matteosal opened a new issue #15464: MKL-DNN gives wrong bias gradient if weights gradient is not requested URL: https://github.com/apache/incubator-mxnet/issues/15464 ## Description When using MKL-DNN and asking the gradient of a convolution with respect to its biases, the result is wrong unless the gradient with respect to the weights is also requested. ## Environment info (Required) ``` ----------Python Info---------- Version : 3.7.2 Compiler : GCC 7.3.0 Build : ('default', 'Dec 29 2018 06:19:36') Arch : ('64bit', '') ------------Pip Info----------- Version : 19.0.1 Directory : /opt/Anaconda/lib/python3.7/site-packages/pip ----------MXNet Info----------- Version : 1.5.0 Directory : /home/matteo/Git/mxnet/python/mxnet Hashtag not found. Not installed from pre-built package. ----------System Info---------- Platform : Linux-4.15.0-54-generic-x86_64-with-debian-buster-sid system : Linux node : mongolius release : 4.15.0-54-generic version : #58-Ubuntu SMP Mon Jun 24 10:55:24 UTC 2019 ----------Hardware Info---------- machine : x86_64 processor : x86_64 Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Byte Order: Little Endian CPU(s): 8 On-line CPU(s) list: 0-7 Thread(s) per core: 2 Core(s) per socket: 4 Socket(s): 1 NUMA node(s): 1 Vendor ID: GenuineIntel CPU family: 6 Model: 94 Model name: Intel(R) Core(TM) i7-6700HQ CPU @ 2.60GHz Stepping: 3 CPU MHz: 2700.094 CPU max MHz: 3500,0000 CPU min MHz: 800,0000 BogoMIPS: 5184.00 Virtualization: VT-x L1d cache: 32K L1i cache: 32K L2 cache: 256K L3 cache: 6144K NUMA node0 CPU(s): 0-7 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb invpcid_single pti ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx rdseed adx smap clflushopt intel_pt xsaveopt xsavec xgetbv1 xsaves dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp md_clear flush_l1d ----------Network Test---------- Setting timeout: 10 Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0010 sec, LOAD: 1.0852 sec. Timing for Gluon Tutorial(en): http://gluon.mxnet.io, DNS: 0.1153 sec, LOAD: 0.9477 sec. Timing for Gluon Tutorial(cn): https://zh.gluon.ai, DNS: 0.1108 sec, LOAD: 0.8710 sec. Timing for FashionMNIST: https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz, DNS: 0.0825 sec, LOAD: 1.2461 sec. Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0351 sec, LOAD: 1.1176 sec. Timing for Conda: https://repo.continuum.io/pkgs/free/, DNS: 0.0400 sec, LOAD: 0.5449 sec. ``` Using the python interface ## Build info (Required if built from source) Compiler (gcc/clang/mingw/visual studio): gcc MXNet commit hash: 6a8d9eb5fd4f7133c094149dc80a3a236534f223 Build config: unchanged `config.mk`, except for `USE_OPENCV = 0` ## Minimum reproducible example ``` import mxnet as mx sym = mx.sym.Convolution( mx.sym.Variable('in'), mx.sym.Variable('w'), mx.sym.Variable('b'), kernel=(1, 1), num_filter=1 ) args = { 'in': mx.nd.ones([1, 1, 3, 3]), 'w': mx.nd.ones([1, 1, 1, 1]), 'b': mx.nd.ones([1]), } grad1 = { 'in': mx.nd.zeros([1, 1, 3, 3]), 'w': mx.nd.zeros([1, 1, 1, 1]), 'b': mx.nd.zeros([1]), } grad2 = { 'in': mx.nd.zeros([1, 1, 3, 3]), 'w': mx.nd.zeros([1, 1, 1, 1]), 'b': mx.nd.zeros([1]), } req1 = {'in': 'null', 'w': 'write', 'b': 'write'} req2 = {'in': 'null', 'w': 'null', 'b': 'write'} outgrad = mx.nd.ones([1, 1, 3, 3]) ex1 = sym.bind(mx.cpu(), args, args_grad=grad1, grad_req=req1) ex2 = sym.bind(mx.cpu(), args, args_grad=grad2, grad_req=req2) ex1.forward(True); ex1.backward(out_grads=outgrad); ex2.forward(True); ex2.backward(out_grads=outgrad); print(grad1['b']) print(grad2['b']) ``` The above script prints a wrong value (0) for `grad2['b']`, while `grad1['b'] is correct (9)`: ``` [9.] <NDArray 1 @cpu(0)> [0.] <NDArray 1 @cpu(0)> ``` running with `MXNET_MKLDNN_ENABLED=0` produces the correct result (9) for both gradients
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