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| author | Dwight Lidman <dwight.lidman@arm.com> | 2021-03-18 14:51:42 +0100 |
|---|---|---|
| committer | patrik.gustavsson <patrik.gustavsson@arm.com> | 2021-03-26 10:07:24 +0000 |
| commit | 9bb1e2ed361286769c362f002910d1dbd1736b05 (patch) | |
| tree | cf16840abfe8c5494678d5ff824d37eb7417c8e9 /ethosu/vela/graph_optimiser.py | |
| parent | c629129f79666eeea1d86a779c8cb245e052672f (diff) | |
| download | ethos-u-vela-9bb1e2ed361286769c362f002910d1dbd1736b05.tar.gz | |
MLBEDSW-4163: OFM zero point outside valid range
This commit fixes a bug where the OFM zero point
would assume values outside of [0, 255] due to it's
usage as a stand-in for a bias when emulating the
TensorFlow Lite implementation of MEAN.
The solution is to adjust for the bias using an
ADD operator with the bias value as an int16 const
tensor. The 16-bit integer is needed as the bias
is 32 bits in the original implementation but can
effectively assume values in the range [-255, 255].
Signed-off-by: Dwight Lidman <dwight.lidman@arm.com>
Change-Id: I84df48ea89bb559954f1b2c289b65e08a6418274
Diffstat (limited to 'ethosu/vela/graph_optimiser.py')
| -rw-r--r-- | ethosu/vela/graph_optimiser.py | 38 |
1 files changed, 36 insertions, 2 deletions
diff --git a/ethosu/vela/graph_optimiser.py b/ethosu/vela/graph_optimiser.py index e8218fcc..bea22a23 100644 --- a/ethosu/vela/graph_optimiser.py +++ b/ethosu/vela/graph_optimiser.py @@ -1435,12 +1435,46 @@ def convert_mean_to_depthwise_conv(op, arch, nng): # This attribute means a different scaling calculation is used in order to match reference op.low_precision_scaling = True weight_scale = h * w + # Set zero points to 0 as they will be adjusted for with bias term foq = ofmq.clone() - foq.zero_point -= int(np.round(ifmq.zero_point * ifmq.scale_f32 / foq.scale_f32)) - op.forced_output_quantization = foq + foq.zero_point = 0 fiq = ifmq.clone() fiq.zero_point = 0 op.forced_input_quantization = fiq + bias_term = ofmq.zero_point - int(ifmq.zero_point * ifmq.scale_f32 / ofmq.scale_f32) + # If the bias term is outside uint8 range, we need an Add op to apply it. + if bias_term < 0 or bias_term > 255: + intermediate = op.ofm.clone(suffix="_intermediate", set_unique=True) + # Bias term has higher bitness (i32) than input/output (u8). + # 16 bits is enough since the bias is added/subtracted from a u8 value, + # the bias can only effectively assume values in the range [-255, 255]. + intermediate.dtype = DataType.int16 + intermediate.quantization.zero_point = 0 + add_op = Operation(Op.Add, op.name + "_bias") + add_op.forced_output_quantization = foq + add_op.add_input_tensor(intermediate) + quant = QuantizationParameters() + quant.zero_point = 0 + bias_term_tens = create_const_tensor( + op.name + "_bias", + [1, 1, 1, 1], + DataType.int16, + [bias_term], + np.int16, + quantization=quant, + quant_value_dtype=np.int16, + ) + add_op.add_input_tensor(bias_term_tens) + add_op.set_output_tensor(op.ofm) + add_op.set_ifm_ofm_shapes() + add_op.activation = op.activation + op.activation = None + op.set_output_tensor(intermediate) + op.set_ifm_ofm_shapes() + # If not, we can just do it with the OFM zero point. + else: + foq.zero_point = bias_term + op.forced_output_quantization = foq else: assert inp.dtype == DataType.int8 # Use a depthwise to calculate the sum, |