FrozenGene commented on issue #4828: [QNN][TFLite] TFLite rounding mode support URL: https://github.com/apache/incubator-tvm/pull/4828#issuecomment-595200848 > The problem is located and solved. The discrepancy comes from average pooling calculation when input is integral type. tflite does a UPWARD rounding after the division in avg_pool but current topi implementation of avg_pool does of FLOOR after the division. > > The fix is added and tested locally on cpu. Now the qnn tests in tflite test_forward has rtol and atol set to 0, which should meet the bit-exact exectution requirement. > > @FrozenGene @anijain2305 @tqchen @u99127 Please have another look, thanks! Thanks @LiangHao151941 ! Maybe we have to handle it a bit more. Please see: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/kernels/internal/optimized/integer_ops/pooling.h#L300 The default behaviour of TFLite is `optimize`, so TFLite will run `optimzed` of `kernel` directory. For average pool, it has two conditions. The type of `acc` will be `uint16` if `params.filter_height * params.filter_width <= 16 * 16` , otherwise, the type of `acc` will be `int32` as our implementation. If we want to keep bit-exact result, we should handle it here too. You remind me this situation, I met this bug before if we don't handle it. > Another way is we provide rounding args for qnn.add / qnn.mul / qnn.conconcate, because they use requantize in fact too, so they need rounding. We should handle these ops too.
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