Enforce no output rewrite REQUIRE in SCATTER

Signed-off-by: Jeremy Johnson <jeremy.johnson@arm.com>
Change-Id: I3555e7216d403d436bf6e39d4b16bb000645c4bb

2 files changed, 21 insertions, 15 deletions

diff --git a/verif/generator/tosa_arg_gen.py b/verif/generator/tosa_arg_gen.py
index 193da73..35253e0 100644
--- a/verif/generator/tosa_arg_gen.py
+++ b/verif/generator/tosa_arg_gen.py
@@ -213,17 +213,17 @@ class TosaTensorGen:
             assert rank == 3
 
         values_in_shape = testGen.makeShape(rank)
+        K = values_in_shape[1]
 
         # ignore max batch size if target shape is set
         if testGen.args.max_batch_size and not testGen.args.target_shapes:
             values_in_shape[0] = min(values_in_shape[0], testGen.args.max_batch_size)
 
-        W = testGen.randInt(
-            testGen.args.tensor_shape_range[0], testGen.args.tensor_shape_range[1]
-        )
-        # Constrict W if one dimension is too large to keep tensor size reasonable
-        if max(values_in_shape) > 5000:
-            W = testGen.randInt(0, 16)
+        # Make sure W is not greater than K, as we can only write each output index
+        # once (having a W greater than K means that you have to repeat a K index)
+        W_min = min(testGen.args.tensor_shape_range[0], K)
+        W_max = min(testGen.args.tensor_shape_range[1], K)
+        W = testGen.randInt(W_min, W_max) if W_min < W_max else W_min
 
         input_shape = [values_in_shape[0], W, values_in_shape[2]]
 
diff --git a/verif/generator/tosa_test_gen.py b/verif/generator/tosa_test_gen.py
index ba10dcf..53b0b75 100644
--- a/verif/generator/tosa_test_gen.py
+++ b/verif/generator/tosa_test_gen.py
@@ -1771,22 +1771,28 @@ class TosaTestGen:
 
     def build_scatter(self, op, values_in, input, validator_fcns=None, error_name=None):
 
-        # Create a new indicies tensor
-        # here with data that doesn't exceed the dimensions of the values_in tensor
-
         K = values_in.shape[1]  # K
         W = input.shape[1]  # W
-        indicies_arr = np.int32(
-            self.rng.integers(low=0, high=K, size=[values_in.shape[0], W])
-        )  # (N, W)
-        indicies = self.ser.addConst(indicies_arr.shape, DType.INT32, indicies_arr)
+
+        # Create an indices tensor here with data that doesn't exceed the
+        # dimension K of the values_in tensor and does NOT repeat the same K
+        # location as needed by the spec:
+        # "It is not permitted to repeat the same output index within a single
+        # SCATTER operation and so each output index occurs at most once."
+        assert K >= W
+        arr = []
+        for n in range(values_in.shape[0]):
+            # Get a shuffled list of output indices and limit it to size W
+            arr.append(self.rng.permutation(K)[:W])
+        indices_arr = np.array(arr, dtype=np.int32)  # (N, W)
+        indices = self.ser.addConst(indices_arr.shape, DType.INT32, indices_arr)
 
         result_tens = OutputShaper.scatterOp(
-            self.ser, self.rng, values_in, indicies, input, error_name
+            self.ser, self.rng, values_in, indices, input, error_name
         )
 
         # Invalidate Input/Output list for error if checks.
-        input_list = [values_in.name, indicies.name, input.name]
+        input_list = [values_in.name, indices.name, input.name]
         output_list = [result_tens.name]
         pCount, cCount = op["operands"]
         num_operands = pCount + cCount