Clean up synthetic weights/inputs generation integration in pulp-nnx

test/NnxTestClasses.py

@@ -48,6 +48,8 @@ class NnxTestConf(BaseModel):
     has_norm_quant: bool
     has_bias: bool
     has_relu: bool
+    synthetic_weights: bool
+    synthetic_inputs: bool
 
     @model_validator(mode="after")  # type: ignore
     def check_valid_depthwise_channels(self) -> NnxTestConf:
@@ -116,6 +118,8 @@ def __init__(
         scale: Optional[torch.Tensor] = None,
         bias: Optional[torch.Tensor] = None,
         global_shift: Optional[torch.Tensor] = torch.Tensor([0]),
+        synthetic_weights: Optional[bool] = False,
+        synthetic_inputs: Optional[bool] = False,
     ) -> None:
         self.conf = conf
         self.input = input
@@ -124,6 +128,8 @@ def __init__(
         self.scale = scale
         self.bias = bias
         self.global_shift = global_shift
+        self.synthetic_weights = synthetic_weights
+        self.synthetic_inputs = synthetic_inputs
 
     def is_valid(self) -> bool:
         return all(
@@ -243,20 +249,30 @@ def from_conf(
         bias_shape = (1, conf.out_channel, 1, 1)
 
         if input is None:
-            input = NnxTestGenerator._random_data(
-                _type=conf.in_type,
-                shape=input_shape,
-            )
+            if conf.synthetic_inputs:
+                inputs = torch.zeros((1, conf.in_channel, conf.in_height, conf.in_width), dtype=torch.int64)
+                for i in range(conf.in_channel):
+                    inputs[:, i,0,0] = i
+            else:
+                input = NnxTestGenerator._random_data(
+                    _type=conf.in_type,
+                    shape=input_shape,
+                )
 
         if weight is None:
-            weight_mean = NnxTestGenerator._DEFAULT_WEIGHT_MEAN
-            weight_std  = NnxTestGenerator._DEFAULT_WEIGHT_STDEV * (1<<(conf.weight_type._bits-1)-1)
-            weight = NnxTestGenerator._random_data_normal(
-                mean = weight_mean,
-                std = weight_std,
-                _type=conf.weight_type,
-                shape=weight_shape,
-            )
+            if conf.synthetic_weights:
+                weight = torch.zeros((conf.out_channel, 1 if conf.depthwise else conf.in_channel, conf.kernel_shape.height, conf.kernel_shape.width), dtype=torch.int64)
+                for i in range(0, min(weight.shape[0], weight.shape[1])):
+                    weight[i,i,0,0] = 1
+            else:
+                weight_mean = NnxTestGenerator._DEFAULT_WEIGHT_MEAN
+                weight_std  = NnxTestGenerator._DEFAULT_WEIGHT_STDEV * (1<<(conf.weight_type._bits-1)-1)
+                weight = NnxTestGenerator._random_data_normal(
+                    mean = weight_mean,
+                    std = weight_std,
+                    _type=conf.weight_type,
+                    shape=weight_shape,
+                )
 
         if conf.has_norm_quant:
             if scale is None:
@@ -306,6 +322,8 @@ def from_conf(
             scale=scale,
             bias=bias,
             global_shift=global_shift,
+            synthetic_inputs=conf.synthetic_inputs,
+            synthetic_weights=conf.synthetic_weights,
         )
 
     @staticmethod
@@ -361,7 +379,10 @@ def generate(self, test_name: str, test: NnxTest):
         weight_type = test.conf.weight_type
         weight_bits = weight_type._bits
         assert weight_bits > 1 and weight_bits <= 8
-        weight_offset = -(2 ** (weight_bits - 1))
+        if test.synthetic_weights:
+            weight_offset = 0
+        else:
+            weight_offset = -(2 ** (weight_bits - 1))
         weight_out_ch, weight_in_ch, weight_ks_h, weight_ks_w = test.weight.shape
         weight_data: np.ndarray = test.weight.numpy() - weight_offset
         weight_init = self.weightEncode(

test/testgen.py

@@ -86,21 +86,7 @@ def test_gen(
         exit(-1)
 
     test_conf = nnxTestConfCls.model_validate(test_conf_dict)
-    if test_conf_dict['synthetic_weights']:
-        import torch
-        weight = torch.zeros((test_conf.out_channel, 1 if test_conf.depthwise else test_conf.in_channel, test_conf.kernel_shape.height, test_conf.kernel_shape.width), dtype=torch.int64)
-        for i in range(0, min(weight.shape[0], weight.shape[1])):
-            weight[i,i,0,0] = 1
-    else:
-        weight = None
-    if test_conf_dict['synthetic_inputs']:
-        import torch
-        inputs = torch.zeros((1, test_conf.in_channel, test_conf.in_height, test_conf.in_width), dtype=torch.int64)
-        for i in range(test_conf.in_channel):
-            inputs[:, i,0,0] = i
-    else:
-        inputs = None
-    test = NnxTestGenerator.from_conf(test_conf, verbose=args.print_tensors, weight=weight, input=inputs)
+    test = NnxTestGenerator.from_conf(test_conf, verbose=args.print_tensors)
     if not args.skip_save:
         test.save(args.test_dir)
     if args.headers: