Validation checker

Xilinx · Jul 7, 2023 · b136503 · b136503
1 parent b57f994
commit b136503
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Showing 2 changed files with 30 additions and 17 deletions.
diff --git a/src/brevitas_examples/imagenet_classification/ptq/benchmark/post_processing.py b/src/brevitas_examples/imagenet_classification/ptq/benchmark/post_processing.py
@@ -27,7 +27,7 @@ def main():
         'Weight bit width',
         'Act bit width',
         'Bias bit width',
-        'Scaling per output channel',
+        'Weight quant granularity',
         'Act quant type'])
     idx = grouped_df['Top 1% quant accuracy'].transform(max) == df['Top 1% quant accuracy']
     best_config_df = df[idx]

diff --git a/src/brevitas_examples/imagenet_classification/ptq/benchmark/ptq_benchmark_torchvision.py b/src/brevitas_examples/imagenet_classification/ptq/benchmark/ptq_benchmark_torchvision.py
@@ -142,23 +142,10 @@ def ptq_torchvision_models(df, args):
     for key, value in zip(OPTIONS.keys(), combination):
         setattr(config_namespace, key, value)
 
-    # Flexml supports only per-tensor scale factors, power of two scale factors
-    if config_namespace.target_backend == 'flexml' and (
-            config_namespace.weight_quant_granularity == 'per_channel' or
-            config_namespace.scale_factor_type == 'float32'):
-        return
-    # Merge bias can be enabled only when graph equalization is enabled
-    if config_namespace.graph_eq_iterations == 0 and config_namespace.graph_eq_merge_bias:
-        return
-    # For generic and layerwise backend, we only test for int32 bias bit width
-    if (config_namespace.target_backend == 'generic' or config_namespace.target_backend
-            == 'layerwise') and config_namespace.bias_bit_width == 'int16':
-        return
-    # If GPTQ is disabled, we do not care about the act_order heuristic
-    if not config_namespace.gptq and config_namespace.gptq_act_order:
-        return
-    if config_namespace.act_param_method == 'mse' and config_namespace.act_quant_percentile != 99.999:
+    if not validate_config(config_namespace):
         return
+    if config_namespace.act_param_method == 'mse' and config_namespace.act_quant_percentile == 99.999:
+        config_namespace.act_quant_percentile = None
 
     fp_accuracy = TORCHVISION_TOP1_MAP[config_namespace.model_name]
     # Get model-specific configurations about input shapes and normalization
@@ -273,5 +260,31 @@ def ptq_torchvision_models(df, args):
     torchvision_df.to_csv(os.path.join(folder, 'RESULTS_TORCHVISION.csv'), index=False)
 
 
+def validate_config(config_namespace):
+    is_valid = True
+    # Flexml supports only per-tensor scale factors, power of two scale factors
+    if config_namespace.target_backend == 'flexml' and (
+            config_namespace.weight_quant_granularity == 'per_channel' or
+            config_namespace.scale_factor_type == 'float32'):
+        is_valid = False
+    # Merge bias can be enabled only when graph equalization is enabled
+    if config_namespace.graph_eq_iterations == 0 and config_namespace.graph_eq_merge_bias:
+        is_valid = False
+    # For generic and layerwise backend, we only test for int32 bias bit width
+    if (config_namespace.target_backend == 'generic' or config_namespace.target_backend
+            == 'layerwise') and config_namespace.bias_bit_width == 'int16':
+        is_valid = False
+    # If GPTQ is disabled, we do not care about the act_order heuristic
+    if not config_namespace.gptq and config_namespace.gptq_act_order:
+        is_valid = False
+    if config_namespace.act_param_method == 'mse' and config_namespace.act_quant_percentile != 99.999:
+        is_valid = False
+    if config_namespace.act_equalization == 'layerwise' and config_namespace.target_backend == 'generic':
+        is_valid = False
+    if config_namespace.act_bit_width < config_namespace.weight_bit_width:
+        is_valid = False
+    return is_valid
+
+
 if __name__ == '__main__':
     main()