[WIP][QNN] Quantized fully connected #3597
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Requantize converts one quantized tensor representation to another quantized
representation. The PR has following implementation features
- Requantize operator defined in qnn namespace - relay.qnn.requantize
- Lowering of the requantize to exisiting Relay operators
- Integer fixed point implementation of requantize
- Two rounding modes - FE_UPWARDS (round towards infinity) and
FE_AWAY_FROM_ZERO (std::round behavior)
- Floating point implementation as well, that can act as reference or can be
used for devices when FP32 computation is not used.
- Unit test cases
Relevant Issue - apache#2351
Credit to TFLite and GemmLowp to provide reference implementations.
Requantize converts one quantized tensor representation to another quantized
representation. The PR has following implementation features
- Requantize operator defined in qnn namespace - relay.qnn.requantize
- Lowering of the requantize to exisiting Relay operators
- Integer fixed point implementation of requantize
- Two rounding modes - FE_UPWARDS (round towards infinity) and
FE_AWAY_FROM_ZERO (std::round behavior)
- Floating point implementation as well, that can act as reference or can be
used for devices when FP32 computation is not used.
- Unit test cases
Relevant Issue - apache#2351
Credit to TFLite and GemmLowp to provide reference implementations.
# Conflicts: # include/tvm/relay/qnn/attrs.h # python/tvm/relay/qnn/op/qnn.py # src/relay/qnn/pass/qnn_lower.cc # src/relay/qnn/util.h
# Conflicts: # python/tvm/relay/qnn/op/qnn.py
# Conflicts: # src/relay/qnn/pass/qnn_lower.cc # src/relay/qnn/util.h
Contributor
Author
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There is dependency on #3779. Once it is merged the test cases will pass. will wait for it, |
Contributor
Author
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Another PR has been merged with these changes. |
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This PR is the implementation of pre-quantized fully connected op.