sxjscience commented on issue #18193: URL: https://github.com/apache/incubator-mxnet/issues/18193#issuecomment-621588139
I think one of the root cause is that gluon optimizer and initializer are still using the legacy NDArray. We should fix that. Get Outlook for iOS<https://aka.ms/o0ukef> ________________________________ From: JiangZhaoh <notificati...@github.com> Sent: Wednesday, April 29, 2020 7:37:14 PM To: apache/incubator-mxnet <incubator-mx...@noreply.github.com> Cc: Xingjian SHI <xsh...@connect.ust.hk>; Author <aut...@noreply.github.com> Subject: Re: [apache/incubator-mxnet] [Bug][Numpy] Very basic functionality of Gluon is broken (#18193) I have found the reason. It because set_np() only change the mxnet.numpy functions to default dtype as float64, but doesn't effect mxnet.ndarray functions which still default float32. The issue mentioned above just like this code: >>> import mxnet >>> from mxnet import numpy as np >>> from mxnet import ndarray as nd >>> from mxnet import npx >>> npx.set_np() >>> a = nd.zeros(5) >>> a.dtype <class 'numpy.float32'> >>> np.random.uniform(low = -1, high = 1, size = (5), out = a) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/ubuntu/mxnet/python/mxnet/numpy/random.py", line 137, in uniform return _mx_nd_np.random.uniform(low, high, size=size, ctx=ctx, dtype=dtype, out=out) File "/home/ubuntu/mxnet/python/mxnet/ndarray/numpy/random.py", line 145, in uniform return _api_internal.uniform(low, high, size, ctx, dtype, out) File "mxnet/_ffi/_cython/./function.pxi", line 188, in mxnet._ffi._cy3.core.FunctionBase.__call__ File "mxnet/_ffi/_cython/./function.pxi", line 133, in mxnet._ffi._cy3.core.FuncCall File "mxnet/_ffi/_cython/./base.pxi", line 90, in mxnet._ffi._cy3.core.CALL mxnet.base.MXNetError: Traceback (most recent call last): File "../src/imperative/./imperative_utils.h", line 251 MXNetError: Check failed: outputs[i]->dtype() == out_types[i] (0 vs. 1) : 0-th output has invalid dtype. Expecting 1 got 0 in operator _npi_uniform >>> npx.set_np(dtype = False) >>> np.random.uniform(low = -1, high = 1, size = (5), out = a) [0.09762704 0.18568921 0.43037868 0.6885315 0.20552671] <NDArray 5 @cpu(0)> So, I think I'd better not to set dtype=True flag in npx.set_np(), seperate this flag along in a single function npx.set_numpy_default_dtype() instead. Or if there any other better solutions, please tell me. — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub<https://github.com/apache/incubator-mxnet/issues/18193#issuecomment-621577944>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/ABHQH3R5XJLVP76L7A6LBD3RPDP5VANCNFSM4MT3GKPQ>. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
