Fix size mismatch of placeholders for broadcast fuzzing

Signed-off-by: Won Jeon <won.jeon@arm.com>
Change-Id: Ib954db4f8f2fd5008e95d5651a6e84a8f5b7161d

2 files changed, 13 insertions, 17 deletions

diff --git a/verif/frameworks/tensor_gen.py b/verif/frameworks/tensor_gen.py
index d50bc74..d0c0a0b 100644
--- a/verif/frameworks/tensor_gen.py
+++ b/verif/frameworks/tensor_gen.py
@@ -91,7 +91,7 @@ class TGen:
         return tf_placeholders, tf_consts
 
     @staticmethod
-    def tgBFuzz(op, shape, dtype, rng, fuzzed=[]):
+    def tgBFuzz(op, shape, dtype, rng, for_tflite_converter=True):
         # Build random tensor placeholder node args of a given shape, optionally
         # fuzzing the arguments with random 1's to force broadcasting
 
@@ -99,22 +99,23 @@ class TGen:
 
         assert const == 0
 
-        fuzz_arg = rng.integers(0, pl + const)
-        fuzz_idx = rng.integers(0, len(shape))
+        if not for_tflite_converter:
+            fuzz_arg = rng.integers(0, pl + const)
+            fuzz_idx = rng.integers(0, len(shape))
 
         tf_placeholders = []
         tf_consts = []
+
         for i in range(pl):
-            if not fuzzed and i == fuzz_arg:
+            if not for_tflite_converter and i == fuzz_arg:
                 # Insert the broadcast in one dimension index
                 s_fuzz = list(shape)
                 s_fuzz[fuzz_idx] = 1
                 s_fuzz = tuple(s_fuzz)
                 i_shape = s_fuzz
-                # Record the fuzzed index.
-                fuzzed.append(i)
             else:
                 i_shape = shape
+
             tf_placeholders.append(
                 ("placeholder_{}".format(i), TGen.getRand(i_shape, dtype, rng))
             )
diff --git a/verif/frameworks/tosa_verif_framework_generator.py b/verif/frameworks/tosa_verif_framework_generator.py
index 09a06b4..124bf6e 100755
--- a/verif/frameworks/tosa_verif_framework_generator.py
+++ b/verif/frameworks/tosa_verif_framework_generator.py
@@ -960,13 +960,10 @@ def run_unit_test(
         # Get and seed a random number generator for this test
         rng = np.random.default_rng(seed)
 
-        # For broadcast fuzzing, record the fuzzed index if fuzzing is already done.
-        fuzzed = []
-
         # return placeholders=(str: name, np.array: value)
         # consts=(str: name, np.array: value)
         placeholders, consts = (
-            tensor_gen_fcn(op, curr_shape, dtype, rng, fuzzed)
+            tensor_gen_fcn(op, curr_shape, dtype, rng, False)
             if tensor_gen_fcn.__name__ == "tgBFuzz"
             else tensor_gen_fcn(op, curr_shape, dtype, rng)
         )
@@ -1122,12 +1119,10 @@ def run_unit_test(
                     max_val = float(qmax - qzero[idx]) * scale
                 else:
                     scale = (max_val - min_val) / float(qmax - qmin)
-                    zeropoint = -int(round((-min_val) / scale)) + qmin
-
-                # Exit if min_val <= 0.0, in order to avoid assertion error
-                # from tf.quantization.fake_quant_with_min_max_args
-                if min_val > 0.0:
-                    return True
+                    if op_name == "squared_difference":
+                        zeropoint = -int(round((-min_val) / scale)) + qmin
+                    else:
+                        zeropoint = int(round((-min_val) / scale)) + qmin
 
                 # run through tf.fakequant first to assure quantization error aligned
                 fakequant_val = tf.quantization.fake_quant_with_min_max_args(
@@ -1177,7 +1172,7 @@ def run_unit_test(
             def input_stats():
                 for i in range(0, args.num_samples):
                     placeholders, _ = (
-                        tensor_gen_fcn(op, placeholder_shapes[0], dtype, rng, fuzzed)
+                        tensor_gen_fcn(op, placeholder_shapes[0], dtype, rng, True)
                         if tensor_gen_fcn == "tgBFuzz"
                         else tensor_gen_fcn(op, placeholder_shapes[0], dtype, rng)
                     )