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| author | Johan Alfvén <johan.alfven@arm.com> | 2022-09-06 20:33:22 +0200 |
|---|---|---|
| committer | Fredrik Svedberg <fredrik.svedberg@arm.com> | 2022-09-12 08:02:11 +0000 |
| commit | 059166304f9ef47f0b916c1325700ed826f25581 (patch) | |
| tree | 49200e8bc2014e2cdbae52ba49a9e9b5b2ed776f /ethosu/vela/tflite_graph_optimiser.py | |
| parent | d6efcd3cdcd8295fbbe7dd47a7074be39eaf03e4 (diff) | |
| download | ethos-u-vela-059166304f9ef47f0b916c1325700ed826f25581.tar.gz | |
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
Diffstat (limited to 'ethosu/vela/tflite_graph_optimiser.py')
| -rw-r--r-- | ethosu/vela/tflite_graph_optimiser.py | 19 |
1 files changed, 16 insertions, 3 deletions
diff --git a/ethosu/vela/tflite_graph_optimiser.py b/ethosu/vela/tflite_graph_optimiser.py index aaa778ed..0f199de0 100644 --- a/ethosu/vela/tflite_graph_optimiser.py +++ b/ethosu/vela/tflite_graph_optimiser.py @@ -1476,9 +1476,10 @@ def convert_mean_to_depthwise_conv_or_avgpool(op, arch, nng): # 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 @@ def convert_mean_to_depthwise_conv_or_avgpool(op, arch, nng): 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 |