igolan opened a new issue #16188: symbol.contrib.cond does not support some built-in operators URL: https://github.com/apache/incubator-mxnet/issues/16188 ## Description ``symbol.contrib.cond`` operator does not support the build-in operators ``round``, ``floor`` and ``ceil`` (and probably some more). ## Environment info (Required) ``` ----------Python Info---------- Version : 3.7.4 Compiler : Clang 10.0.1 (clang-1001.0.46.4) Build : ('default', 'Jul 9 2019 18:13:23') Arch : ('64bit', '') ------------Pip Info----------- Version : 19.0.3 Directory : /Users/XX/PycharmProjects/XX/venv/lib/python3.7/site-packages/pip-19.0.3-py3.7.egg/pip ----------MXNet Info----------- Version : 1.5.0 Directory : /Users/XX/PycharmProjects/XX/venv/lib/python3.7/site-packages/mxnet Commit Hash : 75a9e187d00a8b7ebc71412a02ed0e3ae489d91f Library : ['/Users/XX/PycharmProjects/XX/venv/lib/python3.7/site-packages/mxnet/libmxnet.so'] Build features: ✖ CUDA ✖ CUDNN ✖ NCCL ✖ CUDA_RTC ✖ TENSORRT ✔ CPU_SSE ✔ CPU_SSE2 ✔ CPU_SSE3 ✔ CPU_SSE4_1 ✔ CPU_SSE4_2 ✖ CPU_SSE4A ✔ CPU_AVX ✖ CPU_AVX2 ✖ OPENMP ✖ SSE ✖ F16C ✖ JEMALLOC ✖ BLAS_OPEN ✖ BLAS_ATLAS ✖ BLAS_MKL ✖ BLAS_APPLE ✔ LAPACK ✖ MKLDNN ✔ OPENCV ✖ CAFFE ✖ PROFILER ✔ DIST_KVSTORE ✖ CXX14 ✖ INT64_TENSOR_SIZE ✔ SIGNAL_HANDLER ✖ DEBUG ----------System Info---------- Platform : Darwin-18.7.0-x86_64-i386-64bit system : Darwin node : XXX release : 18.7.0 version : Darwin Kernel Version 18.7.0: Tue Aug 20 16:57:14 PDT 2019; root:xnu-4903.271.2~2/RELEASE_X86_64 ----------Hardware Info---------- machine : x86_64 processor : i386 b'machdep.cpu.brand_string: Intel(R) Core(TM) i7-7660U CPU @ 2.50GHz' b'machdep.cpu.features: FPU VME DE PSE TSC MSR PAE MCE CX8 APIC SEP MTRR PGE MCA CMOV PAT PSE36 CLFSH DS ACPI MMX FXSR SSE SSE2 SS HTT TM PBE SSE3 PCLMULQDQ DTES64 MON DSCPL VMX SMX EST TM2 SSSE3 FMA CX16 TPR PDCM SSE4.1 SSE4.2 x2APIC MOVBE POPCNT AES PCID XSAVE OSXSAVE SEGLIM64 TSCTMR AVX1.0 RDRAND F16C' b'machdep.cpu.leaf7_features: RDWRFSGS TSC_THREAD_OFFSET SGX BMI1 HLE AVX2 SMEP BMI2 ERMS INVPCID RTM FPU_CSDS MPX RDSEED ADX SMAP CLFSOPT IPT MDCLEAR TSXFA IBRS STIBP L1DF SSBD' b'machdep.cpu.extfeatures: SYSCALL XD 1GBPAGE EM64T LAHF LZCNT PREFETCHW RDTSCP TSCI' ----------Network Test---------- Setting timeout: 10 Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0137 sec, LOAD: 0.5112 sec. Timing for Gluon Tutorial(en): http://gluon.mxnet.io, DNS: 0.0180 sec, LOAD: 0.4525 sec. Timing for Gluon Tutorial(cn): https://zh.gluon.ai, DNS: 0.0198 sec, LOAD: 0.8612 sec. Timing for FashionMNIST: https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz, DNS: 0.0233 sec, LOAD: 0.1894 sec. Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0120 sec, LOAD: 0.3173 sec. Timing for Conda: https://repo.continuum.io/pkgs/free/, DNS: 0.0105 sec, LOAD: 0.0961 sec. ----------Environment---------- ``` I'm using Pyton ## Build info (Required if built from source) N/A ## Error Message: ``` Traceback (most recent call last): File "/Users/XX/PycharmProjects/XX/playground.py", line 39, in <module> out.backward() File "/Users/XX/PycharmProjects/XX/venv/lib/python3.7/site-packages/mxnet/ndarray/ndarray.py", line 2216, in backward ctypes.c_void_p(0))) File "/Users/XX/PycharmProjects/XX/venv/lib/python3.7/site-packages/mxnet/base.py", line 253, in check_call raise MXNetError(py_str(_LIB.MXGetLastError())) mxnet.base.MXNetError: Error in operator node_1_backward: Error in operator mlp0__cond0_backward: [12:15:35] src/imperative/cached_op.cc:1322: Check failed: in_attrs->size() == bwd_input_eid.size() (3 vs. 2) : Stack trace: [bt] (0) 1 libmxnet.so 0x00000001143d3929 mxnet::op::NDArrayOpProp::~NDArrayOpProp() + 4473 [bt] (1) 2 libmxnet.so 0x0000000115939ccf mxnet::CachedOpBackward(mxnet::OpStatePtr const&, mxnet::OpContext const&, std::__1::vector<mxnet::NDArray, std::__1::allocator<mxnet::NDArray> > const&, std::__1::vector<mxnet::OpReqType, std::__1::allocator<mxnet::OpReqType> > const&, std::__1::vector<mxnet::NDArray, std::__1::allocator<mxnet::NDArray> > const&) + 9439 [bt] (2) 3 libmxnet.so 0x0000000115ae40e2 void mxnet::op::extract_by_loc<mxnet::NDArray>(std::__1::vector<mxnet::NDArray, std::__1::allocator<mxnet::NDArray> > const&, mxnet::Tuple<long long>, std::__1::vector<mxnet::NDArray, std::__1::allocator<mxnet::NDArray> >*) + 3586 [bt] (3) 4 libmxnet.so 0x0000000115ae0ad5 MXTVMBridge + 168405 [bt] (4) 5 libmxnet.so 0x000000011591166e std::__1::__tree<std::__1::__value_type<unsigned long, mxnet::NDArray>, std::__1::__map_value_compare<unsigned long, std::__1::__value_type<unsigned long, mxnet::NDArray>, std::__1::less<unsigned long>, true>, std::__1::allocator<std::__1::__value_type<unsigned long, mxnet::NDArray> > >::erase(std::__1::__tree_const_iterator<std::__1::__value_type<unsigned long, mxnet::NDArray>, std::__1::__tree_node<std::__1::__value_type<unsigned long, mxnet::NDArray>, void*>*, long>) + 6094 [bt] (5) 6 libmxnet.so 0x000000011591ecb6 std::__1::__tree<std::__1::__value_type<unsigned long, mxnet::NDArray>, std::__1::__map_value_compare<unsigned long, std::__1::__value_type<unsigned long, mxnet::NDArray>, std::__1::less<unsigned long>, true>, std::__1::allocator<std::__1::__value_type<unsigned long, mxnet::NDArray> > >::erase(std::__1::__tree_const_iterator<std::__1::__value_type<unsigned long, mxnet::NDArray>, std::__1::__tree_node<std::__1::__value_type<unsigned long, mxnet::NDArray>, void*>*, long>) + 60950 [bt] (6) 7 libmxnet.so 0x000000011591763a std::__1::__tree<std::__1::__value_type<unsigned long, mxnet::NDArray>, std::__1::__map_value_compare<unsigned long, std::__1::__value_type<unsigned long, mxnet::NDArray>, std::__1::less<unsigned long>, true>, std::__1::allocator<std::__1::__value_type<unsigned long, mxnet::NDArray> > >::erase(std::__1::__tree_const_iterator<std::__1::__value_type<unsigned long, mxnet::NDArray>, std::__1::__tree_node<std::__1::__value_type<unsigned long, mxnet::NDArray>, void*>*, long>) + 30618 [bt] (7) 8 libmxnet.so 0x0000000115929432 mxnet::CachedOp::SetForwardGraph(mxnet::CachedOp::GraphInfo*, bool, std::__1::vector<mxnet::NDArray*, std::__1::allocator<mxnet::NDArray*> > const&) + 12402 [bt] (8) 9 libmxnet.so 0x00000001159395ab mxnet::CachedOpBackward(mxnet::OpStatePtr const&, mxnet::OpContext const&, std::__1::vector<mxnet::NDArray, std::__1::allocator<mxnet::NDArray> > const&, std::__1::vector<mxnet::OpReqType, std::__1::allocator<mxnet::OpReqType> > const&, std::__1::vector<mxnet::NDArray, std::__1::allocator<mxnet::NDArray> > const&) + 7611 ``` ## Minimum reproducible example ``` import mxnet as mx from mxnet import nd, autograd, gluon class MLP(gluon.HybridBlock): def __init__(self, **kwargs): super(MLP, self).__init__(**kwargs) with self.name_scope(): self.dense1 = gluon.nn.Dense(1, in_units=1) def hybrid_forward(self, F, x): # Not working: cond_out = F.contrib.cond(F.ones(1) == F.ones(1), lambda: self.dense1(x), lambda: F.round(x)) # cond_out = F.contrib.cond(F.ones(1) != F.ones(1), lambda: self.dense1(x), lambda: F.round(x)) # cond_out = F.contrib.cond(F.ones(1) != F.ones(1), lambda: self.dense1(x), lambda: F.floor(x)) # cond_out = F.contrib.cond(F.ones(1) == F.ones(1), lambda: self.dense1(x), lambda: F.floor(x)) # cond_out = F.contrib.cond(F.ones(1) != F.ones(1), lambda: self.dense1(x), lambda: F.ceil(x)) # cond_out = F.contrib.cond(F.ones(1) == F.ones(1), lambda: self.dense1(x), lambda: F.ceil(x)) # Working: # cond_out = F.contrib.cond(F.ones(1) != F.ones(1), lambda: self.dense1(x), lambda: F.relu(x)) # cond_out = F.contrib.cond(F.ones(1) == F.ones(1), lambda: self.dense1(x), lambda: F.relu(x)) # cond_out = F.round(x) # cond_out = F.floor(x) # cond_out = F.ceil(x) # cond_out = F.contrib.cond(F.ones(1) == F.ones(1), lambda: self.dense1(x), lambda: F.sign(x)) # cond_out = F.contrib.cond(F.ones(1) != F.ones(1), lambda: self.dense1(x), lambda: F.sign(x)) cond_out = F.broadcast_mul(cond_out, self.dense1(x)) return cond_out model_ctx = mx.cpu() net = MLP() net.hybridize() net.collect_params().initialize(mx.init.Constant([1]), ctx=model_ctx) data = nd.ones((3,1)) * 1.7 with mx.autograd.record(): out = net(data.as_in_context(model_ctx)) out.backward() print(net.dense1.weight.grad()) with mx.autograd.record(): out = net(data.as_in_context(model_ctx)) out.backward() print(net.dense1.weight.grad()) ``` ## Steps to reproduce 1. Run the code above. hybrid_forward has few lines with comment if it's working or not (uncomment lines to see more examples) ## What have you tried to solve it? N/A Might be related to #12154 , #11641 , #16182 and #16187 . I keep those issues separate because I'm not sure the cause is the same.
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