def globals_generator(cls, gen, node, qrec, pnode, fnode) -> bool:
if isinstance(pnode, FcParameters):
gen_scales(gen, pnode, pnode, qrec)
infos, infos_comment = np.array([0, 0, 0, 0, 0]), "no activation"
fnode = pnode
filt_q = qrec
elif isinstance(pnode, LinearFusionParameters) and isinstance(
fnode, FcParameters) and pnode.fusion_type == "linear_active":
cnodes = pnode.contained_nodes()
quants = [
gen.G.quantization[NodeId(pnode, fnode)] for fnode in cnodes
]
filt_q = quants[0]
gen_scales(gen, pnode, cnodes[0], quants[0])
infos, infos_comment = gen_act_infos(cnodes[1], quants[1])
else:
return False
infos = np.append(infos, [0, 0, 0, 0])
comment = str.format("BiasQ: {}", 0) + infos_comment
infos[5] = 0 # BiasQ
if filt_q.cache.get('ne16'):
conv_mul_bias = filt_q.cache.get('mul_biases_q')
prenorm = conv_mul_bias.pre_normalization if isinstance(
conv_mul_bias, MultMulBiasScaleQType) else 0
pad_value = np.array(filt_q.in_qs[0].zero_point).astype(np.int16)
pad_value1 = np.bitwise_and(pad_value, 0xFF)
pad_value2 = np.bitwise_and(pad_value, 0xFF00) >> 8
w_offset = -np.array(filt_q.in_qs[1].zero_point).astype(np.int32)
w_offset1 = np.bitwise_and(w_offset, 0xFF)
w_offset2 = np.bitwise_and(w_offset, 0xFF00) >> 8
w_offset3 = np.bitwise_and(w_offset, 0xFF0000) >> 16
w_offset4 = np.bitwise_and(w_offset, 0xFF000000) >> 24
infos = np.append(
infos,
verify_scalar([
prenorm if prenorm else 0, pad_value1, pad_value2,
w_offset1, w_offset2, w_offset3, w_offset4
]))
cname, file_name = gen_constant(gen, pnode, fnode, INFOS)
const_info = ConstantInfo(file_name,
QType.Pow2(bits=8, q=0, signed=True),
contents=infos)
gen.globals.append(
GlobalArgInfo("int8",
cname,
gen.opts['default_global_home_location'],
gen.opts['default_global_exec_location'],
const_info=const_info,
comment=comment))
return True
def globals_generator(cls, gen, node, qrec, pnode, fnode) -> bool:
if isinstance(pnode, MatMulOpParameters):
mul_node = pnode
mul_qrec = qrec
fnode = pnode
infos, comment = np.array([0, 0, 0, 0, 0]), "no activation"
elif isinstance(pnode, MatMulOpFusionParameters) and isinstance(fnode, MatMulOpParameters):
cnodes = pnode.contained_nodes()
quants = [gen.G.quantization[NodeId(
pnode, fnode)] for fnode in cnodes]
mul_node = cnodes[0]
mul_qrec = quants[0]
infos, comment = gen_act_infos(cnodes[1], quants[1])
else:
return False
if len(mul_qrec.in_qs[1].scale) > 1:
gen_scales(gen, pnode, mul_node, mul_qrec)
pl_scale = 0
pl_scalen = 0
else:
pl_scale = mul_qrec.cache['mul_biases_q'].qbiases[0]
pl_scalen = mul_qrec.cache['mul_biases_q'].qnorms[0]
infos = np.append(infos, [0, 0, pl_scale, pl_scalen])
if mul_qrec.cache.get('ne16'):
conv_mul_bias = mul_qrec.cache.get('mul_biases_q')
prenorm = conv_mul_bias.pre_normalization if isinstance(conv_mul_bias, MultMulBiasScaleQType) else 0
pad_value = np.array(mul_qrec.in_qs[0].zero_point).astype(np.int16)
pad_value1 = np.bitwise_and(pad_value, 0xFF)
pad_value2 = np.bitwise_and(pad_value, 0xFF00) >> 8
w_offset = -np.array(mul_qrec.in_qs[1].zero_point).astype(np.int32)
w_offset1 = np.bitwise_and(w_offset, 0xFF)
w_offset2 = np.bitwise_and(w_offset, 0xFF00) >> 8
w_offset3 = np.bitwise_and(w_offset, 0xFF0000) >> 16
w_offset4 = np.bitwise_and(w_offset, 0xFF000000) >> 24
infos = np.append(
infos, verify_scalar([prenorm if prenorm else 0, pad_value1, pad_value2, w_offset1, w_offset2, w_offset3, w_offset4]))
cname, file_name = gen_constant(gen, pnode, fnode, INFOS)
const_info = ConstantInfo(file_name, QType.Pow2(bits=8, q=0, signed=True), contents=infos)
gen.globals.append(GlobalArgInfo("int8", cname,
gen.opts['default_global_home_location'],
gen.opts['default_global_exec_location'],
const_info=const_info,
comment=comment))
return True
def globals_generator(cls, gen, node, qrec, pnode, fnode) -> bool:
if isinstance(pnode, (GlobalPoolingParameters, PoolingParameters,
GlobalSumPoolParameters)):
compute_in_out_scale(qrec)
infos, comment = np.array([
qrec.cache['scale_mul_biases_q'].qbiases[0],
qrec.cache['scale_mul_biases_q'].qnorms[0], 0, 0, 0
]), "no activation"
fnode = pnode
pool_q = qrec
elif isinstance(pnode, ActivationFusion) and isinstance(
fnode, (GlobalPoolingParameters, PoolingParameters)):
cnodes = pnode.contained_nodes()
quants = [
gen.G.quantization[NodeId(pnode, fnode)] for fnode in cnodes
]
pool_q = quants[0]
infos, comment = gen_act_infos(cnodes[1], quants[1])
else:
return False
infos = np.append(infos, [0, 0, 0, 0])
if isinstance(fnode, GlobalSumPoolParameters):
compute_in_out_scale(pool_q, in_idx=0, out_idx=0)
infos[0] = 0
infos[1] = 0
infos[5] = pool_q.cache['scale_mul_biases_q'].qbiases[0]
infos[6] = pool_q.cache['scale_mul_biases_q'].qnorms[0]
cname, file_name = gen_constant(gen, pnode, fnode, INFOS)
const_info = ConstantInfo(file_name,
QType.Pow2(bits=8, q=0, signed=True),
contents=infos)
gen.globals.append(
GlobalArgInfo("int8",
cname,
gen.opts['default_global_home_location'],
gen.opts['default_global_exec_location'],
const_info=const_info,
comment=comment))
return True
def globals_generator(cls, gen, node, qrec, pnode, fnode) -> bool:
if not cls.cache_values(node, qrec):
return False
in_q = qrec.in_qs[0]
out_q = qrec.out_qs[0]
comment = f'in q: {in_q} out_q: {out_q}'
if qrec.cache['kernel_type'] == 'KOP_CONVERT_FP_FP_ZEROPOINT':
bits = 8 if in_q.dtype in [np.int8, np.uint8] else 16
if in_q.signed:
offset = ((int(math.pow(2, bits)) + in_q.zero_point[0] -
out_q.zero_point[0]) %
int(math.pow(2, bits))).astype(out_q.dtype)
else:
offset = (int(math.pow(2, bits)) - in_q.zero_point[0] +
out_q.zero_point[0]).astype(out_q.dtype)
contents = np.array(list(offset.tobytes()) + ([0] * 7),
dtype=np.uint8)
elif qrec.cache['kernel_type'] == 'KOP_CONVERT_FP_FP':
# no infos needed
return True
elif qrec.cache['kernel_type'] == 'KOP_CONVERT_FP_FP_SCALE':
scale = in_q.scale / out_q.scale
in_abs_zp = in_q.zero_point.astype(np.int32)
out_abs_zp = out_q.zero_point.astype(np.int32)
if out_q.bits > in_q.bits:
zero_adjust = (np.round(-in_abs_zp * scale) +
out_abs_zp).astype(np.int32)
else:
zero_adjust = (-in_abs_zp +
np.round(out_abs_zp * 1 / scale)).astype(
np.int32)
zero_adjust = list(zero_adjust.tobytes())
if len(scale) > 1:
raise NotImplementedError(
'multiscale conversion not supported')
scale = scale[0]
if in_q.dtype_bits == 8 and out_q.dtype_bits == 16:
# scale Q16 * Q8 OK
scale_adjust = MultMulBiasScaleQType(scale=scale,
dtype=np.int16,
available_bits=16)
else:
scale_adjust = MultMulBiasScaleQType(scale=scale,
dtype=np.int8,
available_bits=8)
qbias = list(scale_adjust.qbiases.tobytes())
qbias = qbias + [0] * (2 - len(qbias))
qnorm = list(scale_adjust.qnorms.tobytes())
contents = np.array(zero_adjust + qbias + qnorm + [0],
dtype=np.int8)
elif qrec.cache['kernel_type'] == 'KOP_CONVERT_FL_FP':
qbias = list((1 / out_q.scale).astype(np.float32).tobytes())
zero_adjust = list((out_q.zero_point.astype(np.int32) *
out_q.scale).astype(np.float32).tobytes())
contents = np.array(zero_adjust + qbias, dtype=np.int8)
elif qrec.cache['kernel_type'] == 'KOP_CONVERT_FP_FL':
qbias = list((in_q.scale).astype(np.float32).tobytes())
zero_adjust = list((-in_q.zero_point.astype(np.int32)).astype(
np.float32).tobytes())
contents = np.array(zero_adjust + qbias, dtype=np.int8)
else:
raise ValueError(f"strange dtype change in {pnode.name}")
cname, file_name = gen_constant(gen, pnode, pnode, INFOS)
const_info = ConstantInfo(file_name,
QType.Pow2(bits=8, q=0, signed=True),
contents=contents)
gen.globals.append(
GlobalArgInfo("int8",
cname,
gen.opts['default_global_home_location'],
gen.opts['default_global_exec_location'],
const_info=const_info,
comment=comment))
def globals_generator(cls, gen, node, qrec, pnode, fnode) -> bool:
names = {val: idx for idx, val in enumerate(RNNParameters.INPUT_NAMES)}
w_q = qrec.in_qs[names['r_2_i_w']]
out_q = qrec.out_qs[0]
out_scale = qrec.cache["s_2_o_q"]
assert len(w_q.zero_point) == 1
assert len(out_scale.qbiases) == 1
assert len(out_scale.qnorms) == 1
if out_q.dtype == np.uint8:
if qrec.cache['act_qtype']:
min_val = qrec.cache['act_qtype'].quantize(-1)
max_val = qrec.cache['act_qtype'].quantize(1)
else:
min_val = max_val = 0
contents = np.array([
min_val, max_val, (-w_q.zero_point[0]).astype(np.int8),
out_q.zero_point[0], 0, out_scale.qbiases[0].astype(
np.int8), out_scale.qnorms[0].astype(np.int8), 0, 0
],
dtype=np.int8)
else:
out_zp = out_q.zero_point[0].astype(np.uint16)
contents = np.array([
0, 0, (-w_q.zero_point[0]).astype(np.int8), out_zp & 0xff,
out_zp >> 8, out_scale.qbiases[0].astype(
np.int8), out_scale.qnorms[0].astype(
np.int8), qrec.cache["i_2_s_q"].pre_normalization,
qrec.cache["s_2_s_q"].pre_normalization
],
dtype=np.int8)
comment = f"A0: {1} B0: {-1}, ZP: {w_q.zero_point}, OutS: {out_scale.qbiases[0]}, OutN: {out_scale.qnorms[0]}"
cname, file_name = gen_constant(gen, pnode, pnode, INFOS)
const_info = ConstantInfo(file_name,
QType.Pow2(bits=8, q=0, signed=True),
contents=contents)
gen.globals.append(
GlobalArgInfo("int8",
cname,
gen.opts['default_global_home_location'],
gen.opts['default_global_exec_location'],
const_info=const_info,
comment=comment))
state_scale = qrec.cache["s_2_s_q"]
if node.rnn_same_inout_scale:
contents = interleave(state_scale.qbiases, state_scale.qnorms)
else:
input_scale = qrec.cache["i_2_s_q"]
contents = interleave(state_scale.qbiases, input_scale.qbiases,
state_scale.qnorms, input_scale.qnorms)
cname, file_name = gen_constant(gen, pnode, pnode, "scalenorm")
const_info = ConstantInfo(file_name,
QType.Pow2(bits=8, q=0, signed=False),
contents=contents)
gen.globals.append(
GlobalArgInfo("uint8",
cname,
gen.opts['default_global_home_location'],
gen.opts['default_global_exec_location'],
const_info=const_info,
comment=f"{node.name} scales and norms"))
if node.rnn_states_as_inputs:
gen.globals.append(
GlobalResetArgInfo(f"{node.name}_Reset", 'AT_MEM_L2',
'AT_MEM_UNDEF'))
return True
def globals_generator(cls, gen, node, qrec, pnode, fnode) -> bool:
names = {val: idx for idx, val in enumerate(GRUParameters.INPUT_NAMES)}
scales = []
weight_zero = None
for gate in ['r', 'h', 'z']:
input_order = ['r', 'w'] if gate == 'h' else ['w', 'r']
for input_tensor in input_order:
scale_name = f'{input_tensor}_2_{gate}_q'
weight_name = f'{input_tensor}_2_{gate}_w'
if weight_zero is None:
weight_zero = qrec.in_qs[names[weight_name]].zero_point[0]
else:
assert weight_zero == qrec.in_qs[
names[weight_name]].zero_point[0]
qscale = qrec.cache[scale_name]
scales.append(qscale.qbiases)
scales.append(qscale.qnorms)
contents = interleave(*scales)
cname, file_name = gen_constant(gen, pnode, pnode, "scalenorm")
const_info = ConstantInfo(file_name,
QType.Pow2(bits=8, q=0, signed=False),
contents=contents)
gen.globals.append(
GlobalArgInfo("uint8",
cname,
gen.opts['default_global_home_location'],
gen.opts['default_global_exec_location'],
const_info=const_info,
comment=f"{node.name} scales and norms"))
if node.rnn_states_as_inputs:
gen.globals.append(
GlobalResetArgInfo(f"{node.name}_Reset", 'AT_MEM_L2',
'AT_MEM_UNDEF'))
out_q = qrec.out_qs[0]
sigmoid_table = interleave(SIGMOID_TABLE & 0xff,
SIGMOID_TABLE >> 8).astype(np.int8)
if out_q.dtype == np.uint8:
contents = np.concatenate(
(sigmoid_table,
np.array([-weight_zero.astype(np.int8), 0], dtype=np.int8)))
else:
contents = np.concatenate(
(sigmoid_table,
np.array([
-weight_zero.astype(np.int8), qrec.cache['gate_prenorm']
],
dtype=np.int8)))
comment = (f"WZP: {weight_zero}")
cname, file_name = gen_constant(gen, pnode, pnode, INFOS)
const_info = ConstantInfo(file_name,
QType.Pow2(bits=8, q=0, signed=True),
contents=contents)
gen.globals.append(
GlobalArgInfo("int8",
cname,
gen.opts['default_global_home_location'],
gen.opts['default_global_exec_location'],
const_info=const_info,
comment=comment))
if node.rnn_states_as_inputs:
gen.globals.append(
GlobalResetArgInfo(f"{node.name}_Reset", 'AT_MEM_L2',
'AT_MEM_UNDEF'))
return True
def mult8_infos_generator(gen, node, qrec, pnode, fnode) -> bool:
if fnode is not None:
return False
# if isinstance(pnode, Conv2DParameters):
# for_ne16 = qrec.cache.get('ne16')
# in_zero_point = qrec.in_qs[0].zero_point
# conv_mul_bias = qrec.cache.get('mul_biases_q')
# prenorm = conv_mul_bias.pre_normalization if isinstance(conv_mul_bias, MultMulBiasScaleQType) else 0
# act_infos(gen, pnode, pnode, None, None, prenorm=prenorm, extra1=0,
# for_ne16=for_ne16, in_zero_point=in_zero_point)
# elif isinstance(pnode, (GlobalPoolingParameters, PoolingParameters)):
# compute_in_out_scale(qrec)
# act_infos(gen, pnode, pnode, None, qrec)
elif isinstance(pnode, ActivationParameters):
act_infos(gen, pnode, pnode, pnode, gen.G.quantization[NodeId(pnode)])
# elif isinstance(pnode, ConvFusionParameters):
# cnodes = node.contained_nodes()
# quants = [gen.G.quantization[NodeId(node, fnode)] for fnode in cnodes]
# for_ne16 = any([qrec.cache.get('ne16') for qrec in quants])
# in_zero_point = quants[0].in_qs[0].zero_point
# for qrec in quants:
# compute_in_out_scale(qrec)
# if node.fusion_type.startswith('linear') or node.fusion_type.startswith('conv') or node.fusion_type.startswith('pool'):
# if node.fusion_type in ("pool_active"):
# act_infos(gen, pnode, cnodes[0], cnodes[1], quants[1],
# extra1=0, for_ne16=for_ne16, in_zero_point=in_zero_point)
# else:
# conv_mul_bias = quants[0].cache.get('mul_biases_q')
# prenorm = conv_mul_bias.pre_normalization if isinstance(conv_mul_bias, MultMulBiasScaleQType) else 0
# if node.fusion_type in ("conv_active_pool", "conv_active", "linear_active"):
# act_infos(gen, pnode, cnodes[0], cnodes[1], quants[1], prenorm=prenorm,
# extra1=0, for_ne16=for_ne16, in_zero_point=in_zero_point)
# elif node.fusion_type == "conv_pool_active":
# act_infos(gen, pnode, cnodes[0], cnodes[2], quants[2], prenorm=prenorm,
# extra1=0, for_ne16=for_ne16, in_zero_point=in_zero_point)
# elif node.fusion_type == "conv_pool":
# act_infos(gen, pnode, cnodes[0], None, None, prenorm=prenorm,
# extra1=0, for_ne16=for_ne16)
elif isinstance(pnode, MatrixMulParameters):
compute_in_out_scale(qrec, in_idx=(0, 1), out_idx=0)
act_infos(gen,
pnode,
pnode,
None,
None,
extra1=qrec.cache['scale_mul_biases_q'].qbiases[0],
extra2=qrec.cache['scale_mul_biases_q'].qnorms[0])
elif isinstance(pnode, SoftMaxParameters):
act_infos(gen, pnode, pnode, pnode, qrec)
# elif isinstance(pnode, ActivationFusionBase):
# cnodes = node.contained_nodes()
# quants = [gen.G.quantization[NodeId(node, fnode)] for fnode in cnodes]
# for qrec in quants:
# compute_in_out_scale(qrec)
# if isinstance(cnodes[0], (GlobalPoolingParameters, PoolingParameters)):
# act_infos(gen, pnode, cnodes[0], cnodes[1], quants[1])
# else:
# return False
# return True
elif isinstance(pnode, (MatMulOpParameters, MatMulOpFusionParameters)):
if isinstance(pnode, MatMulOpFusionParameters):
cnodes = node.contained_nodes()
quants = [
gen.G.quantization[NodeId(node, fnode)] for fnode in cnodes
]
mul_node = cnodes[0]
mul_qrec = quants[0]
act_node = cnodes[1]
act_qrec = quants[1]
else:
mul_node = pnode
mul_qrec = qrec
act_node = None
act_qrec = None
if len(pnode.in_dims) == 3 and len(mul_qrec.in_qs[0].scale) > 1:
gen_scales(gen, pnode, mul_node, mul_qrec)
extra3 = 0
extra4 = 0
else:
extra3 = mul_qrec.cache['mul_biases_q'].qbiases[0]
extra4 = mul_qrec.cache['mul_biases_q'].qnorms[0]
act_infos(gen,
pnode,
mul_node,
act_node,
act_qrec,
extra3=extra3,
extra4=extra4)
elif isinstance(pnode, QuantizeParameters):
in_q = qrec.in_qs[0]
out_q = qrec.out_qs[0]
comment = f'in q: {in_q} out_q: {out_q}'
if qrec.cache['kernel_type'] == 'KOP_CONVERT_FP_FP_ZEROPOINT':
bits = 8 if in_q.dtype == np.int8 else 16
if in_q.signed:
contents = ((int(math.pow(2, bits)) + in_q.zero_point[0] -
out_q.zero_point[0]) %
int(math.pow(2, bits))).astype(np.uint8)
else:
contents = (int(math.pow(2, bits)) - in_q.zero_point[0] +
out_q.zero_point[0]).astype(np.uint8)
# if in_q.dtype == np.int8 and out_q.dtype == np.uint8:
# if not np.allclose(in_q.scale, out_q.scale):
# return False
# if not np.all(in_q.zero_point == (out_q.zero_point - 128)):
# return False
# contents = (
# (256 + in_q.zero_point[0] - out_q.zero_point[0]) % 256).astype(np.uint8)
# elif in_q.dtype == np.uint8 and out_q.dtype == np.int8:
# if not np.allclose(in_q.scale, out_q.scale):
# return False
# if not np.all(in_q.zero_point == (out_q.zero_point - 128)):
# return False
# contents = (
# 256 - in_q.zero_point[0] + out_q.zero_point[0]).astype(np.uint8)
elif in_q.dtype == np.int8 and out_q.dtype == np.int16:
if qrec.cache['kernel_type'] == 'KOP_CONVERT_FP_FP':
return True
raise NotImplementedError()
elif in_q.dtype == np.int16 and out_q.dtype == np.int8:
if qrec.cache['kernel_type'] == 'KOP_CONVERT_FP_FP':
return True
raise NotImplementedError()
else:
raise ValueError(f"strange dtype change in {pnode.name}")
cname, file_name = gen_constant(gen, pnode, pnode, INFOS)
const_info = ConstantInfo(file_name,
QType.Pow2(bits=8, q=0, signed=True),
contents=contents)
gen.globals.append(
GlobalArgInfo("int8",
cname,
gen.opts['default_global_home_location'],
gen.opts['default_global_exec_location'],
const_info=const_info,
comment=comment))
else:
return False
return True
def act_infos(gen,
pnode,
fnode,
act_params,
act_q,
extra1=0,
extra2=0,
extra3=0,
extra4=0,
extra5=None,
extra6=None,
prenorm=0,
extra_name='',
for_ne16=False,
in_zero_point=0):
if isinstance(pnode, FilterParameters):
comment = str.format("BiasQ: {}", extra1)
elif isinstance(pnode, MatrixAddParameters):
comment = str.format(
"In1Scale: {} In1ScaleN: {} OutScale: {} OutScaleN: {}", extra1,
extra2, extra3, extra4)
else:
comment = ""
if act_params is None:
contents = np.array([0, 0, 0, 0, 0], dtype=np.int8)
elif isinstance(act_params, ReluActivationParameters):
compute_in_out_scale(act_q)
actscale = act_q.cache['scale_mul_biases_q'].qbiases[0]
actscalen = act_q.cache['scale_mul_biases_q'].qnorms[0]
if act_params.upper_bound is None: # or fnode is not None:
if act_q.in_qs[0].zero_point == 0:
contents = np.array([actscale, actscalen, 0, 0, 0],
dtype=np.int8)
if len(comment) == 0:
comment = "all 0"
else:
fac_1 = act_q.in_qs[0].zero_point
contents = np.array([actscale, actscalen, fac_1, 0, 0],
dtype=np.int8)
comment += str.format(
"in: {:05f} out: {:05f} A0: {} B0: 0 C0: 0",
act_q.in_qs[0].scale[0], act_q.out_qs[0].scale[0],
fac_1[0])
else:
if act_q.in_qs[0].zero_point == 0:
fac_1 = act_q.in_qs[0].quantize(act_params.upper_bound)
contents = np.array([actscale, actscalen, fac_1, 0, 0],
dtype=np.int8)
comment += str.format(
"in: {:05f} out: {:05f} A0: {} B0: 0 C0: 0",
act_q.in_qs[0].scale[0], act_q.out_qs[0].scale[0],
fac_1[0])
else:
fac_1 = act_q.in_qs[0].zero_point
fac_2 = act_q.in_qs[0].quantize(act_params.upper_bound)
contents = np.array([actscale, actscalen, fac_1, fac_2, 0],
dtype=np.int8)
comment += str.format(
"in: {:05f} out: {:05f} A0: {} B0: {} C0: 0",
act_q.in_qs[0].scale[0], act_q.out_qs[0].scale[0],
fac_1[0], fac_2[0])
elif isinstance(act_params, HSigmoidActivationParameters):
# currently combines all scaling factors into one scale and shift
assert act_q.in_qs[0].zero_point == 0 and act_q.out_qs[
0].zero_point == 0, "asymmetric not supported"
fac_1, upper_bound, _ = hsigmoid_mult_gen_factors(act_params, act_q)
contents = np.array([
act_q.cache['scale_mul_biases_q'].qbiases[0],
act_q.cache['scale_mul_biases_q'].qnorms[0], upper_bound, fac_1, 1
],
dtype=np.int8)
comment += str.format(
"in: {:05f} out: {:05f} qbias: {} qnorm: {} A0: {} B0: {} C0: 1",
act_q.in_qs[0].scale[0], act_q.out_qs[0].scale[0],
act_q.cache['scale_mul_biases_q'].qbiases[0],
act_q.cache['scale_mul_biases_q'].qnorms[0], upper_bound[0],
fac_1[0])
elif isinstance(act_params, HSwishActivationParameters):
# currently combines all scaling factors into one scale and shift
assert act_q.in_qs[0].zero_point == 0 and act_q.out_qs[
0].zero_point == 0, "asymmetric not supported"
fac_1, upper_bound, _ = hswish_mult_gen_factors(act_q)
contents = np.array([
act_q.cache['scale_mul_biases_q'].qbiases[0],
act_q.cache['scale_mul_biases_q'].qnorms[0], upper_bound, fac_1, 1
],
dtype=np.int8)
comment += str.format(
"in: {:05f} out: {:05f} qbias: {} qnorm: {} A0: {} B0: {} C0: 1",
act_q.in_qs[0].scale[0], act_q.out_qs[0].scale[0],
act_q.cache['scale_mul_biases_q'].qbiases[0],
act_q.cache['scale_mul_biases_q'].qnorms[0], upper_bound[0],
fac_1[0])
elif isinstance(act_params, SoftMaxParameters):
assert act_q.in_qs[0].zero_point == 0 and act_q.out_qs[
0].zero_point == 0, "asymmetric not supported"
norm = 15 + np.ceil(np.log2(act_q.in_qs[0].scale))
contents = np.array([norm, 0, 0, 0, 0], dtype=np.int8)
comment += str.format("in: {:05f} out: {:05f} NORM: {}",
act_q.in_qs[0].scale[0],
act_q.out_qs[0].scale[0], int(norm[0]))
elif isinstance(act_params, LeakyActivationParameters):
assert act_q.in_qs[0].zero_point == 0 and act_q.out_qs[
0].zero_point == 0, "asymmetric not supported"
compute_in_out_scale(act_q)
leak_factor_quant = leak_mult_gen_factor_q7(act_params)
contents = np.array([
act_q.cache['scale_mul_biases_q'].qbiases[0],
act_q.cache['scale_mul_biases_q'].qnorms[0], leak_factor_quant, 0,
0
],
dtype=np.int8)
comment += str.format(
"in: {:05f} out: {:05f} qbias: {} qnorm: {} A0: {} B0: x C0: x",
act_q.in_qs[0].scale[0], act_q.out_qs[0].scale[0],
act_q.cache['scale_mul_biases_q'].qbiases[0],
act_q.cache['scale_mul_biases_q'].qnorms[0], leak_factor_quant)
elif isinstance(act_params,
(SigmoidActivationParameters, TanHActivationParameters)):
assert act_q.in_qs[0].zero_point == 0 and act_q.out_qs[
0].zero_point == 0, "asymmetric not supported"
compute_in_out_scale(
act_q,
extra_scale=QType.Pow2(bits=32, q=7, signed=True).scale /
act_q.in_qs[0].scale)
contents = np.array([
act_q.cache['scale_mul_biases_q'].qbiases[0],
act_q.cache['scale_mul_biases_q'].qnorms[0], 0, 0, 0
],
dtype=np.int8)
comment += str.format(
"in: {:05f} out: {:05f} qbias: {} qnorm: {} A0: x B0: x C0: x",
act_q.in_qs[0].scale[0], act_q.out_qs[0].scale[0],
act_q.cache['scale_mul_biases_q'].qbiases[0],
act_q.cache['scale_mul_biases_q'].qnorms[0])
else:
raise NotImplementedError("activation tye not implemented")
if isinstance(pnode, (GlobalPoolingParameters, PoolingParameters)):
contents = np.array([
act_q.cache['scale_mul_biases_q'].qbiases[0],
act_q.cache['scale_mul_biases_q'].qnorms[0], 0, 0, 0
],
dtype=np.int8)
contents = np.append(contents, [extra1, extra2, extra3, extra4])
if extra5 is not None:
contents = np.append(contents, [extra5])
if extra6 is not None:
contents = np.append(contents, [extra6])
if for_ne16:
# append weights_offset and pad_val for ne16
# TODO - default config maybe in future
if isinstance(pnode, (ConvFusionParameters, LinearFusionParameters)):
filt_q = gen.G.quantization[NodeId(pnode, fnode)]
else:
filt_q = gen.G.quantization[NodeId(pnode)]
pad_value = np.array(in_zero_point).astype(np.int16)
pad_value1 = np.bitwise_and(pad_value, 0xFF)
pad_value2 = np.bitwise_and(pad_value, 0xFF00) >> 8
w_offset = -np.array(filt_q.in_qs[1].zero_point).astype(np.int32)
w_offset1 = np.bitwise_and(w_offset, 0xFF)
w_offset2 = np.bitwise_and(w_offset, 0xFF00) >> 8
w_offset3 = np.bitwise_and(w_offset, 0xFF0000) >> 16
w_offset4 = np.bitwise_and(w_offset, 0xFF000000) >> 24
contents = np.append(
contents, [[prenorm] if prenorm else [0], pad_value1, pad_value2,
w_offset1, w_offset2, w_offset3, w_offset4])
cname, file_name = gen_constant(gen, pnode, fnode, INFOS, extra_name)
const_info = ConstantInfo(file_name,
QType.Pow2(bits=8, q=0, signed=True),
contents=contents)
gen.globals.append(
GlobalArgInfo("int8",
cname,
gen.opts['default_global_home_location'],
gen.opts['default_global_exec_location'],
const_info=const_info,
comment=comment))
def globals_generator(cls, gen, node, qrec, pnode, fnode) -> bool:
names = {
val: idx
for idx, val in enumerate(LSTMParameters.INPUT_NAMES)
}
scales = []
weight_zero = None
for gate in ['i', 'c', 'f', 'o']:
for input_tensor in ['i', 'r']:
scale_name = f'{input_tensor}_2_{gate}_q'
weight_name = f'{input_tensor}_2_{gate}_w'
if weight_zero is None:
weight_zero = qrec.in_qs[names[weight_name]].zero_point[0]
else:
assert weight_zero == qrec.in_qs[
names[weight_name]].zero_point[0]
w_q = qrec.in_qs[names['r_2_i_w']]
qscale = qrec.cache[scale_name]
scales.append(qscale.qbiases)
scales.append(qscale.qnorms)
contents = interleave(*scales)
cname, file_name = gen_constant(gen, pnode, pnode, "scalenorm")
const_info = ConstantInfo(file_name,
QType.Pow2(bits=8, q=0, signed=False),
contents=contents)
gen.globals.append(
GlobalArgInfo("uint8",
cname,
gen.opts['default_global_home_location'],
gen.opts['default_global_exec_location'],
const_info=const_info,
comment=f"{node.name} scales and norms"))
if node.rnn_states_as_inputs:
gen.globals.append(
GlobalResetArgInfo(f"{node.name}_Reset", 'AT_MEM_L2',
'AT_MEM_UNDEF'))
out_q = qrec.out_qs[0]
out_scale = qrec.cache["state_out_q"].qbiases[0]
out_scalen = qrec.cache["state_out_q"].qnorms[0]
cin_scale = qrec.cache["cell_in_q"].qbiases[0]
cin_scalen = qrec.cache["cell_in_q"].qnorms[0]
cout_scale = qrec.cache["cell_out_q"].qbiases[0]
cout_scalen = qrec.cache["cell_out_q"].qnorms[0]
out_zeropoint = out_q.zero_point[0]
# define LSTM_NE16_W_ZEROPOINT 0
# define LSTM_NE16_GATE_PRENORM 1
# define LSTM_NE16_CIN_SCALE (0 + LSTM_NE16_OUT_OFF)
# define LSTM_NE16_CIN_SCALEN (1 + LSTM_NE16_OUT_OFF)
# define LSTM_NE16_COUT_SCALE (2 + LSTM_NE16_OUT_OFF)
# define LSTM_NE16_COUT_SCALEN (3 + LSTM_NE16_OUT_OFF)
# define LSTM_NE16_OUT_SCALE (4 + LSTM_NE16_OUT_OFF)
# define LSTM_NE16_OUT_SCALEN (5 + LSTM_NE16_OUT_OFF)
# define LSTM_NE16_OUT_ZEROPOINT (6 + LSTM_NE16_OUT_OFF)
# define LSTM_NE16_INT_A0 (0 + LSTM_NE16_INT_OFF)
# define LSTM_NE16_INT_B0 (1 + LSTM_NE16_INT_OFF)
# define LSTM_NE16_INT_C0 (2 + LSTM_NE16_INT_OFF)
sigmoid_table = interleave(SIGMOID_TABLE & 0xff,
SIGMOID_TABLE >> 8).astype(np.int8)
if out_q.dtype == np.uint8:
# Maybe get rid of this
if qrec.cache.get('act_qtype'):
min_val = qrec.cache['act_qtype'].quantize(-1)
max_val = qrec.cache['act_qtype'].quantize(1)
else:
min_val = max_val = 0
contents = np.concatenate(
(sigmoid_table,
np.array([
-weight_zero.astype(np.int8), qrec.cache['gate_prenorm'],
cin_scale.astype(np.int8),
cin_scalen.astype(np.int8),
cout_scale.astype(np.int8),
cout_scalen.astype(np.int8),
out_scale.astype(np.int8),
out_scalen.astype(np.int8),
out_zeropoint.astype(np.int8), 0, 0, 0, 0
],
dtype=np.int8)))
else:
contents = np.concatenate(
(sigmoid_table,
np.array([
-weight_zero.astype(np.int8),
qrec.cache['gate_prenorm'],
cin_scale.astype(np.int8),
cin_scalen.astype(np.int8),
cout_scale.astype(np.int8),
cout_scalen.astype(np.int8),
out_scale.astype(np.int8),
out_scalen.astype(np.int8),
out_zeropoint.astype(np.uint16) & 0xff,
out_zeropoint.astype(np.uint16) >> 8,
],
dtype=np.int8)))
comment = (
f"WZP: {weight_zero}, Out: {out_scale}/{out_scalen}, Cin: {cin_scale}/{cin_scalen}"
f"Cout: {cout_scale}/{cout_scalen}, OZP: {out_zeropoint}")
cname, file_name = gen_constant(gen, pnode, pnode, INFOS)
const_info = ConstantInfo(file_name,
QType.Pow2(bits=8, q=0, signed=True),
contents=contents)
gen.globals.append(
GlobalArgInfo("int8",
cname,
gen.opts['default_global_home_location'],
gen.opts['default_global_exec_location'],
const_info=const_info,
comment=comment))
if node.rnn_states_as_inputs:
gen.globals.append(
GlobalResetArgInfo(f"{node.name}_Reset", 'AT_MEM_L2',
'AT_MEM_UNDEF'))
return True
