Feat (mx): unpadding during dequantization

notebooks/minifloat_mx_tutorial.ipynb

@@ -206,15 +206,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Non padding weights shape torch.Size([64, 8, 3, 3])\n",
-      "Padded weights shape torch.Size([64, 32, 3, 3])\n"
+      "Non padding weights shape torch.Size([64, 1, 8, 3, 3])\n",
+      "Padded weights shape torch.Size([64, 1, 32, 3, 3])\n"
      ]
     }
    ],
@@ -257,8 +257,8 @@
     "o = mx_model(x)\n",
     "\n",
     "# The quant weight of the padded model is different from the non padding one\n",
-    "print(f\"Non padding weights shape {mx_model_no_padding.conv.quant_weight().value.shape}\")\n",
-    "print(f\"Padded weights shape {mx_model.conv.quant_weight().value.shape}\")\n",
+    "print(f\"Non padding weights shape {mx_model_no_padding.conv.quant_weight().value_.shape}\")\n",
+    "print(f\"Padded weights shape {mx_model.conv.quant_weight().value_.shape}\")\n",
     "\n",
     "# However, results are still the same \n",
     "assert torch.allclose(o, o_no_padding)"

src/brevitas/export/inference/handler.py

@@ -24,6 +24,7 @@
 from brevitas.proxy.runtime_quant import ActQuantProxyFromInjector
 from brevitas.proxy.runtime_quant import DynamicActQuantProxyFromInjector
 from brevitas.quant.experimental.mx_quant_ocp import GroupwiseActQuantProxyFromInjector
+from brevitas.utils.quant_utils import groupwise_dequant_expand
 from brevitas.utils.torch_utils import float_internal_scale
 
 
@@ -146,8 +147,8 @@ def __init__(self):
     def prepare_for_export(self, module):
         super().prepare_for_export(module)
         if module.is_quant_enabled:
+            self.group_dim = module.group_dim
             self.input_view = module.input_view_impl
-            self.flattened_view = module.apply_input_view
             if module._cached_weight is not None and not module.cache_inference_quant_weight_metadata_only:
                 self.cached_weight = module._cached_weight.quant_tensor.value_
             else:
@@ -165,12 +166,13 @@ def forward(self, x: Tensor) -> Tuple[Tensor]:
         if self.cached_weight is not None:
             out = self.cached_weight
         else:
+            inp_shape = x.shape
             x = self.input_view(x)
             out = self.dequantize(self.quantize(x, scale, zero_point), scale, zero_point)
 
             # If we skip quant tensor, we return the flattened version of the groupwise tensor
             if self.skip_create_quant_tensor:
-                out = self.flattened_view(out)
+                out = groupwise_dequant_expand(out, scale, zero_point, self.group_dim, inp_shape)[0]
         return out, scale, zero_point, self.bit_width
 
 
@@ -294,6 +296,7 @@ def prepare_for_export(self, module: nn.Module):
         if module.is_quant_enabled:
             self.input_view = module.input_view_impl
             self.flattened_view = module.apply_input_view
+            self.group_dim = module.group_dim
             if module._cached_weight is not None and not module.cache_inference_quant_weight_metadata_only:
                 self.cached_weight = module._cached_weight.quant_tensor.value_
             else:
@@ -311,11 +314,12 @@ def forward(self, x: Tensor) -> Tuple[Tensor]:
         if self.cached_weight is not None:
             out = self.cached_weight
         else:
+            inp_shape = x.shape
             x = self.input_view(x)
             out = self.dequantize(self.quantize(x, scale, zero_point), scale, zero_point)
 
             # If we skip quant tensor, we return the flattened version of the groupwise tensor
             if self.skip_create_quant_tensor:
-                out = self.flattened_view(out)
+                out = groupwise_dequant_expand(out, scale, zero_point, self.group_dim, inp_shape)[0]
 
         return out, scale, zero_point, self.exponent_bit_width, self.mantissa_bit_width, self.exponent_bias, self.saturating, self.inf_values, self.nan_values

src/brevitas/graph/gpxq.py

@@ -187,12 +187,6 @@ def __init__(
         self.layer = layer
         self.name = name
         self.act_order = act_order
-        if self.layer.weight_quant.is_groupwise:
-            weight = self.layer.weight_quant.apply_input_view(self.layer.weight)
-            weight = weight.view(self.layer.weight_quant.quant_injector.reshaped_groupwise_shape)
-            self.layer.weight.data = weight.data
-            self.layer.in_channels = weight.shape[1] if is_conv_transposed(
-                self.layer) else weight.shape[0]
 
         weight_shape = torch.tensor(layer.weight.shape)
 

src/brevitas/proxy/float_runtime_quant.py

@@ -77,7 +77,7 @@ def create_quant_tensor(
             self,
             qt_args: Union[torch.Tensor, Tuple[Any]],
             x: Optional[FloatQuantTensor] = None) -> FloatQuantTensor:
-        if x is None:
+        if isinstance(qt_args, tuple):
             out = FloatQuantTensor(*qt_args, signed=self.is_signed, training=self.training)
         else:
             out = FloatQuantTensor(

src/brevitas/proxy/groupwise_float_parameter_quant.py

@@ -34,6 +34,7 @@ def apply_input_view(self, x):
         return x.flatten(start_dim, start_dim + 1)
 
     def create_quant_tensor(self, qt_args: Tuple[Any]) -> GroupwiseFloatQuantTensor:
+        shape = self.tracked_parameter_list[0].shape
         out, scale, zero_point, exponent_bit_width, mantissa_bit_width, exponent_bias, saturating, inf_values, nan_values = qt_args
         return GroupwiseFloatQuantTensor(
             out,
@@ -48,4 +49,5 @@ def create_quant_tensor(self, qt_args: Tuple[Any]) -> GroupwiseFloatQuantTensor:
             inf_values,
             nan_values,
             self.is_signed,
-            self.training)
+            self.training,
+            shape)

src/brevitas/proxy/groupwise_float_runtime_quant.py

@@ -29,8 +29,8 @@ def apply_input_view(self, x):
     def create_quant_tensor(
             self,
             qt_args: Union[torch.Tensor, Tuple[Any]],
-            x: Optional[GroupwiseFloatQuantTensor] = None) -> GroupwiseFloatQuantTensor:
-        if x is None:
+            x: Union[torch.Tensor, GroupwiseFloatQuantTensor]) -> GroupwiseFloatQuantTensor:
+        if isinstance(qt_args, tuple):
             value, scale, zero_point, exponent_bit_width, mantissa_bit_width, exponent_bias, saturating, inf_values, nan_values = qt_args
             out = GroupwiseFloatQuantTensor(
                 value,
@@ -45,7 +45,8 @@ def create_quant_tensor(
                 inf_values,
                 nan_values,
                 self.is_signed,
-                self.training)
+                self.training,
+                dequant_shape=x.shape)
         else:
             out = GroupwiseFloatQuantTensor(
                 qt_args,
@@ -60,5 +61,6 @@ def create_quant_tensor(
                 x.inf_values,
                 x.nan_values,
                 x.signed,
-                self.training)
+                self.training,
+                x.shape)
         return out

src/brevitas/proxy/groupwise_int_parameter_quant.py

@@ -34,6 +34,7 @@ def apply_input_view(self, x):
         return x.flatten(start_dim, start_dim + 1)
 
     def create_quant_tensor(self, qt_args: Tuple[Any]) -> GroupwiseIntQuantTensor:
+        shape = self.tracked_parameter_list[0].shape
         out, scale, zero_point, bit_width = qt_args
         return GroupwiseIntQuantTensor(
             out,
@@ -43,4 +44,5 @@ def create_quant_tensor(self, qt_args: Tuple[Any]) -> GroupwiseIntQuantTensor:
             self.group_dim,
             bit_width,
             self.is_signed,
-            self.training)
+            self.training,
+            shape)

src/brevitas/proxy/groupwise_int_runtime_quant.py

@@ -29,8 +29,8 @@ def apply_input_view(self, x):
     def create_quant_tensor(
             self,
             qt_args: Union[torch.Tensor, Tuple[Any]],
-            x: Optional[GroupwiseIntQuantTensor] = None) -> GroupwiseIntQuantTensor:
-        if x is None:
+            x: Union[torch.Tensor, GroupwiseIntQuantTensor]) -> GroupwiseIntQuantTensor:
+        if isinstance(qt_args, tuple):
             value, scale, zero_point, bit_width = qt_args
             out = GroupwiseIntQuantTensor(
                 value,
@@ -40,7 +40,8 @@ def create_quant_tensor(
                 self.group_dim,
                 bit_width,
                 self.is_signed,
-                self.training)
+                self.training,
+                x.shape)
         else:
             out = GroupwiseIntQuantTensor(
                 qt_args,
@@ -50,5 +51,6 @@ def create_quant_tensor(
                 self.group_dim,
                 x.bit_width,
                 x.signed,
-                self.training)
+                self.training,
+                x.shape)
         return out

src/brevitas/proxy/parameter_quant.py

@@ -157,7 +157,7 @@ def forward(self, x: torch.Tensor) -> Union[Tensor, QuantTensor]:
                             out.detach(),
                             metadata_only=self.cache_inference_quant_weight_metadata_only)
         else:  # quantization disabled
-            out = self.apply_input_view(x)
+            out = x
         return out
 
 

src/brevitas/proxy/runtime_quant.py

@@ -157,9 +157,8 @@ def init_tensor_quant(self):
 
     @abstractmethod
     def create_quant_tensor(
-            self,
-            qt_args: Union[torch.Tensor, Tuple[Any]],
-            x: Optional[QuantTensor] = None) -> QuantTensor:
+            self, qt_args: Union[torch.Tensor, Tuple[Any]], x: Union[Tensor,
+                                                                     QuantTensor]) -> QuantTensor:
         # Supports the following:
         # - qt_args as tuple of Tensors and bools = standard quant activations
         # - qt_args as Tensor and x as QuantTensor = passthrough activation
@@ -181,8 +180,7 @@ def forward(self, x: Union[Tensor, QuantTensor]) -> Union[Tensor, QuantTensor]:
         elif not self.is_quant_enabled:
             # A tuple helps later with control flows
             # The second None value is used later
-            # If quant is not enabled, we still apply input_view in the case of groupwise + padding
-            y = self.apply_input_view(self.fused_activation_quant_proxy.activation_impl(y))
+            y = self.fused_activation_quant_proxy.activation_impl(y)
             y = (y, None)
         else:
             y = self.fused_activation_quant_proxy(y)
@@ -194,7 +192,7 @@ def forward(self, x: Union[Tensor, QuantTensor]) -> Union[Tensor, QuantTensor]:
         else:
             # If the second value (i.e., scale) is None, then quant is disabled
             if y[1] is not None:
-                out = self.create_quant_tensor(y)
+                out = self.create_quant_tensor(y, x=x)
             elif self.is_passthrough_act and isinstance(x, QuantTensor):
                 # preserve scale/zp/bit/sign even without output quant
                 y = y[0]
@@ -224,11 +222,9 @@ def bit_width(self, force_eval=True):
         return self.retrieve_attribute('bit_width', force_eval)
 
     def create_quant_tensor(
-            self,
-            qt_args: Union[Tensor, Tuple[Any]],
-            x: Optional[IntQuantTensor] = None) -> IntQuantTensor:
-
-        if x is None:
+            self, qt_args: Union[torch.Tensor, Tuple[Any]],
+            x: Union[Tensor, IntQuantTensor]) -> IntQuantTensor:
+        if isinstance(qt_args, tuple):
             out = IntQuantTensor(*qt_args, self.is_signed, self.training)
         else:
             out = IntQuantTensor(

src/brevitas/quant_tensor/base_quant_tensor.py

@@ -1,4 +1,4 @@
-from typing import List, NamedTuple, Optional
+from typing import List, NamedTuple, Optional, Tuple
 
 from torch import Tensor
 
@@ -129,6 +129,7 @@ class GroupwiseFloatQuantTensorBase(NamedTuple):
     nan_values: List[str]
     signed_t: Tensor
     training_t: Tensor
+    dequant_shape: Optional[Tuple] = None
 
 
 class GroupwisIntQuantTensorBase(NamedTuple):
@@ -140,6 +141,7 @@ class GroupwisIntQuantTensorBase(NamedTuple):
     bit_width: Tensor
     signed_t: Tensor
     training_t: Tensor
+    dequant_shape: Optional[Tuple] = None
 
 
 def _unpack_quant_tensor(input_data):

src/brevitas/quant_tensor/groupwise_float_quant_tensor.py

@@ -31,7 +31,8 @@ def __new__(
             inf_values,
             nan_values,
             signed,
-            training):
+            training,
+            dequant_shape=None):
 
         if not isinstance(scale, torch.Tensor):
             scale = torch.tensor(scale, dtype=torch.float)
@@ -63,7 +64,8 @@ def __new__(
             inf_values,
             nan_values,
             signed,
-            training)
+            training,
+            dequant_shape)
         return quant_tensor
 
     @property
@@ -89,19 +91,9 @@ def __torch_function__(self, func, types, args=(), kwargs=None):
             return func(*args, **kwargs)
 
     def expand(self):
-        curr_shape = self.value_.shape
-        start_dim = self.group_dim if self.group_dim != -1 else -2
-        new_value = self.value_.flatten(start_dim, start_dim + 1)
-        if self.scale_.shape != ():
-            new_scale = self.scale_.expand(curr_shape).flatten(start_dim, start_dim + 1)
-        else:
-            new_scale = self.scale_
-        if self.zero_point_.shape != ():
-            new_zp = self.zero_point_.expand(curr_shape).flatten(start_dim, start_dim + 1)
-        else:
-            new_zp = self.zero_point_
-
-        return new_value, new_scale, new_zp
+        from brevitas.utils.quant_utils import groupwise_dequant_expand
+        return groupwise_dequant_expand(
+            self.value_, self.scale_, self.zero_point_, self.group_dim, self.dequant_shape)
 
     @staticmethod
     def from_expanded(value, group_size, group_dim, compress=False):

src/brevitas/quant_tensor/groupwise_int_quant_tensor.py

@@ -18,7 +18,17 @@
 
 class GroupwiseIntQuantTensor(GroupwisIntQuantTensorBase, QuantTensor):
 
-    def __new__(cls, value, scale, zero_point, group_size, group_dim, bit_width, signed, training):
+    def __new__(
+            cls,
+            value,
+            scale,
+            zero_point,
+            group_size,
+            group_dim,
+            bit_width,
+            signed,
+            training,
+            dequant_shape=None):
 
         if not isinstance(scale, torch.Tensor):
             scale = torch.tensor(scale, dtype=torch.float)
@@ -31,7 +41,16 @@ def __new__(cls, value, scale, zero_point, group_size, group_dim, bit_width, sig
         if not isinstance(training, torch.Tensor):
             training = torch.tensor(training, dtype=torch.bool)
         quant_tensor = super().__new__(
-            cls, value, scale, zero_point, group_size, group_dim, bit_width, signed, training)
+            cls,
+            value,
+            scale,
+            zero_point,
+            group_size,
+            group_dim,
+            bit_width,
+            signed,
+            training,
+            dequant_shape)
         return quant_tensor
 
     @property
@@ -58,19 +77,9 @@ def __torch_function__(self, func, types, args=(), kwargs=None):
             return func(*args, **kwargs)
 
     def expand(self):
-        curr_shape = self.value_.shape
-        start_dim = self.group_dim if self.group_dim != -1 else -2
-        new_value = self.value_.flatten(start_dim, start_dim + 1)
-        if self.scale_.shape != ():
-            new_scale = self.scale_.expand(curr_shape).flatten(start_dim, start_dim + 1)
-        else:
-            new_scale = self.scale_
-        if self.zero_point_.shape != ():
-            new_zp = self.zero_point_.expand(curr_shape).flatten(start_dim, start_dim + 1)
-        else:
-            new_zp = self.zero_point_
-
-        return new_value, new_scale, new_zp
+        from brevitas.utils.quant_utils import groupwise_dequant_expand
+        return groupwise_dequant_expand(
+            self.value_, self.scale_, self.zero_point_, self.group_dim, self.dequant_shape)
 
     @staticmethod
     def from_expanded(value, group_size, group_dim, compress=False):