fix quantization zero-point generation

* for int8 and uint8 only
* not for int16
* pass all data types to the quantizaion generator functions as some
  operators (all convolutions, fully_connected) may use different types
  for the input and weight tensors
* also allows input type filtering for convolution operators

Change-Id: Iea3d00f03807a8db35b40d4e8988929ec6549b44
Signed-off-by: Les Bell <les.bell@arm.com>

1 files changed, 22 insertions, 22 deletions

diff --git a/verif/tosa_test_gen.py b/verif/tosa_test_gen.py
index 2ccbb0a..c458538 100644
--- a/verif/tosa_test_gen.py
+++ b/verif/tosa_test_gen.py
@@ -55,45 +55,45 @@ class TosaQuantGen:
         pass
 
     @staticmethod
-    def needsQinfo(op, dtype):
-        if dtype == DType.INT8 or dtype == DType.INT16:
-            return True
-        return False
+    def getQinfo(testGen, dtype):
+        if dtype == DType.INT8:
+            return testGen.randInt(-128, 128)
+        if dtype == DType.UINT8:
+            return testGen.randInt(0, 256)
+        return 0
 
     @staticmethod
     def qgUnary(testGen, op, dtype):
         qinfo = ts.TosaSerializerQuantInfo()
-        if TosaQuantGen.needsQinfo(op, dtype):
-            qinfo.UnaryQuantInfo(testGen.randInt(), testGen.randInt())
-        else:
-            qinfo.UnaryQuantInfo(0, 0)
+        qinfo.UnaryQuantInfo(TosaQuantGen.getQinfo(testGen, dtype),
+                TosaQuantGen.getQinfo(testGen, dtype))
         return qinfo
 
     @staticmethod
-    def qgConv(testGen, op, dtype):
+    def qgConv(testGen, op, dtype_or_dtypeList):
         qinfo = ts.TosaSerializerQuantInfo()
-        if TosaQuantGen.needsQinfo(op, dtype):
-            qinfo.ConvQuantInfo(testGen.randInt(), testGen.randInt())
+        if isinstance(dtype_or_dtypeList, list):
+            # a list of [input, weights, accumulator] dtypes
+            dtypeList = dtype_or_dtypeList
         else:
-            qinfo.ConvQuantInfo(0, 0)
+            # an int, [input, weights, accumulator] dtypes are the same
+            dtypeList = [dtype_or_dtypeList] * 3
+        input_zp = TosaQuantGen.getQinfo(testGen, dtypeList[0])
+        weights_zp = TosaQuantGen.getQinfo(testGen, dtypeList[1])
+        qinfo.ConvQuantInfo(input_zp, weights_zp)
         return qinfo
 
     @staticmethod
     def qgMatmul(testGen, op, dtype):
         qinfo = ts.TosaSerializerQuantInfo()
-        if TosaQuantGen.needsQinfo(op, dtype):
-            qinfo.MatMulQuantInfo(testGen.randInt(), testGen.randInt())
-        else:
-            qinfo.MatMulQuantInfo(0, 0)
+        qinfo.MatMulQuantInfo(TosaQuantGen.getQinfo(testGen, dtype),
+                TosaQuantGen.getQinfo(testGen, dtype))
         return qinfo
 
     @staticmethod
     def qgPad(testGen, op, dtype):
         qinfo = ts.TosaSerializerQuantInfo()
-        if TosaQuantGen.needsQinfo(op, dtype):
-            qinfo.PadQuantInfo(testGen.randInt())
-        else:
-            qinfo.PadQuantInfo(0)
+        qinfo.PadQuantInfo(TosaQuantGen.getQinfo(testGen, dtype))
         return qinfo
 
     @staticmethod
@@ -1636,7 +1636,7 @@ class TosaTestGen:
 
                 # Filter tests based on dtype?
                 if dtypeFilter is not None:
-                    if t not in dtypeFilter:
+                    if not (t in dtypeFilter or (isinstance(t, list) and t[0] in dtypeFilter)):
                         continue
 
                 # Create the placeholder and const tensors
@@ -1836,7 +1836,7 @@ class TosaTestGen:
             tens.extend(self.buildConstTensors(shapeList[pCount:], dtypeList[pCount:]))
 
         if qgen is not None:
-            qinfo = qgen(self, op, dtypeList[0])
+            qinfo = qgen(self, op, dtype_or_dtypeList)
         else:
             qinfo = None