Typo + ocp/fnuz quantizers

notebooks/minifloat_mx_tutorial.ipynb

@@ -140,7 +140,7 @@
     "```\n",
     "\n",
     "With respect to pre-defined quantizers, Brevitas offers several Groupwise and MX options.\n",
-    "The meain difference between the two is that MX is restricted to group_size=32 and the scale factor must be a power-of-2.\n",
+    "The main difference between the two is that MX is restricted to group_size=32 and the scale factor must be a power-of-2.\n",
     "The user can override these settings but the corresponding output won't be MX compliant.\n",
     "\n",
     "Another difference is that MXFloat relies on the OCP format as underlying data type, while generic groupwise float relies on the non-standard minifloat representation explained above.\n",

src/brevitas_examples/llm/main.py

@@ -81,7 +81,8 @@
     '--weight-quant-format',
     type=quant_format_validator,
     default='int',
-    help='Weight quantization type. Either int or eXmY, with X+Y==weight_bit_width-1. Default: int.'
+    help=
+    'Weight quantization type. Either int or eXmY, with X+Y==weight_bit_width-1. It\'s possible to add float_ocp_ or float_fnuz_ before the exponent/mantissa bitwidth. Default: int.'
 )
 parser.add_argument(
     '--weight-quant-granularity',
@@ -105,7 +106,9 @@
     '--input-quant-format',
     type=quant_format_validator,
     default='int',
-    help='Input quantization type. Either int or eXmY, with X+Y==weight_bit_width-1. Default: int.')
+    help=
+    'Input quantization type. Either int or eXmY, with X+Y==weight_bit_width-1. It\'s possible to add float_ocp_ or float_fnuz_ before the exponent/mantissa bitwidth. Default: int.'
+)
 parser.add_argument(
     '--input-param-method',
     type=str,
@@ -187,10 +190,6 @@
     type=str,
     default=None,
     help="Filename to save checkpoint. If `None`, no checkpoint is saved (default: %(default)s)")
-add_bool_arg(
-    parser, 'use-ocp', default=False, help='Use OCP format for float quantization. Default: False')
-add_bool_arg(
-    parser, 'use-fnuz', default=True, help='Use FNUZ format for float quantization. Default: True')
 
 
 def set_seed(seed):
@@ -385,8 +384,6 @@ def main():
             input_quant_granularity=args.input_quant_granularity,
             input_group_size=args.input_group_size,
             quantize_input_zero_point=args.quantize_input_zero_point,
-            use_ocp=args.use_ocp,
-            use_fnuz=args.use_fnuz,
             device=device)
         layer_map = generate_quant_maps(
             linear_input_quant=linear_input_quant,

src/brevitas_examples/stable_diffusion/main.py

@@ -759,13 +759,15 @@ def input_zp_stats_type():
         type=quant_format_validator,
         default='int',
         help=
-        'Weight quantization type. Either int or eXmY, with X+Y==weight_bit_width-1. Default: int.')
+        'Weight quantization type. Either int or eXmY, with X+Y==weight_bit_width-1. It\'s possible to add float_ocp_ or float_fnuz_ before the exponent/mantissa bitwidth. Default: int.'
+    )
     parser.add_argument(
         '--input-quant-format',
         type=quant_format_validator,
         default='int',
         help=
-        'Input quantization type. Either int or eXmY, with X+Y==input_bit_width-1. Default: int.')
+        'Input quantization type. Either int or eXmY, with X+Y==input_bit_width-1. It\'s possible to add float_ocp_ or float_fnuz_ before the exponent/mantissa bitwidth. Default: int.'
+    )
     parser.add_argument(
         '--weight-quant-granularity',
         type=str,