anirudh2290 opened a new issue #15032: FC with bias ndim > 1 fails with MKLDNN URL: https://github.com/apache/incubator-mxnet/issues/15032 Minimum reproducible example: ``` import numpy as np import mxnet as mx from mxnet.test_utils import * def test_fully_connected(): data = mx.sym.var("data") fc_weight = mx.sym.var("weight") fc_bias = mx.sym.var("bias") fc = mx.sym.FullyConnected(data=data, weight=fc_weight, bias=fc_bias, num_hidden=10, no_bias=False, name='fc') data = mx.nd.random.uniform(shape=(5, 5, 5, 13), dtype=np.float32) fc_weight = mx.nd.random.uniform(shape=(10, 325), dtype=np.float32) fc_bias = mx.nd.random.uniform(shape=(10), dtype=np.float32) fc_bias2 = mx.nd.random.uniform(shape=(10, 1), dtype=np.float32) data_np = data.asnumpy().reshape(5, 325) fc_weight_np = np.transpose(fc_weight.asnumpy()) fc_bias_np = fc_bias.asnumpy() res = np.dot(data_np, fc_weight_np) + fc_bias.asnumpy() check_symbolic_forward(fc, {'data': data_np, 'weight': fc_weight.asnumpy(), 'bias': fc_bias_np}, {'fc_output': res}) check_numeric_gradient(fc, {'data': data_np, 'weight': fc_weight.asnumpy(), 'bias': fc_bias_np}, numeric_eps=1e-2, rtol=1e-4, atol=1e-2) # TODO: Fix Bug when bias has ndim > 1 check_symbolic_forward(fc, {'data': data_np, 'weight': fc_weight.asnumpy(), 'bias': fc_bias2.asnumpy()}, {'fc_output': res}) ```
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