def matscale3(cls, in_tensors, qrec):
assert qrec.in_qs[0].bits == qrec.in_qs[1].bits
assert qrec.in_qs[1].bits == qrec.in_qs[2].bits
if qrec.in_qs[0].bits == 8:
q_calc = QType.Pow2(bits=32,
q=qrec.in_qs[0].q + qrec.in_qs[1].q +
qrec.in_qs[2].q,
signed=True)
res = np.multiply(np.multiply(in_tensors[0],
in_tensors[1],
dtype=np.int32),
in_tensors[2],
dtype=np.int32)
res = qrec.out_qs[0].reduce_from(res, q_calc)
elif qrec.in_qs[0].bits == 16:
q_calc = QType.Pow2(bits=32,
q=qrec.in_qs[0].q + qrec.in_qs[1].q,
signed=True)
res = np.multiply(in_tensors[0], in_tensors[1], dtype=np.int32)
res = qrec.out_qs[0].reduce_from(res, q_calc)
q_calc = QType.Pow2(bits=32,
q=qrec.in_qs[2].q + qrec.out_qs[0].q,
signed=True)
res = np.multiply(res, in_tensors[2], dtype=np.int32)
res = qrec.out_qs[0].reduce_from(res, q_calc)
else:
raise ValueError("only 8 and 16 bits supported")
return res
def execute(cls, params,
in_tensors,
qrec: QRec,
**kwargs):
in_tensors = qrec.prepare_inputs(params, in_tensors, ktype="symmetric")
func = PIECEWISE_OPS[params.__class__]
op = func['op']
if func['is_mult']:
i1 = in_tensors[0].astype(np.int32)
i2 = in_tensors[1].astype(np.int32)
res = op(i1, i2, np.int32)
q_calc = QType.Pow2(
bits=32, q=qrec.in_qs[0].q+qrec.in_qs[1].q, signed=True)
res = qrec.out_qs[0].reduce_from(res, q_calc)
else:
off_in = abs(qrec.in_qs[0].q - qrec.in_qs[1].q)
if qrec.in_qs[0].q > qrec.in_qs[1].q:
i1 = at_norm(in_tensors[0].astype(np.int32), off_in)
i2 = in_tensors[1].astype(np.int32)
else:
i1 = in_tensors[0].astype(np.int32)
i2 = at_norm(in_tensors[1].astype(np.int32), off_in)
res = op(i1, i2, None)
q_calc = QType.Pow2(bits=32, q=min(qrec.in_qs[0].q, qrec.in_qs[1].q), signed=True)
res = qrec.out_qs[0].reduce_from(res, q_calc)
return qrec.get_outputs(params, [res], ktype="symmetric")
def get_outputs(self,
params: Parameters,
output_tensors: Sequence[np.ndarray],
ktype: str = None) -> Sequence[np.ndarray]:
if ktype == "symmetric":
if isinstance(params, (MatrixAddParameters, MatrixSubParameters)):
q_calc = QType.Pow2(bits=32,
q=min(self.in_qs[0].q, self.in_qs[1].q),
signed=True)
output_tensors = [
self.out_qs[0].reduce_from(output_tensors[0], q_calc)
]
elif isinstance(params,
(MatrixMulParameters, MatrixDivParameters)):
q_calc = QType.Pow2(bits=32,
q=self.in_qs[0].q + self.in_qs[1].q,
signed=True)
output_tensors = [
self.out_qs[0].reduce_from(output_tensors[0], q_calc)
]
elif isinstance(
params,
GlobalPoolParameters) and params.pool_type == "sum":
output_tensors = [
self.out_qs[0].reduce_from(output_tensors[0],
self.in_qs[0])
]
if self._auto_dequantize_outputs:
return [
self.out_qs[idx].dequantize(output_tensor)
for idx, output_tensor in enumerate(output_tensors)
]
return output_tensors
def _quantize(cls, params, in_qs, stats, **kwargs):
force_out_qs, _ = cls.get_pow2_opts(**kwargs)
force_out_q = force_out_qs and force_out_qs[0]
out_q = QType.Pow2(16, 15, True)
if force_out_q and force_out_q != out_q:
return None
return SymmetricQuantizationRecord(in_qs=in_qs, out_qs=[QType.Pow2(16, 15, True)])
def gen_ssd_globals(gen, node, qrec):
qrec.set_scales(node)
scores_q = qrec.in_qs[1]
scores_scale, scores_norm = compute_mul_bias(scores_q.scale)
cname_scales, file_name_scales = gen_constant(gen, node, node, SSD_SCALES)
contents = np.array([qrec.scale_x_q.qbiases,
qrec.scale_x_anc_q.qbiases,
qrec.scale_y_q.qbiases,
qrec.scale_y_anc_q.qbiases,
qrec.scale_h_q.qbiases,
qrec.scale_w_q.qbiases,
qrec.scale_ao_q.qbiases,
scores_scale], dtype=np.int8)
scale_info = ConstantInfo(file_name_scales, QType.Pow2(bits=8, q=0, signed=True), contents=contents)
cname_norms, file_name_norms = gen_constant(gen, node, node, SSD_NORMS)
contents = np.array([qrec.scale_x_q.qnorms,
qrec.scale_x_anc_q.qnorms,
qrec.scale_y_q.qnorms,
qrec.scale_y_anc_q.qnorms,
qrec.scale_h_q.qnorms,
qrec.scale_w_q.qnorms,
qrec.scale_ao_q.qnorms,
scores_norm], dtype=np.int8)
norms_info = ConstantInfo(file_name_norms, QType.Pow2(bits=8, q=0, signed=True), contents=contents)
score_threshold = scores_q.quantize(node.nms_score_threshold)
cname_infos, file_name_infos = gen_constant(gen, node, node, INFOS)
contents = np.array([round(node.nms_iou_threshold * 2**7), # Q7
score_threshold, # Q0 [0:255]
node.max_detections, # Q0 [0:255]
node.max_classes_per_detection, # Q0 [0:255]
node.max_bb_before_nms >> 8,
node.max_bb_before_nms], dtype=np.int8) # max_bb = Infos[4]<<8 + Infos[5]
ssd_infos = ConstantInfo(file_name_infos, QType.Pow2(bits=8, q=0, signed=True), contents=contents)
gen.globals.append(GlobalArgInfo(qrec.scale_x_q.ctype, cname_scales,
gen.opts['default_global_home_location'],
gen.opts['default_global_exec_location'],
const_info=scale_info))
gen.globals.append(GlobalArgInfo(qrec.scale_x_q.shift_ctype, cname_norms,
gen.opts['default_global_home_location'],
gen.opts['default_global_exec_location'],
const_info=norms_info))
gen.globals.append(GlobalArgInfo('uint8', cname_infos,
gen.opts['default_global_home_location'],
gen.opts['default_global_exec_location'],
const_info=ssd_infos))
def _quantize(cls, params, in_qs, stats, **kwargs):
force_out_qs, params_dtype = cls.get_pow2_opts(**kwargs)
force_out_q = force_out_qs and force_out_qs[0]
fusion = kwargs.get('fusion', None)
cls.check_valid_ranges(params, stats, idx=0, dirs='out')
if fusion:
activation = fusion.contained_nodes()[1]
if isinstance(activation, ReluActivationParameters):
# Take stats from activation after the convolution
range_out = kwargs['all_stats'][NodeId(
fusion, activation)]['range_out'][0]
out_dtype = np.int32
else:
out_dtype = params_dtype
range_out = stats['range_out'][0]
in_q1 = deepcopy(in_qs[0]).scale_to_pow2()
in_q2 = deepcopy(in_qs[0]).scale_to_pow2()
biases_q = QType.Pow2(32, in_q1.q + in_q2.q, True)
if force_out_q:
o_q = force_out_q
else:
o_q = QType.from_min_max_pow2(range_out['min'],
range_out['max'],
dtype=out_dtype)
if len(in_qs) == 3:
return QRec.symmetric(in_qs=[in_q1, in_q2, biases_q], out_qs=[o_q])
return QRec.symmetric(in_qs=[in_q1, in_q2], out_qs=[o_q])
def rnn_infos(gen, node, qrec):
i_state_q = qrec.in_qs[node.INPUT_NAMES.index('i_state')]
contents = []
comments = []
# info for activation (scale the act input to the proper scale)
info, comment = INFOS_FUNCS[node.activation]("f", qrec.s_2_s_q, i_state_q)
contents.append(info)
comments.append(comment)
# info for input scaling (only used with non SameInputStateScale kernels)
info, comment = scale_infos("f", getattr(qrec, "i_2_a_q"))
contents.append(info)
comments.append(comment)
# info for scaling the activation out to out scale (only used for non Hard activations kernels)
info, comment = scale_infos("f", getattr(qrec, "s_2_o_q"))
contents.append(info)
comments.append(comment)
cname, file_name = gen_constant(gen, node, node, INFOS)
const_info = ConstantInfo(file_name,
QType.Pow2(bits=8, q=0, signed=True),
contents=np.hstack(tuple(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 execute(cls, params, in_tensors, qrec: QRec, **kwargs):
in_tensors = [
in_tensor.astype(np.int32) for in_tensor in qrec.prepare_inputs(
params, in_tensors, ktype="symmetric")
]
if isinstance(params, MatMulTransposedParameters):
mat1, mat2 = in_tensors[0], np.transpose(in_tensors[1], (1, 0))
else:
mat1, mat2 = in_tensors[0], in_tensors[1]
if len(in_tensors) > 2:
biases = in_tensors[2]
if len(biases.shape) == 1:
if biases.shape[0] == mat1.shape[0]:
biases = np.expand_dims(biases, -1)
else:
biases = 0
# expect biases in in_q1 + in_q2
q_calc = QType.Pow2(bits=32,
q=qrec.in_qs[0].q + qrec.in_qs[1].q,
signed=True)
out_tensor = np.matmul(mat1, mat2) + biases
out_tensor = qrec.out_qs[0].reduce_from(out_tensor, q_calc)
return qrec.get_outputs(params, [out_tensor], ktype="symmetric")
def gru_infos(gen, node, qrec):
i_qtype = internal_qtype(qrec)
contents = []
comments = []
r_to_int_scale = qrec.cache['r_WR_2_int_q'].qbiases[0]
r_to_int_scalen = qrec.cache['r_WR_2_int_q'].qnorms[0]
r_to_in_scale = qrec.cache['i_2_r_WR_q'].qbiases[0]
r_to_in_scalen = qrec.cache['i_2_r_WR_q'].qnorms[0]
z_to_int_scale = qrec.cache['z_WR_2_int_q'].qbiases[0]
z_to_int_scalen = qrec.cache['z_WR_2_int_q'].qnorms[0]
z_to_in_scale = qrec.cache['i_2_z_WR_q'].qbiases[0]
z_to_in_scalen = qrec.cache['i_2_z_WR_q'].qnorms[0]
ht_to_in_scale = qrec.cache['i_2_h_WR_q'].qbiases[0]
ht_to_in_scalen = qrec.cache['i_2_h_WR_q'].qnorms[0]
h_to_int_scale = qrec.cache['h_WR_2_int_q'].qbiases[0]
h_to_int_scalen = qrec.cache['h_WR_2_int_q'].qnorms[0]
# GRU_R_INFOS
comments.append(str.format("r_to_int_scale: {} r_to_int_scalen: {} r_to_in_scale: {} r_to_in_scalen: {}",
r_to_int_scale, r_to_int_scalen, r_to_in_scale, r_to_in_scalen,))
contents.append(np.array(
[r_to_int_scale, r_to_int_scalen, r_to_in_scale, r_to_in_scalen], dtype=np.int8))
# GRU_Z_INFOS
comments.append(str.format("z_to_int_scale: {} z_to_int_scalen: {} z_to_in_scale: {} z_to_in_scalen: {}",
z_to_int_scale, z_to_int_scalen, z_to_in_scale, z_to_in_scalen,))
contents.append(np.array(
[z_to_int_scale, z_to_int_scalen, z_to_in_scale, z_to_in_scalen], dtype=np.int8))
# GRU_HT_INFOS
comments.append(str.format("ht_to_in_scale: {} ht_to_in_scalen: {}",
ht_to_in_scale, ht_to_in_scalen,))
contents.append(np.array([ht_to_in_scale, ht_to_in_scalen], dtype=np.int8))
# GRU_H_INFOS
comments.append(str.format("h_to_int_scale: {} h_to_int_scalen: {}",
h_to_int_scale, h_to_int_scalen,))
contents.append(np.array([h_to_int_scale, h_to_int_scalen], dtype=np.int8))
three = i_qtype.quantize(np.array([3]))[0]
six = i_qtype.quantize(np.array([6]))[0]
sixth = i_qtype.quantize(np.array([1/6]))[0]
comments.append(str.format("int_q: {} A0: {} B0: {} C0: {}",
i_qtype.q, six, three, sixth))
contents.append(np.array([lowb(six), highb(six),
lowb(three), highb(three),
lowb(sixth), highb(sixth), i_qtype.q],
dtype=np.int8))
cname, file_name = gen_constant(gen, node, node, INFOS)
const_info = ConstantInfo(file_name, QType.Pow2(bits=8, q=0, signed=True),
contents=np.hstack(tuple(contents)))
gen.globals.append(GlobalArgInfo("int8", cname,
gen.opts['default_global_home_location'],
gen.opts['default_global_exec_location'],
const_info=const_info,
comment=" ".join(comments)))
def matscale2(cls, in_tensors, qrec=None):
assert qrec.in_qs[0].bits == qrec.in_qs[1].bits
q_calc = QType.Pow2(bits=32,
q=qrec.in_qs[0].q + qrec.in_qs[1].q,
signed=True)
res = np.multiply(in_tensors[0], in_tensors[1], dtype=np.int32)
res = qrec.out_qs[0].reduce_from(res, q_calc)
return res
def _quantize(cls, params, in_qs, stats, **kwargs):
force_out_qs, _ = cls.get_pow2_opts(**kwargs)
force_out_q = force_out_qs and force_out_qs[0]
in_q = deepcopy(in_qs[0]).scale_to_pow2()
in_q.set_forced()
out_q = QType.Pow2(16, 15, True, forced=True)
if force_out_q and force_out_q != out_q:
return None
return QRec.symmetric(in_qs=[in_q], out_qs=[out_q])
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 execute(cls, params, in_tensors, qrec: QRec, **kwargs):
in_tensor = qrec.prepare_inputs(params, in_tensors,
ktype="symmetric")[0]
out_q15 = tanh_lut(in_tensor.astype(np.int32) << 8)
compute_in_out_scale(
qrec,
extra_scale=QType.Pow2(bits=32, q=7, signed=True).scale /
qrec.in_qs[0].scale)
scale_mul_biases_q = qrec.cache['scale_mul_biases_q']
output = scale_mul_biases_q.apply_scales(out_q15 >> 8)
return qrec.get_outputs(params, [output], ktype="symmetric")
def compute_scales(cls, params, qrec):
if isinstance(params,
(SigmoidScaledSymmetricMult, TanHActivationParameters)):
compute_in_out_scale(
qrec,
extra_scale=QType.Pow2(bits=32, q=7, signed=True).scale /
qrec.in_qs[0].scale)
elif isinstance(params, HSwishActivationParameters):
compute_in_out_scale(qrec, extra_scale=qrec.in_qs[0].scale * 1 / 6)
else:
compute_in_out_scale(qrec)
return qrec
def _quantize(cls, params, in_qs, stats, **kwargs):
force_out_qs, _ = cls.get_mult_opts(**kwargs)
force_out_q = force_out_qs and force_out_qs[0]
if force_out_q:
return None
in_q, win_q, fft_twiddles_q, swap_table_q, rfft_twiddles_q, fft_out_q, spect_q = cls.get_spectrogram_in_out_q(
in_qs[0], params)
melcoeff_q = QType.Pow2(bits=16, signed=True, q=MFCC_COEFF_Q)
mel_sparsity_table_q = QType.Pow2(bits=16, signed=False, q=0)
dctmat_q = QType.Pow2(bits=16, signed=True, q=DCT_TWIDDLE_Q)
if params.mel_type == "melspectrogram":
out_q = QType.Pow2(bits=32, signed=True, q=16)
elif params.mel_type == "logmelspectrogram":
out_q = QType.Pow2(bits=16, signed=True, q=15 - params.quant_norm)
else:
out_q = QType.Pow2(bits=16,
signed=True,
q=15 - params.quant_norm - DCT_TWIDDLE_Q)
return QRec.symmetric(in_qs=[
in_q, win_q, fft_twiddles_q, swap_table_q, rfft_twiddles_q,
mel_sparsity_table_q, melcoeff_q, dctmat_q
],
out_qs=[out_q],
fft_out_q=fft_out_q)
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 execute(cls, params,
in_tensors,
qrec: QuantizationRecordBase,
**kwargs):
in_tensor = qrec.prepare_inputs(params, in_tensors, ktype="symmetric")[0]
calc_q = QType.Pow2(bits=32, q=qrec.in_qs[0].q + 15, signed=True)
fac_1 = qrec.in_qs[0].quantize(np.array([params.offset]))
fac_2 = (1 << 15) // 6
upper_bound = qrec.in_qs[0].quantize(np.array([6.]))
lower_bound = qrec.in_qs[0].quantize(np.array([0.]))
in_tensor = in_tensor.astype(np.int32)
in_tensor = np.multiply(np.minimum(np.maximum(in_tensor + fac_1, lower_bound),
upper_bound), fac_2, dtype=np.int32)
return qrec.get_outputs(params, [qrec.out_qs[0].reduce_from(in_tensor, calc_q)], ktype="symmetric")
def lstm_infos(gen, node, qrec):
i_qtype = internal_qtype(qrec)
contents = []
comments = []
for k, v in LSTM_INFOS_ORDER.items():
info, comment = scale_infos(k, qrec.cache["r_2_%s_q" % k])
contents.append(info)
comments.append(comment)
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_scale = qrec.cache['state_out_q'].qbiases[0]
out_scalen = qrec.cache['state_out_q'].qnorms[0]
comments.append(str.format("cin_scale: {} cin_scale_n: {} cout_scale: {} cout_scale_n: {}",
cin_scale, cin_scalen, cout_scale, cout_scalen,))
comments.append(str.format("out_scale: {} out_scale_n: {}",
out_scale, out_scalen))
contents.append(np.array([cin_scale, cin_scalen, cout_scale, cout_scalen,
out_scale, out_scalen], dtype=np.int8))
three = i_qtype.quantize(np.array([3]))[0]
six = i_qtype.quantize(np.array([6]))[0]
sixth = i_qtype.quantize(np.array([1/6]))[0]
comments.append(str.format("int_q: {} A0: {} B0: {} C0: {}",
i_qtype.q, six, three, sixth))
contents.append(np.array([lowb(six), highb(six),
lowb(three), highb(three),
lowb(sixth), highb(sixth), i_qtype.q],
dtype=np.int8))
for k in LSTM_INFOS_ORDER.keys():
info, comment = scale_infos(k, qrec.cache["i_2_%s_q" % k])
contents.append(info)
comments.append(comment)
cname, file_name = gen_constant(gen, node, node, INFOS)
const_info = ConstantInfo(file_name, QType.Pow2(bits=8, q=0, signed=True),
contents=np.hstack(tuple(contents)))
gen.globals.append(GlobalArgInfo("int8", cname,
gen.opts['default_global_home_location'],
gen.opts['default_global_exec_location'],
const_info=const_info,
comment=" ".join(comments)))
def _quantize(cls, params, in_qs, stats, **kwargs):
force_out_qs, out_dtype = cls.get_mult_opts(**kwargs)
force_out_q = force_out_qs and force_out_qs[0]
fusion = kwargs.get('fusion', None)
in_q = in_qs[0]
if not fusion and in_q.dtype == np.int32:
return None
if isinstance(params, (HSwishActivationParameters, HSigmoidActivationParameters)):
max_val = in_q.scale * pow(2, in_q.bits - 1)
if max_val < 6:
in_q = QType.from_min_max_sq(-6, 6, dtype=in_q.dtype, forced=True)
elif isinstance(params, SigmoidActivationParameters):
in_q = QType.from_min_max_sq(-8, 8, dtype=in_q.dtype, forced=True)
if force_out_q:
if force_out_q.signed != in_q.signed:
return None
if fusion and fusion.fusion_type in ['conv_active_pool', 'conv_active']:
if not isinstance(params, (SigmoidActivationParameters, HTanHActivationParameters,
HSwishActivationParameters, HSigmoidActivationParameters)):
in_q = deepcopy(force_out_q)
o_q = deepcopy(force_out_q)
# activation cannot move zeropoint unless it is a reduction step
if o_q.zero_point != in_q.zero_point and in_q.dtype != np.int32:
return None
else:
cls.check_valid_ranges(params, stats, idx=0, dirs='out')
zero_point = in_q.zero_point if in_q.zero_point != 0 else None
o_q = QType.from_min_max_sq(stats['range_out'][0]['min'],
stats['range_out'][0]['max'],
dtype=in_q.dtype,
zero_point=zero_point)
qrec = QRec.scaled(in_qs=[in_q], out_qs=[o_q])
if isinstance(params, (SigmoidScaledSymmetricMult, TanHActivationParameters)):
compute_in_out_scale(qrec, extra_scale=QType.Pow2(bits=32, q=7, signed=True).scale/qrec.in_qs[0].scale)
elif isinstance(params, HSwishActivationParameters):
compute_in_out_scale(qrec, extra_scale=qrec.in_qs[0].scale * 1/6)
else:
compute_in_out_scale(qrec)
return qrec
def execute(cls, params,
in_tensors,
qrec: QRec,
**kwargs):
in_tensors = [in_tensor.astype(np.int32) for in_tensor in qrec.prepare_inputs(
params, in_tensors, ktype="symmetric")]
if len(in_tensors) > 2:
biases = in_tensors[2]
if len(biases.shape) == 1:
biases = np.expand_dims(biases, -1)
else:
biases = 0
# expect biases in in_q1 + in_q2
q_calc = QType.Pow2(bits=32, q=qrec.in_qs[0].q + qrec.in_qs[1].q, signed=True)
out_tensor = np.matmul(in_tensors[0], in_tensors[1]) + biases
out_tensor = qrec.out_qs[0].reduce_from(out_tensor, q_calc)
return qrec.get_outputs(params, [out_tensor], ktype="symmetric")
def get_spectrogram_in_out_q(cls, in_q, params):
win_q = QType.Pow2(bits=16, signed=True, q=WINDOW_Q)
fft_twiddles_q = QType.Pow2(bits=16, signed=True, q=FFT_TWIDDLES_Q)
rfft_twiddles_q = QType.Pow2(bits=16, signed=True, q=FFT_TWIDDLES_Q)
swap_table_q = QType.Pow2(bits=16, signed=False, q=0)
in_q = QType.Pow2(bits=16,
signed=True,
q=int(-np.ceil(np.log2(in_q.scale))),
forced=True)
if params.is_radix4():
#in_q = QType.Pow2(bits=16, signed=True, q=12)
fft_out_q = in_q.q - 2 * (int(np.log2(params.n_cfft) / 2) - 2) - 1
else:
#in_q = QType.Pow2(bits=16, signed=True, q=13)
fft_out_q = in_q.q - (int(np.log2(params.n_cfft)) - 3) - 1
fft_out_q = QType.Pow2(bits=16, signed=True, q=fft_out_q)
if not params.magsquared:
out_q = QType.Pow2(bits=32, signed=False, q=15)
else:
out_q = QType.Pow2(bits=32, signed=False, q=15) #fft_out_q.q*2)
return in_q, win_q, fft_twiddles_q, swap_table_q, rfft_twiddles_q, fft_out_q, out_q
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:
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 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 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 step_kernel(cls, params: LSTMParameters, args: Mapping[str,
np.ndarray],
idx: int, input_tensor: np.ndarray, qrec):
use_cifg = 'i_2_i_w' in args and args['i_2_i_w'][0] is None
use_peephole = 'c_2_o_w' in args and args['c_2_o_w'][0] is not None
use_layer_norm = 'f_norm' in args and args['f_norm'][0] is not None
if use_cifg:
raise NotImplementedError("cifg mode is not supported")
if use_peephole:
raise NotImplementedError("peephole mode is not supported")
if use_layer_norm:
raise NotImplementedError("layer norm mode is not supported")
# INPUT vs WEIGHTS
# For each cell: compute input_weight * input if there is an input
input_gate_scratch = 0
forget_gate_scratch = 0
cell_scratch = 0
output_gate_scratch = 0
if idx < params.n_input_cells:
input_gate_scratch += scale_lstm_input_input(
qrec, args['i_2_i_w'][0].astype(np.int32).dot(
input_tensor[idx].astype(np.int32)), 0)
forget_gate_scratch += scale_lstm_input_forget(
qrec, args['i_2_f_w'][0].astype(np.int32).dot(
input_tensor[idx].astype(np.int32)), 0)
cell_scratch += scale_lstm_input_cell(
qrec, args['i_2_c_w'][0].astype(np.int32).dot(
input_tensor[idx].astype(np.int32)), 0)
output_gate_scratch += scale_lstm_input_output(
qrec, args['i_2_o_w'][0].astype(np.int32).dot(
input_tensor[idx].astype(np.int32)), 0)
# Initialize scratch buffers with bias for regular lstm
input_gate_scratch_state = args['i_b'][0].astype(np.int32)
forget_gate_scratch_state = args['f_b'][0].astype(np.int32)
cell_scratch_state = args['c_b'][0].astype(np.int32)
output_gate_scratch_state = args['o_b'][0].astype(np.int32)
# STATE vs WEIGHTS INITIALIZED WITH BIASES
# For each cell: compute recurrent_weight * output_state
input_gate_scratch_state += args['r_2_i_w'][0].astype(np.int32).dot(
args['i_state'][0].astype(np.int32))
forget_gate_scratch_state += args['r_2_f_w'][0].astype(np.int32).dot(
args['i_state'][0].astype(np.int32))
cell_scratch_state += args['r_2_c_w'][0].astype(np.int32).dot(
args['i_state'][0].astype(np.int32))
output_gate_scratch_state += args['r_2_o_w'][0].astype(np.int32).dot(
args['i_state'][0].astype(np.int32))
input_gate_scratch = scale_lstm_internal_input(
qrec, input_gate_scratch_state + input_gate_scratch, 0)
forget_gate_scratch = scale_lstm_internal_forget(
qrec, forget_gate_scratch_state + forget_gate_scratch, 0)
cell_scratch = scale_lstm_internal_cell(
qrec, cell_scratch_state + cell_scratch, 0)
output_gate_scratch = scale_lstm_internal_output(
qrec, output_gate_scratch_state + output_gate_scratch, 0)
# Apply activations in internal Q * 1
input_gate_scratch = get_activation('sigmoid', params.hard_act)(
input_gate_scratch, internal_qtype(qrec))
forget_gate_scratch = get_activation('sigmoid', params.hard_act)(
forget_gate_scratch, internal_qtype(qrec))
output_gate_scratch = get_activation('sigmoid', params.hard_act)(
output_gate_scratch, internal_qtype(qrec))
cell_scratch = get_activation('tanh',
params.hard_act)(cell_scratch,
internal_qtype(qrec))
# cstate = cstate * Of + Og * Oi
if params.hard_act:
# Scale cell state to internal Q * 1
cstate = scale_lstm_cellin(qrec,
args['c_state'][0].astype(np.int32), 0)
cstate = cstate * forget_gate_scratch + cell_scratch * input_gate_scratch
# cstate now in (2 * Q) * 1
else:
# Multiply cstate [Scstate] * Of [Sq15] and scale to [Sq12]
# Multiply Og [Sq15] * Oi [Sq15] --> [Sq30] >> 30-12 --> [Sq12]
# cstate is now in q12 = internal_qtype
cstate = scale_lstm_cellin(qrec, args['c_state'][0] * forget_gate_scratch, 0) \
+ ((cell_scratch * input_gate_scratch)
>> (15+(15-internal_qtype(qrec).q)))
# if params.cell_clip > 0.0:
# args['c_state'] = abs_clip(args['c_state'], params.cell_clip)
# if there is a clip value this should override the min max here
# clip here
args['c_state'][0] = scale_lstm_cellout(qrec, cstate, 0)
if params.hard_act:
two_qtype = QType.Pow2(
internal_qtype(qrec).bits,
internal_qtype(qrec).q * 2, True)
cell_scratch = get_activation('tanh', params.hard_act)(cstate,
two_qtype)
# Assume scaling from internalq * 3 -> Q7 * 1
output_gate_scratch *= cell_scratch
else:
cell_scratch = get_activation('tanh', params.hard_act)(
cstate, internal_qtype(qrec))
# output = Og[Sq15] * tanh(cell_scratch)[Sq15] -> [Sq30] >> 15 -> [Sq15]
output_gate_scratch = (output_gate_scratch * cell_scratch) >> 15
output = scale_lstm_output(qrec, output_gate_scratch, 0)
output = qrec.out_qs[0].clip(output)
use_projection_weight = 'proj_w' in args and args['proj_w'][
0] is not None
use_projection_bias = 'proj_b' in args and args['proj_b'][0] is not None
if use_projection_weight or use_projection_bias:
raise NotImplementedError("LSTMP is not yet supported by kernel")
#args['i_state'][0] = qrec.scale_i_state(output_gate_scratch.copy(), 0, ktype="symmetric")
args['i_state'][0] = output.copy()
if params.lstm_output_c_state:
return output, args['c_state'][0]
return output, None
def get_qtype(qparam1, qparam2):
try:
bits_idx = STATS_BITS.index(qparam1 + qparam2)
except ValueError:
raise TuneError("bit width is not valid")
return QType.Pow2(STATS_BITS[bits_idx], qparam2, True)
def gen_act_infos(act_params, act_q):
comment = ""
if 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")
return contents, comment
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 act_infos(gen,
pnode,
fnode,
act_params,
act_q,
extra1=0,
extra2=0,
extra3=0,
extra4=0,
extra_name=''):
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, extra1, extra2, extra3, extra4],
dtype=np.int8)
elif isinstance(act_params, ReluActivationParameters):
actscale = act_q.scale_mul_biases_q.qbiases[0]
actscalen = act_q.scale_mul_biases_q.qnorms[0]
if act_params.upper_bound is None: # or fnode is not None:
contents = np.array(
[actscale, actscalen, 0, 0, 0, extra1, extra2, extra3, extra4],
dtype=np.int8)
if len(comment) == 0:
comment = "all 0"
else:
fac_1 = act_q.in_qs[0].quantize(act_params.upper_bound)
contents = np.array([
actscale, actscalen, fac_1, 0, 0, extra1, extra2, extra3,
extra4
],
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])
elif isinstance(act_params, HSigmoidActivationParameters):
# currently combines all scaling factors into one scale and shift
fac_1, upper_bound, _ = hsigmoid_mult_gen_factors(act_params, act_q)
contents = np.array([
act_q.scale_mul_biases_q.qbiases[0],
act_q.scale_mul_biases_q.qnorms[0], upper_bound, fac_1, 1, extra1,
extra2, extra3, extra4
],
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.scale_mul_biases_q.qbiases[0],
act_q.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
fac_1, upper_bound, _ = hswish_mult_gen_factors(act_q)
contents = np.array([
act_q.scale_mul_biases_q.qbiases[0],
act_q.scale_mul_biases_q.qnorms[0], upper_bound, fac_1, 1, extra1,
extra2, extra3, extra4
],
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.scale_mul_biases_q.qbiases[0],
act_q.scale_mul_biases_q.qnorms[0], upper_bound[0], fac_1[0])
elif isinstance(act_params, SoftMaxParameters):
norm = 15 + np.ceil(np.log2(act_q.in_qs[0].scale))
contents = np.array([norm, 0, 0, 0, 0, extra1, extra2, extra3, extra4],
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):
act_q.set_scale()
leak_factor_quant = leak_mult_gen_factor_q7(act_params)
contents = np.array([
act_q.scale_mul_biases_q.qbiases[0],
act_q.scale_mul_biases_q.qnorms[0], leak_factor_quant, 0, 0,
extra1, extra2, extra3, extra4
],
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.scale_mul_biases_q.qbiases[0],
act_q.scale_mul_biases_q.qnorms[0], leak_factor_quant)
else:
raise NotImplementedError("activation tye not implemented")
if isinstance(pnode, (GlobalPoolParameters, PoolingParameters)):
contents = np.array([
act_q.scale_mul_biases_q.qbiases[0],
act_q.scale_mul_biases_q.qnorms[0], 0, 0, 0, extra1, extra2,
extra3, extra4
],
dtype=np.int8)
comment += str.format("in: {:05f} out: {:05f}",
act_q.in_qs[0].scale[0],
act_q.out_qs[0].scale[0])
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))
