[QNN] Quantize - Fixing the sequence of lowering. #4316
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* [TOPI][OP] Support Faster-RCNN Proposal OP on CPU (apache#4297) * Support Proposal operator on CPU. * PyLint space issue * PyLint space issue * Pylint singleton-comparison issue * [QNN][Legalize] Specialize for Platforms without any fast Int8 arithmetic units. (apache#4307) * fix error when memory_id is VTA_MEM_ID_OUT (apache#4330) * [CI][DOCKER] Add ONNX runtime dep (apache#4314) * [DOCKER] Add ONNX runtime dep * Improve ci script * [QNN] Quantize - Fixing the sequence of lowering. (apache#4316) * [QNN] Use Int16 upcast in Fallback Conv2D. Fix test names. (apache#4329) * [doc][fix] fix sphinx parsing for pass infra tutorial (apache#4337) * change ci image version (apache#4313) * [Codegen] remove fp16 function override for cuda (apache#4331) * add volatile override back * [codegen] remove fp16 function override for cuda * [CI] Set workspace to be per executor (apache#4336) * [Build][Windows] Fix Windows build by including cctype (apache#4319) * Fix build * dummy change to retrigger CI * dummy change to retrigger ci * dummy change to retrigger ci * Enable hipModuleGetGlobal() (apache#4321) * [Relay][Pass] Add pass to remove unused functions in relay module (apache#4334) * [Relay][Pass] Add pass to remove unused functions in relay module * Add tests * Fix lint * Fix visit order * Add pass argument * Fix * Add support for quant. mul operator in tflite frontend (apache#4283) A test for qnn_mul has to be added when the qnn elemwise tests (apache#4282) get merged. * Add topi.nn.fifo_buffer to TVM doc (apache#4343) * Solve custom model of prelu (apache#4326) * Deprecate NNVM warning msg (apache#4333) * [Contrib] Add MKL DNN option (apache#4323) * [Contrib] Add MKL DNN * update * update * [Relay][Frontend][TF] Fix transpose when axes is not a param (apache#4327) * [Relay][Frontend][TF] Use _infer_value_simulated when axes is not a const to Transpose * uncomment tests * dummy change to retrigger ci * [RUNTIME] Add device query for AMD GcnArch (apache#4341) * add gcnArch query * kGcnArch query for cuda is a no-op * [Test][Relay][Pass] Add test case for lambda lift (apache#4317) * [Relay][Frontend][ONNX] operator support: DepthToSpace, SpaceToDepth (apache#4271) * imp module is deprecated (apache#4275) * [VTA] Bug fix for padded load with large inputs (apache#4293) * bug fix for padded load with large inputs * Update TensorLoad.scala * Update test_vta_insn.py * fix inconsistent tag name (apache#4134) * [CodeGen] Add build config option disable_assert to control whether to generate assert (apache#4340) * Bump up CUDA log version in tophub.py (apache#4347) * Add check to ensure input file was successfully opened in NNVM deploy code demo (apache#4315) * [COMMUNITY] Add DISCLAIMER, KEYS for ASF release (apache#4345) * [COMMUNITY] Add DISCLAIMER, KEYS for ASF release * Add file name spec * [Relay][VM][Interpreter] Enable first-class constructors in VM and interpreter via eta expansion (apache#4218) * Fix constructor pretty printing * Make Module::HasDef name consistent with API * Add VM constructor compilation via eta expansion * Lint * Fix CI * Fix failing test * Address comment * Retrigger CI * Retrigger CI * Update dmlc_tvm_commit_id.txt
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Quantize op converts Fp32 tensor to Int8 tensor. This PR delays the rounding and final casting to int as much as possible. Earlier the casting was happening before the addition of zero point.
This will not change the output, as zero points are integers. But this is the right thing to do from maths perspective. No new tests are required for this.
Also, allowing int32 as output type. This is common for quantized bias which is int32 instead of int8.
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