Fix (llm): small fixes to LLM

src/brevitas/core/function_wrapper/shape.py

@@ -165,6 +165,11 @@ def __init__(self, expanded_groupwise_shape, group_size, group_dim) -> None:
 
     @brevitas.jit.script_method
     def forward(self, x: torch.Tensor):
+        # This one is a bit tricky but we could end up here:
+        # - If we quantize the zero point, which will already have expanded shape matching the scale (although no padding, but we don't need the padding)
+        # - Groupwise HQO quantization, where weight will already have been padded and expanded
+        if len(x.shape) == len(self.expanded_groupwise_shape):
+            return x
         y = torch.nn.functional.pad(
             x, padding(x, self.group_size, self.group_dim), mode='constant', value=0.)
         y = y.view(self.expanded_groupwise_shape)

src/brevitas/core/stats/stats_op.py

@@ -692,14 +692,18 @@ def parameter_search(self, xl, x):
                 self.set_local_loss_mode(False)
                 qt_value = self.input_view_shape_impl(quant_tensor.value)
                 qt_scale = self.input_view_shape_impl(quant_tensor.scale)
-                qt_int = self.input_view_shape_impl(quant_tensor.int())
+                qt_zp = self.input_view_shape_impl(quant_tensor.zero_point)
+                qt_int = qt_value / qt_scale + qt_zp
                 loss = torch.abs(qt_value - x).mean()
                 best_candidate = torch.where(loss < best_loss, candidate, best_candidate)
                 if loss >= best_loss:
                     break
                 best_loss = torch.min(loss, best_loss)
                 W_e = shrink_lp_op(x - qt_value, self.beta, self.lp_norm)
 
+                # Compared to the original formulation, the value we're looking for is:
+                # - scaled by qt_scale
+                # - opposite sign
                 val = self.input_view_shape_impl((x - W_e) - qt_int * qt_scale)
 
                 if self.stats_reduce_dim is None:

src/brevitas/quant/base.py

@@ -429,41 +429,31 @@ class AccumulatorAwareZeroCenterWeightQuant(AccumulatorAwareWeightQuant):
     pre_zero_point_stats_input_view_shape_impl = this.scaling_stats_input_view_shape_impl
 
 
-class MSESubInjectorBase(ExtendedInjector):
-
-    @value
-    def inner_stats_input_view_shape_impl(scaling_per_output):
-        if scaling_per_output == ScalingPerOutputType.CHANNEL:
-            return StatsInputViewShapeImpl.OVER_OUTPUT_CHANNELS
-        elif scaling_per_output == ScalingPerOutputType.TENSOR:
-            return StatsInputViewShapeImpl.OVER_TENSOR
-        elif scaling_per_output == ScalingPerOutputType.GROUP:
-            raise RuntimeError("Not implemented yet")
-
-    permute_dims = (this << 1).permute_dims
-
-
-class MSESymmetricScaleSubInjector(MSESubInjectorBase):
+class MSESymmetricScaleSubInjector(ExtendedInjector):
     scaling_per_output = (this << 1).scaling_per_output
     proxy_module = (this << 1).proxy_module
     mse_init_op = AbsMax
     stats_impl = MSE
     stats_reduce_dim = (this << 1).stats_reduce_dim
     device = (this << 1).device
     type = (this << 1).type
+    permute_dims = (this << 1).permute_dims
+    inner_stats_input_view_shape_impl = (this << 1).inner_stats_input_view_shape_impl
 
 
-class MSEAsymmetricScaleSubInjector(MSESubInjectorBase):
+class MSEAsymmetricScaleSubInjector(ExtendedInjector):
     scaling_per_output = (this << 1).scaling_per_output
     proxy_module = (this << 1).proxy_module
     mse_init_op = AbsMinMax
     stats_impl = MSE
     stats_reduce_dim = (this << 1).stats_reduce_dim
     device = (this << 1).device
     dtype = (this << 1).dtype
+    permute_dims = (this << 1).permute_dims
+    inner_stats_input_view_shape_impl = (this << 1).inner_stats_input_view_shape_impl
 
 
-class MSEZeroPointSubInjector(MSESubInjectorBase):
+class MSEZeroPointSubInjector(ExtendedInjector):
     # zp is per channel when scaling is per channel
     scaling_per_output = (this << 1).scaling_per_output
     proxy_module = (this << 1).proxy_module
@@ -473,6 +463,8 @@ class MSEZeroPointSubInjector(MSESubInjectorBase):
     stats_reduce_dim = (this << 1).stats_reduce_dim
     device = (this << 1).device
     dtype = (this << 1).dtype
+    permute_dims = (this << 1).permute_dims
+    inner_stats_input_view_shape_impl = (this << 1).inner_stats_input_view_shape_impl
 
 
 class MSEAsymmetricScale(ExtendedInjector):
@@ -484,6 +476,15 @@ class MSEAsymmetricScale(ExtendedInjector):
     scaling_impl_type = ScalingImplType.PARAMETER_FROM_STATS
     scaling_stats_input_view_shape_impl = nn.Identity()
 
+    @value
+    def inner_stats_input_view_shape_impl(scaling_per_output):
+        if scaling_per_output == ScalingPerOutputType.CHANNEL:
+            return StatsInputViewShapeImpl.OVER_OUTPUT_CHANNELS
+        elif scaling_per_output == ScalingPerOutputType.TENSOR:
+            return StatsInputViewShapeImpl.OVER_TENSOR
+        elif scaling_per_output == ScalingPerOutputType.GROUP:
+            return StatsInputViewShapeImpl.OVER_SUBCHANNEL_BLOCK
+
     @value
     def scaling_stats_impl():
         return this.mse_scale.stats_impl
@@ -498,6 +499,15 @@ class MSESymmetricScale(ExtendedInjector):
     scaling_impl_type = ScalingImplType.PARAMETER_FROM_STATS
     scaling_stats_input_view_shape_impl = nn.Identity()
 
+    @value
+    def inner_stats_input_view_shape_impl(scaling_per_output):
+        if scaling_per_output == ScalingPerOutputType.CHANNEL:
+            return StatsInputViewShapeImpl.OVER_OUTPUT_CHANNELS
+        elif scaling_per_output == ScalingPerOutputType.TENSOR:
+            return StatsInputViewShapeImpl.OVER_TENSOR
+        elif scaling_per_output == ScalingPerOutputType.GROUP:
+            return StatsInputViewShapeImpl.OVER_SUBCHANNEL_BLOCK
+
     @value
     def scaling_stats_impl():
         return this.mse_scale.stats_impl
@@ -511,6 +521,15 @@ class MSEZeroPoint(ExtendedInjector):
     mse_zero_point = MSEZeroPointSubInjector
     zero_point_stats_input_view_shape_impl = nn.Identity()
 
+    @value
+    def inner_stats_input_view_shape_impl(scaling_per_output):
+        if scaling_per_output == ScalingPerOutputType.CHANNEL:
+            return StatsInputViewShapeImpl.OVER_OUTPUT_CHANNELS
+        elif scaling_per_output == ScalingPerOutputType.TENSOR:
+            return StatsInputViewShapeImpl.OVER_TENSOR
+        elif scaling_per_output == ScalingPerOutputType.GROUP:
+            return StatsInputViewShapeImpl.OVER_SUBCHANNEL_BLOCK
+
     @value
     def zero_point_stats_impl():
         return this.mse_zero_point.stats_impl

src/brevitas/quant/experimental/mx_quant_ocp.py

@@ -123,14 +123,14 @@ class MXInt8Act(MXActMixin, GroupwiseActProxyMixin, IntQuant, MaxStatsScaling, A
     bit_width = 8
 
 
-class MXInt8WeightMSE(MXInt8Weight, MSESymmetricScale):
+class MXInt8WeightMSE(MSESymmetricScale, MXInt8Weight):
     """
     MX Int signed weight quantizer with per-channel MSE-based scaling.
     """
     pass
 
 
-class ShiftedMXUInt8WeightMSE(ShiftedMXUInt8Weight, MSEAsymmetricScale):
+class ShiftedMXUInt8WeightMSE(MSEAsymmetricScale, ShiftedMXUInt8Weight):
     """
     MX Int signed weight quantizer with per-channel MSE-based scaling.
     """

src/brevitas/quant/shifted_scaled_int.py

@@ -5,7 +5,7 @@
 from brevitas.proxy.groupwise_int_parameter_quant import GroupwiseWeightQuantProxyFromInjector
 from brevitas.quant.base import *
 from brevitas.quant.base import HQOActZeroPoint
-from brevitas.quant.base import HQOZeroPoint
+from brevitas.quant.base import HQOWeightZeroPoint
 from brevitas.quant.solver.act import ActQuantSolver
 from brevitas.quant.solver.weight import WeightQuantSolver
 
@@ -145,7 +145,7 @@ class ShiftedUint8WeightPerChannelFloatMSE(MSEAsymmetricScale,
     pass
 
 
-class ShiftedUint8WeightPerTensorFloatHQO(HQOZeroPoint, ShiftedUint8WeightPerTensorFloat):
+class ShiftedUint8WeightPerTensorFloatHQO(HQOWeightZeroPoint, ShiftedUint8WeightPerTensorFloat):
     """
     8-bit per-tensor unsigned int weight quantizer with floating-point per-channel scale factor and integer
     zero point. Zero-point is initialized from HQO local loss.
@@ -157,7 +157,7 @@ class ShiftedUint8WeightPerTensorFloatHQO(HQOZeroPoint, ShiftedUint8WeightPerTen
     quantize_zero_point = False
 
 
-class ShiftedUint8WeightPerChannelFloatHQO(HQOZeroPoint, ShiftedUint8WeightPerChannelFloat):
+class ShiftedUint8WeightPerChannelFloatHQO(HQOWeightZeroPoint, ShiftedUint8WeightPerChannelFloat):
     """
     8-bit per-tensor unsigned int weight quantizer with floating-point per-channel scale factor and integer
     zero point. Zero-point is initialized from HQO local loss.

src/brevitas_examples/common/generative/quantize.py

@@ -102,9 +102,9 @@
                 'per_tensor': {
                     'sym': Int8WeightPerTensorFixedPointMSE},
                 'per_channel': {
-                    'sym': Int8WeightPerChannelFixedPointMSE}},
-            'per_group': {
-                'sym': MXInt8WeightMSE, 'asym': ShiftedMXUInt8WeightMSE}}},
+                    'sym': Int8WeightPerChannelFixedPointMSE},
+                'per_group': {
+                    'sym': MXInt8WeightMSE, 'asym': ShiftedMXUInt8WeightMSE}}}},
     'float': {
         'float_scale': {
             'stats': {
@@ -210,6 +210,7 @@ def generate_quantizers(
         weight_group_size,
         quantize_weight_zero_point,
         weight_quant_format='int',
+        weight_group_dim=None,
         input_bit_width=None,
         input_quant_format='',
         input_scale_precision=None,
@@ -276,6 +277,10 @@ def generate_quantizers(
             'narrow_range': False,
             'quantize_zero_point': quantize_weight_zero_point},
         **weight_float_format)
+
+    if weight_group_dim is not None:
+        weight_quant = weight_quant.let(**{'group_dim': weight_group_dim})
+
     if dtype == torch.float16:
         weight_quant = weight_quant.let(**{'scaling_min_val': 1e-4})
     if weight_kwargs is not None:
@@ -285,9 +290,8 @@ def generate_quantizers(
     if weight_quant_granularity == 'per_group':
         weight_quant = weight_quant.let(**{'group_size': weight_group_size})
     # weight scale is converted to a standalone parameter
-    # This is done already by default in the per_group quantizer
-    if weight_quant_granularity != 'per_group':
-        weight_quant = weight_quant.let(scaling_impl_type='parameter_from_stats')
+
+    weight_quant = weight_quant.let(scaling_impl_type='parameter_from_stats')
     # weight zero-point is converted to a standalone parameter
     # This is done already by default in the per_group quantizer
     if weight_quant_type == 'asym' and weight_quant_granularity != 'per_group':

src/brevitas_examples/llm/README.md

@@ -17,11 +17,12 @@ Set the env variable BREVITAS_JIT=1 to speed up the quantization process. Curren
 usage: main.py [-h] [--model MODEL] [--seed SEED] [--nsamples NSAMPLES]
                [--seqlen SEQLEN] [--eval] [--dataset {wikitext2,c4}]
                [--weight-bit-width WEIGHT_BIT_WIDTH]
-               [--weight-param-method {stats,mse}]
+               [--weight-param-method {stats,mse,hqo}]
                [--weight-scale-precision {float_scale,po2_scale}]
                [--weight-quant-type {sym,asym}]
                [--weight-quant-format WEIGHT_QUANT_FORMAT]
                [--weight-quant-granularity {per_channel,per_tensor,per_group}]
+               [--weight-group-dim {1,0}]
                [--weight-group-size WEIGHT_GROUP_SIZE]
                [--quantize-weight-zero-point]
                [--input-bit-width INPUT_BIT_WIDTH]
@@ -38,6 +39,7 @@ usage: main.py [-h] [--model MODEL] [--seed SEED] [--nsamples NSAMPLES]
                [--weight-equalization]
                [--act-equalization {None,layerwise,fx}] [--load-awq LOAD_AWQ]
                [--export-target {None,onnx_qcdq,torch_qcdq,sharded_torchmlir_group_weight,sharded_packed_torchmlir_group_weight}]
+               [--export-prefix EXPORT_PREFIX]
                [--checkpoint-name CHECKPOINT_NAME]
 
 options:
@@ -51,7 +53,7 @@ options:
                         Dataset to use for quantization (default: wikitext2)
   --weight-bit-width WEIGHT_BIT_WIDTH
                         Weight bit width. Default: 8.
-  --weight-param-method {stats,mse}
+  --weight-param-method {stats,mse,hqo}
                         How scales/zero-point are determined. Default: stats.
   --weight-scale-precision {float_scale,po2_scale}
                         Whether scale is a float value or a po2. Default: po2.
@@ -65,6 +67,9 @@ options:
   --weight-quant-granularity {per_channel,per_tensor,per_group}
                         Granularity for scales/zero-point of weights. Default:
                         per_group.
+  --weight-group-dim {1,0}
+                        Override default group_dim for groupsize quantization.
+                        Default: layer-dependant
   --weight-group-size WEIGHT_GROUP_SIZE
                         Group size for per_group weight quantization. Default:
                         128.
@@ -119,6 +124,10 @@ options:
   --load-awq LOAD_AWQ   Load the awq search results.
   --export-target {None,onnx_qcdq,torch_qcdq,sharded_torchmlir_group_weight,sharded_packed_torchmlir_group_weight}
                         Model export.
+  --export-prefix EXPORT_PREFIX
+                        Path prefix to use for the various export flows. If
+                        None, a path will be derived from the model name
+                        (default: None)
   --checkpoint-name CHECKPOINT_NAME
                         Filename to save checkpoint. If `None`, no checkpoint
                         is saved (default: None)

src/brevitas_examples/llm/main.py

@@ -226,6 +226,7 @@ def main(args):
             weight_quant_type=args.weight_quant_type,
             weight_quant_granularity=args.weight_quant_granularity,
             weight_group_size=args.weight_group_size,
+            weight_group_dim=args.weight_group_dim,
             quantize_weight_zero_point=args.quantize_weight_zero_point,
             weight_quant_format=args.weight_quant_format,
             input_bit_width=args.input_bit_width,
@@ -358,6 +359,12 @@ def parse_args(args):
         default='per_group',
         choices=['per_channel', 'per_tensor', 'per_group'],
         help='Granularity for scales/zero-point of weights. Default: per_group.')
+    parser.add_argument(
+        '--weight-group-dim',
+        type=int,
+        default=None,
+        choices=[1, 0],
+        help='Override default group_dim for groupsize quantization. Default: layer-dependant')
     parser.add_argument(
         '--weight-group-size',
         type=int,