> @FrozenGene For the output_min and max, isn't the out_dtype enough? If its
> uint8, we can clamp at 0 and 255. If its int8, we can clamp at -128 and 127.
> I don't see any reason the values will be any different, unless you want to
> fuse the quantized relu in the quantized convolution from th
> > It appears to me this would let them simulate smaller than 8 bit
> > quantizations.
>
> If _simulating 8 smaller bit_ is the case, 8 bit should be able to hold
> activation min/max value.
8 bits could hold. But what the value output_min / output_max is ? I think
@jnorwood want to express t
> @FrozenGene a clarifying question to your above comment. If we pass in the
> output scale and shift can we not compute int32-> int8 by simply adding more
> nodes in the graph.
doesn't understand your comment fully. do you mean could we avoid int32 -> int8
computation? If so, I think we can no
> It appears to me this would let them simulate smaller than 8 bit quantizations
If *simulating 8 smaller bit* is the case, 8 bit should be able to hold
activation min/max value.
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The tflite quantized convolution reference implementation passes in both limits
as int32 values. It appears to me this would let them simulate smaller than 8
bit quantizations, if that is something you want to support.
this is from ` tensorflow/lite/kernels/internal/reference/conv.h `
`
acc
@FrozenGene For the output_min and max, isn't the out_dtype enough? If its
uint8, we can clamp at 0 and 255. If its int8, we can clamp at -128 and 127. I
don't see any reason the values will be any different, unless you want to fuse
the quantized relu in the quantized convolution from the starti
@FrozenGene a clarifying question to your above comment. If we pass in the
output scale and shift can we not compute int32-> int8 by simply adding more
nodes in the graph.
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htt
@anijain2305 I understand your thought and thought. I agree we should make the
api minimal. However, no matter what way, q_conv2d’s int32 output should be
clamped into uint8 range. If you don’t pass min / max, you also need do `output
= std::max(output, 0)` and `output = std::min(output, 255)` t
@tqchen One thing I wanted to clarify: why isn't the Analyzer class integrated
into the Node hierarchy? Instead some separate closure-based mechanism is used
for python integration, which feels strange and seemingly makes it harder to
create functions which accept Analyzer objects and work acros
