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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2019/10/21 06:10:36 UTC
[GitHub] [incubator-mxnet] reminisce opened a new issue #16559: Tracking
mxnet.numpy operator issues for 1.6.0 release
reminisce opened a new issue #16559: Tracking mxnet.numpy operator issues for 1.6.0 release
URL: https://github.com/apache/incubator-mxnet/issues/16559
## Issues from running D2L [numpy2](https://github.com/d2l-ai/d2l-en/tree/numpy2) branch
1. chapter_preliminaries/probability.md
```
TypeError: no implementation found for 'numpy.broadcast_arrays' on types that implement __array_function__: [<class 'numpy.ndarray'>, <class 'mxnet.numpy.ndarray'>]
```
2. chapter_preliminaries/probability.md
```
MXNetError: [09:16:23] src/operator/numpy/np_true_divide.cc:43: Check failed: lhs_dtype == rhs_dtype (7 vs. 0) : true_divide currently only supports same dtype for dividend and divisor
```
3. chapter_multilayer-perceptrons/dropout.md
```
mask = np.random.uniform(0, 1, X.shape) > drop_prob
return mask * X / (1.0-drop_prob)
Fails on the second line there, should be the problem with boolean multiplying with float
```
4. chapter_deep-learning-computation/parameters.md
```
data[:] = np.random.uniform(-10, 10, data.shape)
data *= np.abs(data) >= 5
Fails on the 2nd line, still the multiplication between boolean & float
```
5. chapter_recurrent-neural-networks/seq2seq.md
```
MXNetError: [10:46:26] src/operator/numpy/np_true_divide.cc:43: Check failed: lhs_dtype == rhs_dtype (0 vs. 6) : true_divide currently only supports same dtype for dividend and divisor<ipython-input-10-23a855fab898> in train_s2s_ch8(model, data_iter, lr, num_epochs, ctx) 24 metric.add(l.sum(), num_tokens) 25 if epoch % 10 == 0: ---> 26 animator.add(epoch, (metric[0]/metric[1],)) 27 print('loss %.3f, %d tokens/sec on %s ' % ( 28 metric[0]/metric[1], metric[1]/timer.stop(), ctx))
--
```
6. All notebooks with train_s2s_ch8
```
MXNetError: [10:46:26] src/operator/numpy/np_true_divide.cc:43: Check failed: lhs_dtype == rhs_dtype (0 vs. 6) : true_divide currently only supports same dtype for dividend and divisor
<ipython-input-10-23a855fab898> in train_s2s_ch8(model, data_iter, lr, num_epochs, ctx) 24 metric.add(l.sum(), num_tokens) 25 if epoch % 10 == 0: ---> 26 animator.add(epoch, (metric[0]/metric[1],)) 27 print('loss %.3f, %d tokens/sec on %s ' % ( 28 metric[0]/metric[1], metric[1]/timer.stop(), ctx))
```
7. chapter_optimization/optimization-intro.md
```
ValueError: mxnet.numpy operator `<function column_stack at 0x7ff910066e18>` has not been registered in the _NUMPY_ARRAY_FUNCTION_LIST. Please make sure you are using NumPy >= 1.17.0 and the operator implementation is compatible with NumPy. Then add the operator name to the list.
```
8. chapter_optimization/convexity.md
```
Same as above
```
9. chapter_natural-language-processing/sentiment-analysis-rnn.md
```
---------------------------------------------------------------------------MXNetError Traceback (most recent call last)<ipython-input-7-a7e697bf18e7> in <module>* 2* trainer = gluon.Trainer(net.collect_params(), 'adam', {'learning_rate': lr})* 3* loss = gluon.loss.SoftmaxCrossEntropyLoss()----> 4 d2l.train_ch12(net, train_iter, test_iter, loss, trainer, num_epochs, ctx)~/d2l-numpy/d2l/d2l.py in train_ch12(net, train_iter, test_iter, loss, trainer, num_epochs, ctx_list, split_f)* 1054* timer.start()* 1055* l, acc = train_batch_ch12(-> 1056 net, features, labels, loss, trainer, ctx_list, split_f)* 1057* metric.add(l, acc, labels.shape[0], labels.size)* 1058* timer.stop()~/d2l-numpy/d2l/d2l.py in train_batch_ch12(net, features, labels, loss, trainer, ctx_list, split_f)* 1037* l.backward()* 1038* trainer.step(features.shape[0])-> 1039 train_loss_sum = sum([float(l.sum()) for l in ls])* 1040* train_acc_sum = sum(d2l.accuracy(py, y) for py, y in zip(pys, ys))* 1041* return train_loss_sum, train_acc_sum
~/d2l-numpy/d2l/d2l.py in <listcomp>(.0)* 1037* l.backward()* 1038* trainer.step(features.shape[0])-> 1039 train_loss_sum = sum([float(l.sum()) for l in ls])* 1040* train_acc_sum = sum(d2l.accuracy(py, y) for py, y in zip(pys, ys))* 1041* return train_loss_sum, train_acc_sum
~/mxnet_master/python/mxnet/numpy/multiarray.py in __float__(self)* 791* if num_elements != 1:* 792* raise TypeError('only size-1 arrays can be converted to Python scalars')--> 793 return float(self.item())* 794** 795* def __int__(self):~/mxnet_master/python/mxnet/numpy/multiarray.py in item(self, *args)* 830* """* 831* # TODO(junwu): no need to call asnumpy() on the whole array.--> 832 return self.asnumpy().item(*args)* 833** 834* @property
~/mxnet_master/python/mxnet/ndarray/ndarray.py in asnumpy(self)* 2517* self.handle,* 2518* data.ctypes.data_as(ctypes.c_void_p),-> 2519 ctypes.c_size_t(data.size)))* 2520* return data* 2521*~/mxnet_master/python/mxnet/base.py in check_call(ret)* 252* """* 253* if ret != 0:--> 254 raise MXNetError(py_str(_LIB.MXGetLastError()))* 255** 256*MXNetError: [11:23:59] src/operator/./rnn-inl.h:1505: Check failed: e == cudaSuccess || e == cudaErrorCudartUnloading: CUDA: invalid resource handle
Stack trace:
[bt] (0) /home/ubuntu/mxnet_master/python/mxnet/../../lib/libmxnet.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x43) [0x7fe81ce57ff3]
[bt] (1) /home/ubuntu/mxnet_master/python/mxnet/../../lib/libmxnet.so(mxnet::op::RNNOp<mshadow::gpu, float, float>::Backward(mxnet::OpContext const&, std::vector<mxnet::TBlob, std::allocator<mxnet::TBlob> > const&, std::vector<mxnet::TBlob, std::allocator<mxnet::TBlob> > const&, std::vector<mxnet::TBlob, std::allocator<mxnet::TBlob> > const&, std::vector<mxnet::OpReqType, std::allocator<mxnet::OpReqType> > const&, std::vector<mxnet::TBlob, std::allocator<mxnet::TBlob> > const&)+0x129a) [0x7fe82357b35a]
[bt] (2) /home/ubuntu/mxnet_master/python/mxnet/../../lib/libmxnet.so(void mxnet::op::RNNStatefulGradCompute<mshadow::gpu>(mxnet::OpStatePtr const&, mxnet::OpContext const&, std::vector<mxnet::TBlob, std::allocator<mxnet::TBlob> > const&, std::vector<mxnet::OpReqType, std::allocator<mxnet::OpReqType> > const&, std::vector<mxnet::TBlob, std::allocator<mxnet::TBlob> > const&)+0x146e) [0x7fe8235b508e]
```
**Note:** Caused by #16391 (https://github.com/apache/incubator-mxnet/pull/16391), verified fix by reverting the PR.
Also pinged DickJC123 for a fix
10. chapter_recommender-systems/autorec.md
```
---------------------------------------------------------------------------MXNetError Traceback (most recent call last)<ipython-input-4-cc01b0d0d033> in <module>* 2* # Load the MovieLens 100K dataset* 3* df, num_users, num_items = d2l.read_data_ml100k()----> 4 train_data, test_data = d2l.split_data_ml100k(df, num_users, num_items)* 5* _, _, _, train_inter_mat = d2l.load_data_ml100k(train_data, num_users,* 6* num_items)
~/d2l-numpy/d2l/d2l.py in split_data_ml100k(data, num_users, num_items, split_mode, test_ratio)* 1432* else:* 1433* mask = [True if x == 1 else False for x in np.random.uniform(-> 1434 0, 1, (len(data))) < 1 - test_ratio]* 1435* neg_mask = [not x for x in mask]* 1436* train_data, test_data = data[mask], data[neg_mask]~/d2l-numpy/d2l/d2l.py in <listcomp>(.0)* 1431* test_data = pd.DataFrame(test_data)* 1432* else:-> 1433 mask = [True if x == 1 else False for x in np.random.uniform(* 1434* 0, 1, (len(data))) < 1 - test_ratio]* 1435* neg_mask = [not x for x in mask]~/mxnet_master/python/mxnet/numpy/multiarray.py in __bool__(self)* 781* return False* 782* elif num_elements == 1:--> 783 return bool(self.item())* 784* else:* 785* raise ValueError("The truth value of an ndarray with multiple elements is ambiguous.")~/mxnet_master/python/mxnet/numpy/multiarray.py in item(self, *args)* 830* """* 831* # TODO(junwu): no need to call asnumpy() on the whole array.--> 832 return self.asnumpy().item(*args)* 833** 834* @property
~/mxnet_master/python/mxnet/ndarray/ndarray.py in asnumpy(self)* 2517* self.handle,* 2518* data.ctypes.data_as(ctypes.c_void_p),-> 2519 ctypes.c_size_t(data.size)))* 2520* return data* 2521*~/mxnet_master/python/mxnet/base.py in check_call(ret)* 252* """* 253* if ret != 0:--> 254 raise MXNetError(py_str(_LIB.MXGetLastError()))* 255** 256*MXNetError: [11:27:58] src/operator/numpy/../tensor/elemwise_binary_scalar_op.h:264: Unknown type enum 7
```
**Note:** Verified fix by changing that type switch at src/operator/tensor/elemwise_binary_scalar_op.h:264 to the "WITH_BOOL" version
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