MLBEDSW-6909: Use int32 acc for the Mean op
Changed acc type from int16 to int32. This will solve
saturation problems and the constraint added in
commit "MLBEDSW-5029: Output diff for Mean op"
can be removed.
Signed-off-by: Johan Alfven <johan.alfven@arm.com>
Change-Id: I05ec8835b43313b1a264d61a2b147fa62da123fe
diff --git a/ethosu/vela/tflite_graph_optimiser.py b/ethosu/vela/tflite_graph_optimiser.py
index aaa778e..0f199de 100644
--- a/ethosu/vela/tflite_graph_optimiser.py
+++ b/ethosu/vela/tflite_graph_optimiser.py
@@ -1476,9 +1476,10 @@
# followed by a multiplication with 1/N to get the MEAN
weight_scale = 1
intermediate = op.ofm.clone(suffix="_intermediate", set_unique=True)
- intermediate.dtype = DataType.int16
+ intermediate.dtype = DataType.int32
mul_op = Operation(Op.Mul, op.name + "_mul")
mul_op.add_input_tensor(intermediate)
+ mul_op.set_output_tensor(op.ofm)
# Create scalar containing 1/N
quant = QuantizationParameters()
quant.zero_point = 0
@@ -1492,11 +1493,23 @@
n = int(h * w)
eps = 1 / (256 * (n + 1)) if n % 2 == 0 else 0
quant.scale_f32 = 1 / (n - eps)
+
+ # For int8/int16 we could use IFM/OFM scaling to do the division
+ # intermediate * 1 -> scale > round and shift.
+ #
+ # For int32 scaling is not supported so instead multiply with the scale
+ # intermediate * scale -> round and shift.
+ #
+ # Calculate the scale and shift value. const Tensor must be created
+ # with correct quantization since the scale and shift is calculated later
+ # in the command stream generator.
+ mul_scale, _ = scaling.elementwise_mul_scale(
+ mul_op.ifm.quantization.scale_f32, quant.scale_f32, mul_op.ofm.quantization.scale_f32
+ )
scalar = create_const_tensor(
- op.name + "_scalar", [1, 1, 1, 1], DataType.uint8, [1], np.uint8, quantization=quant
+ op.name + "_scalar", [1, 1, 1, 1], DataType.int32, [mul_scale], np.int32, quantization=quant
)
mul_op.add_input_tensor(scalar)
- mul_op.set_output_tensor(op.ofm)
mul_op.set_ifm_ofm_shapes()
mul_op.rounding_mode = NpuRoundingMode.NATURAL
mul_op.activation = op.activation