def _quantize(cls, params, in_qs, stats, **kwargs):
_, dtype = cls.get_float_opts(**kwargs)
names = {
val: idx
for idx, val in enumerate(LSTMParameters.INPUT_NAMES)
}
edges = kwargs['G'].indexed_in_edges(params.name)
in_qs = deepcopy(in_qs)
scale_pairs = {
chan: ('i_2_%s_w' % chan, 'r_2_%s_w' % chan)
for chan in ['i', 'o', 'c', 'f']
}
for scale_pair in scale_pairs.values():
w_q = in_qs[names[scale_pair[0]]]
in_qs[names[scale_pair[0]]] = QType(min_val=w_q.min_val,
max_val=w_q.max_val,
dtype=dtype,
dont_generate_value=True)
w_q = in_qs[names[scale_pair[1]]]
in_qs[names[scale_pair[1]]] = QType(
min_val=w_q.min_val,
max_val=w_q.max_val,
dtype=dtype,
concatenated_nodes=[
edges[names[scale_pair[0]]].from_node.name
])
if params.lstm_output_c_state:
out_qs = [QType(dtype=dtype), QType(dtype=dtype)]
else:
out_qs = [QType(dtype=dtype)]
return QRec.float(in_qs=in_qs, out_qs=out_qs, float_dtype=dtype)
def _quantize(cls, params, in_qs, stats, **kwargs):
force_out_qs, dtype = cls.get_float_opts(**kwargs)
if force_out_qs and all(qtype.dtype != dtype for qtype in force_out_qs if qtype is not None):
return None
return QRec.float(in_qs=[QType(dtype=dtype), QType(dtype=dtype), QType(dtype=dtype), QType(dtype=np.int16), QType(dtype=dtype), QType(dtype=np.int16), QType(dtype=dtype), QType(dtype=dtype)],
out_qs=[QType(dtype=dtype)],
float_dtype=dtype)
def _quantize(cls, params, in_qs, stats, **kwargs):
_, dtype = cls.get_float_opts(**kwargs)
out_qs = [deepcopy(in_qs[0])]
opts = kwargs['opts']
if opts['hwc']:
cls.check_order(params, AT_HWC_KER_IN_ORDER, AT_HWC_KER_OUT_ORDER)
else:
cls.check_order(params, AT_CHW_KER_IN_ORDER, AT_CHW_KER_OUT_ORDER)
return QRec.float(in_qs=in_qs, out_qs=out_qs, float_dtype=dtype)
def _quantize(cls, params, in_qs, stats, **kwargs):
force_out_qs, dtype = cls.get_float_opts(**kwargs)
if force_out_qs and all(qtype.dtype != dtype for qtype in force_out_qs if qtype is not None):
return None
# all inputs and outputs are set to the required float type
return QRec.float(in_qs=[QType(dtype=dtype)
for _ in range(3)],
out_qs=[QType(dtype=dtype)],
float_dtype=dtype)
def _quantize(cls, params, in_qs, stats, **kwargs):
force_out_qs, dtype = cls.get_float_opts(**kwargs)
if force_out_qs:
o_q = force_out_qs[0]
else:
o_q = QType(dtype=dtype)
o_q.is_constant = True
return QRec.float(in_qs=None, out_qs=[o_q], float_dtype=dtype)
def _quantize(cls, params, in_qs, stats, **kwargs):
params = cast(QuantizeParameters, params)
force_out_qs, dtype = cls.get_float_opts(**kwargs)
if force_out_qs:
o_q = deepcopy(force_out_qs[0])
else:
o_q = QType(dtype=dtype,
min_val=in_qs[0].min_val,
max_val=in_qs[0].max_val)
params.from_qtype = in_qs[0]
params.to_qtype = o_q
return QRec.float(in_qs=in_qs, out_qs=[o_q], float_dtype=dtype)
def _quantize(cls, params, in_qs, stats, **kwargs):
force_out_qs, dtype = cls.get_float_opts(**kwargs)
if force_out_qs and any(qtype.dtype != dtype for qtype in force_out_qs
if qtype is not None):
return None
# all inputs and outputs are set to the required float type
opts = kwargs['opts']
if opts['hwc']:
cls.check_order(params, AT_HWC_KER_IN_ORDER, AT_HWC_KER_OUT_ORDER)
else:
cls.check_order(params, AT_CHW_KER_IN_ORDER, AT_CHW_KER_OUT_ORDER)
return QRec.float(in_qs=[QType(dtype=dtype) for _ in range(3)],
out_qs=[QType(dtype=dtype)],
float_dtype=dtype)
def _quantize(cls, params, in_qs, stats, **kwargs):
force_out_qs, dtype = cls.get_float_opts(**kwargs)
if force_out_qs and all(qtype.dtype != dtype for qtype in force_out_qs
if qtype is not None):
return None
# use cur_G not G here since this may be called inside a fusion
# cur_G == G or fusion subgraph if inside fusion
G = kwargs['cur_G']
in_len = len(G.indexed_in_edges(params.name))
out_len = len(G.indexed_out_edges(params.name))
# all inputs and outputs are set to the required float type
return QRec.float(in_qs=[QType(dtype=dtype) for _ in range(in_len)],
out_qs=[QType(dtype=dtype) for _ in range(out_len)],
float_dtype=dtype)
def _quantize(cls, params, in_qs, stats, **kwargs):
force_out_qs, dtype = cls.get_float_opts(**kwargs)
if force_out_qs and any(qtype.dtype != dtype for qtype in force_out_qs
if qtype is not None):
return None
opts = kwargs['opts']
if opts['hwc']:
cls.check_order(params, [['h', 'w', 'c']], [['h', 'w', 'c']])
elif params.in_dims_hint:
cls.check_order(params, [['c', 'h', 'w']], [['c', 'h', 'w']])
# all inputs and outputs are set to the required float type
return QRec.float(in_qs=[QType(dtype=dtype)],
out_qs=[QType(dtype=dtype)],
float_dtype=dtype)
def _quantize(cls, params, in_qs, stats, **kwargs):
_, dtype = cls.get_float_opts(**kwargs)
names = {val: idx for idx, val in enumerate(RNNParameters.INPUT_NAMES)}
edges = kwargs['G'].indexed_in_edges(params.name)
in_qs = deepcopy(in_qs)
w_q = in_qs[names['i_2_i_w']]
in_qs[names['i_2_i_w']] = QType.from_min_max_sq(
w_q.min_val, w_q.max_val, dtype=dtype, dont_generate_value=True)
w_q = in_qs[names['r_2_i_w']]
in_qs[names['r_2_i_w']] = QType.from_min_max_sq(
w_q.min_val,
w_q.max_val,
dtype=dtype,
concatenated_nodes=[edges[names['i_2_i_w']].from_node.name])
return QRec.float(in_qs=in_qs,
out_qs=[QType(dtype=dtype)],
float_dtype=dtype)
def _quantize(cls, params, in_qs, stats, **kwargs):
_, dtype = cls.get_float_opts(**kwargs)
force_out_qs = kwargs.get('force_out_qs')
if stats is None or 'expression' not in stats:
raise ValueError(
f'no valid range information is present for {params.name}')
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(dtype=dtype)
prequant[sym_name] = o_q
o_qs.append(o_q)
qfunc_col = params.func_col.quantize(FloatQuantization,
symbol_control,
quantize_inputs=False,
qtypes=prequant)
return QRec.float(in_qs=in_qs,
out_qs=o_qs,
float_dtype=dtype,
qfunc_col=qfunc_col)
def _quantize(cls, params, in_qs, stats, **kwargs):
_, dtype = cls.get_float_opts(**kwargs)
out_qs = kwargs['out_qs']
return QRec.float(in_qs=in_qs, out_qs=out_qs, float_dtype=dtype)
