MLBEDSW-4892: Fix crash affecting biases without quantization.

Remove quant_values attribute from Tensor class.
It only needs a single values attribute, holding either
quantized or unquantized values as appropriate.

Change-Id: Ie96f80ac58061b6077e0f7048dc60209fdfbcafa
Signed-off-by: James Peet <james.peet@arm.com>

1 files changed, 14 insertions, 21 deletions

diff --git a/ethosu/vela/tensor.py b/ethosu/vela/tensor.py
index 7dbdcddf..677757ca 100644
--- a/ethosu/vela/tensor.py
+++ b/ethosu/vela/tensor.py
@@ -254,20 +254,8 @@ class QuantizationParameters:
         res.quant_max = self.quant_max
         return res
 
-    def dequantize(self, values):
-        if self.zero_point.size == 1 and self.scale_f32.size == 1:
-            # same scale is used for all values
-            res = (values.astype(np.float64) - self.zero_point) * self.scale_f32
-        else:
-            # a different scale is used for different sets of values
-            values_as_float = values.astype(np.float64)
-
-            # this is not compatible with the format of depthwise weights,
-            # where input is at index 3 (Output, Kh, Kw, Input)
-            # return the quantized values
-            return np.ndarray((values_as_float.shape))
-
-        return res
+    def dequantize(self, values) -> np.ndarray:
+        return np.subtract(values, self.zero_point) * self.scale_f32
 
     def is_scaling_equal(self, other: Optional["QuantizationParameters"]) -> bool:
         # quantisation parameter scaling is not equal if 'other' is None because
@@ -300,16 +288,12 @@ def create_const_tensor(
     value_dtype: np.dtype = None,
     purpose: TensorPurpose = TensorPurpose.Unknown,
     quantization: QuantizationParameters = None,
-    quant_value_dtype: np.dtype = None,
 ):
     # Tensor
     const_tensor = Tensor(shape, dtype, name + "_0")
     const_tensor.purpose = purpose
     const_tensor.quantization = quantization
     const_tensor.values = np.array(values, dtype=value_dtype)
-    const_tensor.quant_values = np.frombuffer(
-        const_tensor.values.tobytes(), dtype=np.uint8 if not quant_value_dtype else quant_value_dtype
-    )
     # Operator
     const_op = Operation(Op.Const, name)
     const_op.set_output_tensor(const_tensor)
@@ -349,7 +333,6 @@ class Tensor:
         "ops",
         "consumer_list",
         "values",
-        "quant_values",
         "compressed_values",
         "compressed_values_substream_offsets",
         "mem_area",
@@ -391,8 +374,7 @@ class Tensor:
         self.ops: List[Operation] = []
         self.consumer_list: List[Operation] = []
 
-        self.values: Optional[np.ndarray] = None
-        self.quant_values: Optional[np.ndarray] = None
+        self.values: Optional[np.ndarray] = None  # elements are of type self.dtype
         self.compressed_values: Optional[np.ndarray] = None
         self.compressed_values_substream_offsets: Optional[List] = None
         self.mem_area: MemArea = MemArea.Unknown
@@ -816,6 +798,17 @@ class Tensor:
 
         return (self.dtype.type & BaseType.Int) != 0 and self.quantization.is_valid()
 
+    def get_scalar(self):
+        """
+        return: Unquantized or dequantized scalar value
+        rtype: self.dtype (if unquantized) or float (if dequantized)
+        """
+        assert self.values.size == 1, "get_scalar called on non-scalar tensor"
+        if self.is_quantized():
+            return self.quantization.dequantize(self.values).item(0)
+        else:
+            return self.values.item(0)
+
     def __lt__(self, other: "Tensor") -> bool:
         return self.equivalence_id < other.equivalence_id