def __init__(self,
node_name,
cname,
matrixadd_params,
pad_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_activation_op(act_params.activation,
force_relu=force_relu)
else:
self.at_act_params = NO_ACTIVATION
self.padtop = pad_params.padding[0][0]
self.padbot = pad_params.padding[0][1]
self.padded_idx = 0 if matrixadd_params.in_dims[0].size(
) > matrixadd_params.in_dims[1].size() else 1
self.matrixadd_params = matrixadd_params
dimensions0 = make_three_dims(matrixadd_params.in_dims[0])
dimensions1 = make_three_dims(matrixadd_params.in_dims[1])
self.feat_dim = max(dimensions0[0], dimensions1[0])
self.width = dimensions0[1]
self.height = dimensions0[2]
def __init__(self,
node_name,
cname,
matmul_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_activation_op(act_params.activation,
force_relu=force_relu)
else:
self.at_act_params = NO_ACTIVATION
self.matmul_params = matmul_params
self.lineM1 = matmul_params.in_dims[0][0]
self.colM1 = matmul_params.in_dims[0][1]
self.lineM2 = matmul_params.in_dims[1][0]
self.colM2 = matmul_params.in_dims[1][1]
def __init__(self, node_name, cname, matrixadd_params, pad_params, act_params, at_ver=3, gen_ctrl=None, force_relu=True):
if gen_ctrl is None:
gen_ctrl = GenCtrl(None, cname=cname)
else:
gen_ctrl.cname = cname
if act_params is not None:
at_act_params = gen_activation_op(
act_params.activation, force_relu=force_relu)
else:
at_act_params = NO_ACTIVATION
padtop = pad_params.padding[0][0]
padbot = pad_params.padding[0][1]
padded_idx = 0 if matrixadd_params.in_dims[0].size(
) > matrixadd_params.in_dims[1].size() else 1
dimensions0 = make_three_dims(matrixadd_params.in_dims[0])
dimensions1 = make_three_dims(matrixadd_params.in_dims[1])
attrs = {
'feat': max(dimensions0[0], dimensions1[0]),
'width': dimensions0[1],
'height': dimensions0[2],
'padded_idx': padded_idx,
'padtop': padtop,
'padbot': padbot,
'act_oper': at_act_params
}
extra_attrs = {
'cname': cname,
'node_name': node_name
}
super().__init__(attrs, extra_attrs, gen_ctrl=gen_ctrl)
def __init__(self,
node_name,
cname,
matadd_params,
act_params=None,
force_relu=True,
gen_ctrl=None):
if gen_ctrl is None:
gen_ctrl = GenCtrl(None, cname=cname)
else:
gen_ctrl.cname = cname
if act_params is not None:
at_act_params = gen_activation_op(act_params.activation,
force_relu=force_relu)
else:
at_act_params = "KOP_NONE"
dimensions = make_three_dims(matadd_params.in_dims[0])
attrs = {
'feat': dimensions[0],
'width': dimensions[1],
'height': dimensions[2],
'act_oper': at_act_params
}
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,
pool_q,
act_params,
act_q,
force_relu,
gen_ctrl=None):
if gen_ctrl is None:
self.gen_ctrl = gen_ctrl = GenCtrl(None, cname=cname)
else:
gen_ctrl.cname = cname
self.gen_ctrl = gen_ctrl
at_pool_params = gen_globalpool_at_params(pool_params)
in_dim = pool_params.in_dims[0]
out_dim = pool_params.out_dims[0]
in_q = pool_q.in_qs[0]
out_q = pool_q.out_qs[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
feat = reduce_sz
height, width = balanced_divisors(in_dim.size() / reduce_sz)
if act_params is not None:
act_op = gen_activation_op(
act_params.activation,
force_relu=force_relu,
asymmetric=act_q.in_qs[0].zero_point != 0)
if out_dim is None:
out_dim = act_params.out_dims[0].expand_to_chw()
out_q = act_q.out_qs[0]
else:
act_op = "KOP_NONE"
attrs = {
'in_size':
in_q.dtype_bits // 8 if in_q.signed else -in_q.dtype_bits // 8,
'out_size':
out_q.dtype_bits // 8 if out_q.signed else -out_q.dtype_bits // 8,
'feat':
feat,
'width':
width,
'height':
height,
'kop_pool':
at_pool_params.GlobalPoolOper,
'kop_act':
act_op
}
extra_attrs = {'cname': cname, 'node_name': node_name}
super().__init__(attrs, extra_attrs, gen_ctrl=gen_ctrl)
def __init__(self,
node_name,
cname,
linear_params,
act_params,
linear_q,
act_q,
force_relu,
gen_ctrl=None):
if gen_ctrl is None:
gen_ctrl = GenCtrl(None, cname=cname)
else:
gen_ctrl.cname = cname
in_dim = linear_params.in_dims[0]
out_dim = linear_params.out_dims[0]
in_qs = linear_q.in_qs
mulbiases_q = linear_q.cache['mul_biases_q']
if act_params is not None:
at_act_params = gen_activation_op(
act_params.activation,
force_relu=force_relu,
asymmetric=act_q.in_qs[0].zero_point != 0)
if in_dim is None:
in_dim = act_params.in_dims[0]
if out_dim is None:
out_dim = act_params.out_dims[0]
out_qs = act_q.out_qs
else:
at_act_params = "KOP_NONE"
out_qs = linear_q.out_qs
attrs = {
'in_size':
in_qs[0].dtype_bits //
8 if in_qs[0].signed else -in_qs[0].dtype_bits // 8,
'out_size':
out_qs[0].dtype_bits //
8 if out_qs[0].signed else -out_qs[0].dtype_bits // 8,
'bias_size':
in_qs[2].dtype_bits // 8,
'scale_size':
mulbiases_q.dtype_bits // 8,
'filter_bits':
in_qs[1].bits,
'in_feat':
in_dim.size(),
'out_feat':
out_dim.size(),
'act_op':
at_act_params
}
extra_attrs = {'cname': cname, 'node_name': node_name}
super().__init__(attrs, extra_attrs, gen_ctrl=gen_ctrl)
def __init__(self, node_name, cname, matmul_params, matmul_qrec, act_params, 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
if act_params is not None:
act_op = gen_activation_op(
act_params.activation, force_relu=force_relu)
else:
act_op = 'KOP_NONE'
height_1 = matmul_params.in_dims[0][0]
width_1 = matmul_params.in_dims[0][1]
height_2 = matmul_params.in_dims[1][0]
width_2 = matmul_params.in_dims[1][1]
if len(matmul_params.in_dims) == 3:
bias_datasize = at_bits(matmul_qrec.in_qs[2])
matmul_op = 'KOP_MATMUL'
else:
bias_datasize = 0
matmul_op = 'KOP_MATMUL_NOBIAS'
if len(matmul_qrec.in_qs[1].scale) == 1:
matmul_op += '_SCALE_SCALAR'
if isinstance(matmul_params, MatMulTransposedParameters):
matmul_op += '_TRANSPOSED'
height_2 = matmul_params.in_dims[1][1]
width_2 = matmul_params.in_dims[1][0]
# attributes affecting generation
attrs = {
'height_1': height_1,
'width_1': width_1,
'height_2': height_2,
'width_2': width_2,
'bias_datasize': bias_datasize,
'matmul_op': matmul_op,
'act_op': act_op
}
# other attributes
extra_attrs = {
'cname': cname,
'node_name': node_name
}
super().__init__(attrs, extra_attrs, gen_ctrl=gen_ctrl)
def __init__(self, node_name, cname, matmul_params, matmul_qrec, act_params, 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
if act_params is not None:
act_op = gen_activation_op(
act_params.activation, force_relu=force_relu)
else:
act_op = 'KOP_NONE'
height_1 = matmul_params.in_dims[0][0]
width_1 = matmul_params.in_dims[0][1]
height_2 = matmul_params.in_dims[1][1]
width_2 = matmul_params.in_dims[1][0]
bias_datasize = at_bits(matmul_qrec.in_qs[2])
in1_datasize = at_bits(matmul_qrec.in_qs[0])
in2_datasize_bits = matmul_qrec.in_qs[1].bits
out_datasize = at_bits(matmul_qrec.out_qs[0])
matmul_op = 'KOP_MATMUL_TRANSPOSED'
# attributes affecting generation
attrs = {
'height_1': height_1,
'width_1': width_1,
'height_2': height_2,
'width_2': width_2,
'bias_datasize': bias_datasize,
'in1_datasize': in1_datasize,
'in2_datasize_bits': in2_datasize_bits,
'out_datasize': out_datasize,
'matmul_op': matmul_op,
'act_op': act_op
}
# other attributes
extra_attrs = {
'cname': cname,
'node_name': node_name
}
super().__init__(attrs, extra_attrs, gen_ctrl=gen_ctrl)
def __init__(self, node_name, cname, params, aparams, qrec, gen_ctrl=None):
if gen_ctrl is None:
gen_ctrl = GenCtrl(None, cname=cname)
else:
gen_ctrl.cname = cname
if isinstance(aparams,
ReluActivationParameters) and aparams.upper_bound:
gen_ctrl.ReluN = aparams.upper_bound
in_dims = params.in_dims
matop = "KOP_MATMUL" if len(in_dims) > 2 else "KOP_MATMUL_NOBIAS"
if isinstance(params, MatMulTransposedParameters):
matop += "_TRANSPOSED"
attrs = {
'ColM1':
in_dims[0][1],
'LineM1':
in_dims[0][0],
'ColM2':
in_dims[1][0] if isinstance(
params, MatMulTransposedParameters) else in_dims[1][1],
'LineM2':
in_dims[1][1] if isinstance(params, MatMulTransposedParameters)
else in_dims[1][0],
'Width':
0,
'Height':
0,
'Scx':
1,
'Scy':
1,
'kop_matmul':
matop,
'actoper':
gen_activation_op(aparams.activation if aparams else "none")
}
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,
pool_q,
act_params,
act_q,
force_relu,
gen_ctrl=None):
if gen_ctrl is None:
self.gen_ctrl = 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"
]:
hwc = True
gen_ctrl.hwc = 1
pad_compatibilities = []
at_pool_params = gen_pool_at_params(pool_params, pad_compatibilities)
in_dim = pool_params.in_dims[0]
out_dim = pool_params.out_dims[0]
in_q = pool_q.in_qs[0]
out_q = pool_q.out_qs[0]
if act_params is not None:
act_op = gen_activation_op(
act_params.activation,
force_relu=force_relu,
asymmetric=act_q.in_qs[0].zero_point != 0)
if out_dim is None:
out_dim = act_params.out_dims[0].expand_to_chw()
out_q = act_q.out_qs[0]
else:
act_op = "KOP_NONE"
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
attrs = {
'in_size':
in_q.dtype_bits // 8 if in_q.signed else -in_q.dtype_bits // 8,
'out_size':
out_q.dtype_bits // 8 if out_q.signed else -out_q.dtype_bits // 8,
'feat':
in_dim.c,
'width':
in_dim.w,
'height':
in_dim.h,
'kop_pool':
at_pool_params.PoolOper,
'fpx':
at_pool_params.Fpx,
'fpy':
at_pool_params.Fpy,
'dpx':
at_pool_params.Dpx,
'dpy':
at_pool_params.Dpy,
'spx':
at_pool_params.Spx,
'spy':
at_pool_params.Spy,
'pool_pad':
at_pool_params.PoolPad,
'kop_act':
act_op
}
extra_attrs = {'cname': cname, 'node_name': node_name}
super().__init__(attrs, extra_attrs, gen_ctrl=gen_ctrl)
def __init__(self, node_name, cname, conv_params, conv_q, pool_params, pool_q, act_params, act_q, force_relu, gen_ctrl=None):
if gen_ctrl is None:
self.gen_ctrl = gen_ctrl = GenCtrl(None, cname=cname)
else:
gen_ctrl.cname = cname
self.gen_ctrl = gen_ctrl
is_ne16 = conv_q.cache.get('ne16')
hwc = False
if not is_ne16 and conv_params.ker_in_order and conv_params.ker_in_order[0] == ["h", "w", "c"]:
hwc = True
gen_ctrl.hwc = 1
if not is_ne16 and not hwc and conv_params.filter.h == 1 and conv_params.filter.w == 1 and gen_ctrl.enableim2col is None:
gen_ctrl.enableim2col = 1
in_q = filter_q = out_q = bias_q = None
in_dim = out_dim = None
pad_compatibilities = []
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]
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]
pad_val = in_q.zero_point[0]
if pool_params is not None:
at_pool_params = gen_pool_at_params(
pool_params, pad_compatibilities)
out_dim = pool_params.out_dims[0]
out_q = pool_q.out_qs[0]
else:
at_pool_params = NO_POOL
if act_params is not None:
act_op = gen_activation_op(
act_params.activation, force_relu=force_relu, asymmetric=act_q.in_qs[0].zero_point != 0)
if out_dim is None:
out_dim = act_params.out_dims[0].expand_to_chw()
out_q = act_q.out_qs[0]
else:
act_op = "KOP_NONE"
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
attrs = {
'in_size': in_q.dtype_bits//8 if in_q.signed else -in_q.dtype_bits//8,
'out_size': out_q.dtype_bits//8 if out_q.signed else -out_q.dtype_bits//8,
'bias_size': bias_q.dtype_bits//8,
'filter_bits': filter_q.bits,
'in_feat': in_dim.c,
'out_feat': out_dim.c,
'in_width': in_dim.w,
'in_height': in_dim.h,
'kop_conv': at_conv_params.ConvOper,
'fcx': at_conv_params.Fcx,
'fcy': at_conv_params.Fcy,
'dcx': at_conv_params.Dcx,
'dcy': at_conv_params.Dcy,
'scx': at_conv_params.Scx,
'scy': at_conv_params.Scy,
'conv_pad': at_conv_params.ConvPad,
'pad_value': pad_val,
'kop_pool': at_pool_params.PoolOper,
'fpx': at_pool_params.Fpx,
'fpy': at_pool_params.Fpy,
'dpx': at_pool_params.Dpx,
'dpy': at_pool_params.Dpy,
'spx': at_pool_params.Spx,
'spy': at_pool_params.Spy,
'pool_pad': at_pool_params.PoolPad,
'kop_act': act_op
}
extra_attrs = {
'cname': cname,
'node_name': node_name
}
super().__init__(attrs, extra_attrs, gen_ctrl=gen_ctrl)
