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feat (ex/llm): boilerplate for adding new custom quantizer #1377

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feat (ex/llm): boilerplate for adding new custom quantizer #1377

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5 changes: 5 additions & 0 deletions src/brevitas_examples/common/generative/quantize.py
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Expand Up @@ -79,6 +79,7 @@
from brevitas_examples.common.generative.quantizers import ShiftedUint8DynamicActPerGroupFloat
from brevitas_examples.common.generative.quantizers import ShiftedUint8DynamicActPerRowFloat
from brevitas_examples.common.generative.quantizers import ShiftedUint8DynamicActPerTensorFloat
from brevitas_examples.common.generative.quantizers import Int8WeightPerChannelFloatSparse

WEIGHT_QUANT_MAP = {
'int': {
Expand Down Expand Up @@ -107,6 +108,10 @@
'asym': ShiftedUint8WeightPerChannelFloatHQO},
'per_group': {
'asym': ShiftedUint8WeightPerGroupFloatHQO}},},
'float_sparse_scale': {
'stats': {
'per_channel': {
'sym': Int8WeightPerChannelFloatSparse}}},
'po2_scale': {
'stats': {
'per_tensor': {
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16 changes: 16 additions & 0 deletions src/brevitas_examples/common/generative/quantizers.py
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Expand Up @@ -213,3 +213,19 @@ class FP8e4m3FNUZDynamicActPerRowFloat(Fp8e4m3FNUZActPerTensorFloat):
scaling_stats_op = 'min_max'
scaling_per_output_channel = True
proxy_class = DynamicActFloatQuantProxyFromInjector

def sparse_ste(x, sparse_ratio):
pass

class SparseRoundSte(torch.nn.Module):
def __init__(self, sparse_ratio):
self.sparse_ratio = sparse_ratio
super().__init__()

def forward(self, x):
# Sparse logic here
return round_ste(x)

class Int8WeightPerChannelFloatSparse(Int8WeightPerChannelFloat):
float_to_int_impl = SparseRoundSte
sparse_ratio = 0.5
2 changes: 1 addition & 1 deletion src/brevitas_examples/llm/llm_args.py
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Expand Up @@ -63,7 +63,7 @@ def create_args_parser() -> ArgumentParser:
'--weight-scale-precision',
type=str,
default='float_scale',
choices=['float_scale', 'po2_scale'],
choices=['float_scale', 'float_sparse_scale', 'po2_scale'],
help='Whether scale is a float value or a po2. Default: po2.')
parser.add_argument(
'--weight-quant-rescaling-init',
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