anirudh2290 commented on issue #14467: MKL-DNN QuantizedFullyConnectedOp Error URL: https://github.com/apache/incubator-mxnet/issues/14467#issuecomment-475526979 Thanks @ciyongch . Can you please let me know why quantized_fully_connected doesn't handle inferring the data dimension 0 based on the output shape. For example, the following runs fine on fp32: ``` import mxnet as mx qdtype="float32" num_hidden=100 no_bias=False flatten=True x = mx.sym.var("x", dtype=qdtype) qdata = mx.sym.Variable(name='qdata')#, shape=data_shape, dtype=qdtype) qbias = mx.sym.Variable(name='qbias')#, shape=(10, 100), dtype=qdtype) y = mx.sym.exp(x) fc_fp32 = mx.sym.FullyConnected(data=qdata, num_hidden=num_hidden, no_bias=no_bias, flatten=flatten) sum_first = mx.sym.elemwise_add(y, fc_fp32) sum_first_1 = mx.sym.Group([sum_first, x, y]) ex = sum_first_1.simple_bind(mx.cpu(), qdata=(0, 1024), fullconnected0_weight=(100, 1024), fullyconnected0_bias=(100,), x=(10, 100)) print(ex.arg_dict["qdata"].shape) ``` Expectation is after quantization also it should run fine. But it fails at this check. Is there any reason why we cant remove the check here: https://github.com/apache/incubator-mxnet/blob/master/src/operator/quantization/quantized_fully_connected.cc#L50 and add a inference from output to input like in non quantized fully connected here: https://github.com/apache/incubator-mxnet/blob/master/src/operator/nn/fully_connected.cc#L78
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