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 _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]
opts = kwargs['opts']
if force_out_q:
if force_out_q.forced_scale or force_out_q.forced_zero_point:
return None
if in_qs[0].dtype == np.int8:
dtypes = [np.int8, np.int16]
else:
dtypes = [np.int16]
if force_out_q.forced_dtype and force_out_q.dtype not in dtypes:
return None
in_qs = cls.force_symmetric_and_dtype(in_qs)
if in_qs is None:
return None
# force the input to be POW2 scaled
pow2_scale = np.power(2, np.ceil(np.log2(in_qs[0].scale)))
in_q = QType(min_val=in_qs[0].min_val,
max_val=in_qs[0].max_val,
dtype=in_qs[0].dtype,
scale=pow2_scale,
forced=True)
if in_q.dtype == np.int8 and (opts.get('softmax_out_8bits', None) or
(force_out_q
and force_out_q.dtype == np.int8)):
# params.at_options.softmax_out_8bits = 1
o_q = QType(min_val=-1, max_val=1, dtype=np.int8, scale=2**(-7))
else:
o_q = QType(min_val=-1, max_val=1, dtype=np.int16, scale=2**(-15))
if in_q.dtype == np.int16 and o_q.dtype == np.int16:
return QRec.symmetric(in_qs=[in_q], out_qs=[o_q])
return QRec.scaled(in_qs=[in_q], out_qs=[o_q])
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 _quantize(cls, params, in_qs, stats, **kwargs):
force_out_qs, out_dtype = cls.get_pow2_opts(**kwargs)
force_out_q = force_out_qs and force_out_qs[0]
fusion = kwargs.get('fusion', None)
if not fusion and in_qs[0].dtype == np.int32:
return None
if params.activation == "relu6":
int_bits = calc_bits(6)
elif params.activation == "relun":
relun = params.activation_params
if isinstance(relun, list):
relun = max(relun)
int_bits = calc_bits(relun)
elif params.activation in [
"relu", "hswish", "hsigmoid", "leaky", "htanh"
]:
cls.check_valid_ranges(params, stats, idx=0, dirs='out')
int_bits = calc_bits(stats['range_out'][0]['max'],
stats['range_out'][0]['min'])
elif params.activation == "sigmoid" or params.activation == "tanh":
if force_out_q is None:
q = 7 if out_dtype == np.int8 else 15
return QRec.symmetric(in_qs=[in_qs[0]],
out_qs=[QType(q=q, dtype=out_dtype)])
else:
q = 7 if force_out_q.dtype == np.int8 else 15
if force_out_q.q != q:
return None
return QRec.symmetric(in_qs=[in_qs[0]], out_qs=[force_out_q])
else:
LOG.error(
f'no support for activation {params.activation} in POW2 quantizer'
)
return None
in_q = in_qs[0]
if force_out_q is None:
q = max(cls.get_pow2_bits(**kwargs) - int_bits, 0)
out_q = QType(q=q, dtype=out_dtype)
else:
if force_out_q.bits - force_out_q.q < int_bits:
return None
out_q = force_out_q
return QRec.symmetric(in_qs=[in_q], out_qs=[out_q])
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 _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, out_q = cls.get_spectrogram_in_out_q(
in_qs[0], params)
return QRec.symmetric(
in_qs=[in_q, win_q, fft_twiddles_q, swap_table_q, rfft_twiddles_q],
out_qs=[out_q],
fft_out_q=fft_out_q)
def _quantize(cls, params, in_qs, stats, **kwargs):
force_out_qs, out_dtype = cls.get_pow2_opts(**kwargs)
force_out_q = force_out_qs and force_out_qs[0]
if force_out_q:
o_q = force_out_q
else:
cls.check_valid_ranges(params, stats, idx=0, dirs='out')
o_q = QType.from_min_max_pow2(stats['range_out'][0]['min'],
stats['range_out'][0]['max'],
dtype=out_dtype)
o_q.is_constant = True
return QRec.symmetric(in_qs=None, out_qs=[o_q])
def _quantize(cls, params, in_qs, stats, **kwargs):
force_out_qs, out_dtype = cls.get_pow2_opts(**kwargs)
force_out_q = force_out_qs and force_out_qs[0]
cls.check_valid_ranges(params, stats, idx=0, dirs='out')
o_q = QType.from_min_max_pow2(stats['range_out'][0]['min'],
stats['range_out'][0]['max'],
dtype=out_dtype)
if force_out_q:
if force_out_q.is_pow2 and force_out_q.bits - force_out_q.q < o_q.bits - o_q.q:
LOG.warning('%s is being forced to output in Q%s and may clip',
params.name, force_out_q.q)
o_q = force_out_q
return QRec.symmetric(in_qs=in_qs, out_qs=[o_q])
def _quantize(cls, params, in_qs, stats, **kwargs):
params = cast(QuantizeParameters, params)
force_out_qs = kwargs.get('force_out_qs', None)
force_out_q = force_out_qs and force_out_qs[0]
backwards = kwargs.get('backwards')
# keep the in_qtype for informational purposes
params.from_qtype = in_qs[0]
# if we are going backwards
if backwards:
# if output must be forced
assert force_out_q, f'going backwards at {params.name} but output is not forced'
# change our output to match the force
params.to_qtype = deepcopy(force_out_q)
return QRec(in_qs=deepcopy(in_qs), out_qs=[deepcopy(force_out_q)])
# copy the out_qtype into the qrec
return QRec.symmetric(in_qs=in_qs, out_qs=[deepcopy(params.to_qtype)])
def _quantize(cls, params, in_qs, stats, **kwargs):
force_out_qs, out_dtype = cls.get_pow2_opts(**kwargs)
if stats is None or 'expression' not in stats:
raise ValueError(
f'no valid range information is present for {params.name}')
# expressions need a symmetric input
# this is done on the mult8 version but probably isn't necessary here
# in_qs = cls.force_symmetric(in_qs)
symbol_control = SymbolStats(stats['expression'])
# preload the input and output quantization
# This will force variables to the right scales in the expression quantizer
# first the input
prequant = {
params.input_symbols[idx]: in_q
for idx, in_q in enumerate(in_qs)
}
# now the output
o_qs = []
for idx, sym_name in enumerate(params.output_symbols):
if force_out_qs and force_out_qs[idx]:
o_q = force_out_qs[idx]
else:
cls.check_valid_ranges(params, stats, idx=idx, dirs='out')
o_q = QType.from_min_max_pow2(stats['range_out'][idx]['min'],
stats['range_out'][idx]['max'],
dtype=out_dtype)
prequant[sym_name] = o_q
o_qs.append(o_q)
qfunc_col = params.func_col.quantize(Q15ScaledQuantization,
symbol_control,
quantize_inputs=False,
qtypes=prequant)
return QRec.symmetric(in_qs=in_qs, out_qs=o_qs, qfunc_col=qfunc_col)
def _quantize(cls, params, in_qs, stats, **kwargs):
out_qs = kwargs['out_qs']
return QRec.symmetric(in_qs=in_qs, out_qs=out_qs)
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)
pow2_biases = kwargs.get('opts')['pow2_biases']
G = kwargs['G']
weights_node, biases_node = cls.get_weights_and_biases_nodes(
G, fusion if fusion else params)
range_acc = stats.get('range_acc', stats['range_out'][0])
conv_active = fusion and fusion.fusion_type in [
'conv_active_pool', 'conv_active'
]
int_dtype = np.int32
cls.check_valid_ranges(params, stats, idx=0, dirs='out')
if conv_active:
# Take stats from activation after the convolution
range_out = kwargs['all_stats'][NodeId(
fusion,
fusion.contained_nodes()[1])]['range_out'][0]
out_dtype = np.int32
else:
out_dtype = params_dtype
range_out = stats['range_out'][0]
in_q = deepcopy(in_qs[0]).scale_to_pow2()
calc_width = 31
o_q = QType.from_min_max_pow2(range_out['min'],
range_out['max'],
dtype=out_dtype)
if force_out_q:
if o_q.scale > force_out_q.scale:
return None
weights_q = QType.from_array_pow2(arr=weights_node.dqvalue,
dtype=params_dtype)
calc_q = in_q.q + weights_q.q
acc_bits = calc_bits(range_acc['max'], range_acc['min'])
act_bits = calc_bits(range_out['min'], range_out['max'])
act_acc_bits = max(acc_bits, act_bits)
calc_int_bits = calc_width - calc_q
if calc_int_bits < act_acc_bits:
# we don't have enough space for the integer portion so reduce the precision of
# the weights and input
missing_bits = act_acc_bits - calc_int_bits
if missing_bits > calc_q * 0.75:
raise ValueError(
f'Quantizing {params.name} at this precision will loose more than 75% of fractional part'
)
prec_inp = min(math.floor(0.5 + missing_bits * in_q.q / calc_q),
in_q.q)
prec_w = min(math.floor(0.5 + missing_bits * weights_q.q / calc_q),
weights_q.q)
left = missing_bits - prec_inp - prec_w
if left > 0:
prec_w += left
LOG.warning(
'reducing weight and input precision (%s, %s) in %s to satisfy quantization constraints',
prec_w, prec_inp, params.name)
weights_q.q -= prec_w
in_q.q -= prec_inp
calc_q = in_q.q + weights_q.q
calc_int_bits = calc_width - calc_q
c_q = acc_q = QType(bits=calc_width, q=calc_q, signed=True)
if conv_active:
o_q = c_q
if pow2_biases == 0:
biases_dtype = params_dtype
elif pow2_biases == 8:
biases_dtype = np.int8
elif pow2_biases == 16:
biases_dtype = np.int16
else:
biases_dtype = np.int32
biases_q = QType.from_array_pow2(arr=biases_node.dqvalue,
dtype=biases_dtype)
# make sure that the biases are not stored more precisily than the accumulator. It's pointless and will
# cause a negative shift
if biases_q.q > acc_q.q:
biases_q.q = acc_q.q
if isinstance(params,
MultiplicativeBiasParameters) and params.has_mul_bias:
mb_q = QType.from_array_pow2(arr=params.mul_biases,
dtype=int_dtype)
else:
mb_q = None
return QRec.symmetric(in_qs=[in_q, weights_q, biases_q],
out_qs=[o_q],
calc_q=c_q,
acc_q=acc_q,
mul_biases_q=mb_q)
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)
pow2_biases = kwargs.get('opts')['pow2_biases']
G = kwargs['G']
weights_node, biases_node = cls.get_weights_and_biases_nodes(
G, fusion if fusion else params)
range_acc = stats['range_acc']
conv_active = fusion and fusion.fusion_type in [
'conv_active_pool', 'conv_active'
]
int_dtype = np.int32
cls.check_valid_ranges(params, stats, idx=0, dirs='out')
if conv_active:
# Take stats from activation after the convolution
range_out = kwargs['all_stats'][NodeId(
fusion,
fusion.contained_nodes()[1])]['range_out'][0]
out_dtype = np.int32
else:
out_dtype = params_dtype
range_out = stats['range_out'][0]
in_q = deepcopy(in_qs[0]).scale_to_pow2()
calc_width = 32
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)
weights_q = QType.from_array_pow2(arr=weights_node.dqvalue,
dtype=params_dtype)
calc_q = in_q.q + weights_q.q
acc_bits = calc_bits(range_acc['max'], range_acc['min'])
act_bits = calc_bits(range_out['min'], range_out['max'])
act_acc_bits = max(acc_bits, act_bits)
calc_int_bits = calc_width - calc_q
if calc_int_bits < act_acc_bits:
# we don't have enough space for the integer portion so reduce the precision of
# the weights
missing_bits = act_acc_bits - calc_int_bits
# TODO - This needs improving
assert weights_q.q >= missing_bits, "no space in weights to reduce precision"
LOG.warning(
'reducing weight precision in %s to satisfy quantization constraints',
params.name)
weights_q.q = weights_q.q - missing_bits
calc_q = in_q.q + weights_q.q
calc_int_bits = calc_width - calc_q
c_q = acc_q = QType(bits=calc_width, q=calc_q, signed=True)
if conv_active:
o_q = c_q
if pow2_biases == 0:
biases_dtype = params_dtype
elif pow2_biases == 8:
biases_dtype = np.int8
elif pow2_biases == 16:
biases_dtype = np.int16
else:
biases_dtype = np.int32
biases_q = QType.from_array_pow2(arr=biases_node.dqvalue,
dtype=biases_dtype)
# make sure that the biases are not stored more precisily than the accumulator. It's pointless and will
# cause a negative shift
if biases_q.q > acc_q.q:
biases_q.q = acc_q.q
if isinstance(params,
MultiplicativeBiasParameters) and params.has_mul_bias:
mb_q = QType.from_array_pow2(arr=params.mul_biases,
dtype=int_dtype)
else:
mb_q = None
return QRec.symmetric(in_qs=[in_q, weights_q, biases_q],
out_qs=[o_q],
calc_q=c_q,
acc_q=acc_q,
mul_biases_q=mb_q)
