible with pool padding")
if not reduction[2]: # default is balanced pad left
at_pad_ctrl = next(i for i, v in enumerate(reduction) if v)
LOG.debug("%s: generating pad control block", node_name)
self.gen_ctrl.PadType = at_pad_ctrl
self.in_dim = in_dim
self.out_dim = out_dim
self.in_q = in_q
self.bias_q = bias_q
self.out_q = out_q
self.filter_q = filter_q
self.mul_biases_q = mul_biases_q
self.at_act_params = at_act_params
self.at_pool_params = at_pool_params
self.at_conv_params = at_conv_params
self.cname = cname
self.node_name = node_name
self.at_ver = at_ver
def __init__(self,
node_name,
cname,
conv_params,
conv_q,
pool_params,
pool_q,
act_params,
act_q,
at_ver=3,
gen_ctrl=None,
force_relu=True):
if gen_ctrl is None:
self.gen_ctrl = gen_ctrl = GenCtrl(None, cname=cname)
else:
gen_ctrl.cname = cname
self.gen_ctrl = gen_ctrl
in_q = filter_q = out_q = bias_q = mul_biases_q = None
in_dim = out_dim = None
pad_compatibilities = []
if conv_params is not None:
at_conv_params = gen_conv_at_params(conv_params,
pad_compatibilities)
in_dim = conv_params.in_dims[0]
out_dim = conv_params.out_dims[0]
# Set ENABLEIM2COL on 1x1 filters by default
if conv_params.filter.h == 1 and conv_params.filter.w == 1 and gen_ctrl.enableim2col is None:
gen_ctrl.enableim2col = 1
filter_q = conv_q.in_qs[1]
in_q = conv_q.in_qs[0]
out_q = conv_q.out_qs[0]
bias_q = conv_q.in_qs[2]
if conv_params.has_mul_bias:
mul_biases_q = conv_q.mul_biases_q
else:
at_conv_params = NO_CONV
if pool_params is not None:
at_pool_params = gen_pool_at_params(pool_params,
pad_compatibilities)
if in_dim is None:
in_dim = pool_params.in_dims[0]
out_dim = pool_params.out_dims[0]
if in_q is None:
in_q = pool_q.in_qs[0]
out_q = pool_q.out_qs[0]
else:
at_pool_params = NO_POOL
if act_params is not None:
at_act_params = gen_active_at_params(act_params,
force_relu=force_relu)
if in_dim is None:
in_dim = act_params.in_dims[0].expand_to_chw()
if out_dim is None:
out_dim = act_params.out_dims[0].expand_to_chw()
if in_q is None:
in_q = act_q.in_qs[0]
out_q = act_q.out_qs[0]
else:
at_act_params = NO_ACTIVATION
if pad_compatibilities:
reduction = PadDim.pad_compatibility_reduce(
*pad_compatibilities,
"convolution padding is not compatible with pool padding")
if not reduction[2]: # default is balanced pad left
at_pad_ctrl = next(i for i, v in enumerate(reduction) if v)
LOG.debug("%s: generating pad control block", node_name)
self.gen_ctrl.PadType = at_pad_ctrl
self.in_dim = in_dim
self.out_dim = out_dim
self.in_q = in_q
self.bias_q = bias_q
self.out_q = out_q
self.filter_q = filter_q
self.mul_biases_q = mul_biases_q
self.at_act_params = at_act_params
self.at_pool_params = at_pool_params
self.at_conv_params = at_conv_params
self.cname = cname
self.node_name = node_name
self.at_ver = at_ver
