Feat (core): use runtime parameter for scale

src/brevitas/core/scaling/runtime.py

@@ -60,8 +60,10 @@ def __init__(
 
     @brevitas.jit.script_method
     def forward(
-            self, ignored: torch.Tensor, threshold: Optional[torch.Tensor] = None) -> torch.Tensor:
-        stats = self.parameter_list_stats()
+            self,
+            x: Optional[torch.Tensor],
+            threshold: Optional[torch.Tensor] = None) -> torch.Tensor:
+        stats = self.parameter_list_stats(x)
         if threshold is None:
             threshold = torch.ones(1).type_as(stats)
         return self.stats_scaling_impl(stats, threshold)

src/brevitas/core/scaling/standalone.py

@@ -241,17 +241,17 @@ def __init__(
         self.value = Parameter(torch.full(scaling_shape, 1.0, dtype=dtype, device=device))
 
     @brevitas.jit.script_method
-    def forward(self, ignored: Tensor, threshold: Optional[Tensor] = None) -> Tensor:
+    def forward(self, x: Tensor, threshold: Optional[Tensor] = None) -> Tensor:
         if threshold is None:
-            threshold = torch.ones(1).type_as(ignored)
+            threshold = torch.ones(1).type_as(x)
         if self.init_done:
             threshold = self.stats_scaling_impl.restrict_clamp_threshold(
                 self.restrict_threshold_pre(threshold))
             value = abs_binary_sign_grad(self.stats_scaling_impl.restrict_clamp_scaling(self.value))
             value = value / threshold
             return value
         else:
-            stats = self.parameter_list_stats()
+            stats = self.parameter_list_stats(x)
             # workaround to avoid find_ununsed_parameter=True in DDP
             stats = stats + 0. * self.value
             if self.local_loss_mode:

src/brevitas/core/stats/stats_wrapper.py

@@ -13,6 +13,7 @@
 from brevitas.core.utils import inplace_tensor_mul
 
 from .view_wrapper import _ViewCatParameterWrapper
+from .view_wrapper import _ViewParameter
 from .view_wrapper import _ViewParameterWrapper
 
 DEFAULT_MOMENTUM = 0.1
@@ -96,8 +97,12 @@ def __init__(
         super(_ParameterListStats, self).__init__()
 
         self.stats_input_concat_dim = stats_input_concat_dim
-        self.first_tracked_param = _ViewParameterWrapper(
-            tracked_parameter_list[0], stats_input_view_shape_impl)
+        if len(tracked_parameter_list) >= 1:
+            self.first_tracked_param = _ViewParameterWrapper(
+                tracked_parameter_list[0], stats_input_view_shape_impl)
+        else:
+            self.first_tracked_param = _ViewParameter(stats_input_view_shape_impl)
+
         if len(tracked_parameter_list) > 1:
             extra_list = [
                 _ViewCatParameterWrapper(
@@ -109,10 +114,12 @@ def __init__(
         self.stats = _Stats(stats_impl, stats_output_shape)
 
     @brevitas.jit.script_method
-    def forward(self) -> torch.Tensor:
-        stats_input = self.first_tracked_param()
+    def forward(self, x: Optional[torch.Tensor] = None) -> torch.Tensor:
         if self.extra_tracked_params_list is not None:
+            stats_input = self.first_tracked_param(None)
             for extra_tracked_param in self.extra_tracked_params_list:
                 stats_input = extra_tracked_param(stats_input)
+        else:
+            stats_input = self.first_tracked_param(x)
         out = self.stats(stats_input)
         return out

src/brevitas/core/stats/view_wrapper.py

@@ -1,6 +1,8 @@
 # Copyright (C) 2023, Advanced Micro Devices, Inc. All rights reserved.
 # SPDX-License-Identifier: BSD-3-Clause
 
+from typing import Optional
+
 import torch
 from torch import Tensor
 from torch.nn import Module
@@ -19,8 +21,12 @@ def __init__(self, parameter: Parameter, view_shape_impl: Module) -> None:
         self.view_shape_impl = view_shape_impl
 
     @brevitas.jit.script_method
-    def forward(self) -> Tensor:
-        return self.view_shape_impl(self.parameter)
+    def forward(self, x: Optional[Tensor]) -> Tensor:
+        if x is not None:
+            parameter = x
+        else:
+            parameter = self.parameter
+        return self.view_shape_impl(parameter)
 
     def _load_from_state_dict(
             self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys,
@@ -39,6 +45,17 @@ def state_dict(self, destination=None, prefix='', keep_vars=False):
         return output_dict
 
 
+class _ViewParameter(brevitas.jit.ScriptModule):
+
+    def __init__(self, view_shape_impl: Module) -> None:
+        super(_ViewParameter, self).__init__()
+        self.view_shape_impl = view_shape_impl
+
+    @brevitas.jit.script_method
+    def forward(self, x: Tensor) -> Tensor:
+        return self.view_shape_impl(x)
+
+
 class _ViewCatParameterWrapper(brevitas.jit.ScriptModule):
     __constants__ = ['cat_dim']
 

src/brevitas/core/zero_point.py

@@ -82,7 +82,7 @@ def __init__(
 
     @brevitas.jit.script_method
     def forward(self, x: Tensor, scale: Tensor, bit_width: Tensor) -> torch.Tensor:
-        stats = self.parameter_list_stats()
+        stats = self.parameter_list_stats(x)
         return self.scale_shift_zero_point(-stats, scale, bit_width)
 
 
@@ -266,7 +266,7 @@ def forward(self, x: Tensor, scale: Tensor, bit_width: Tensor) -> torch.Tensor:
             value = self.scale_shift_zero_point(value, scale, bit_width)
             return value
         else:
-            stats = self.parameter_list_stats()
+            stats = self.parameter_list_stats(x)
             # workaround to avoid find_ununsed_parameter=True in DDP
             stats = stats + 0. * self.value
             if self.local_loss_mode:

src/brevitas_examples/llm/main.py

@@ -285,6 +285,8 @@ def main(args):
 
     model = offload_model(model)
 
+    model(**calibration_loader[0])
+
     if args.act_calibration:
         print("Apply act calibration...")
         apply_calibration(model, calibration_loader)