Hi Rohit, The existing MKL-DNN and its integration in MXNet should already support *large tensor* which means the total number of elements (Prod(shape)) can exceed INT_MAX. Feel free to me know if you find any issue when using MKL-DNN operators with large tensors.
For large dimension size (shape[x]), MKL-DNN is going to support in its 1.0 release and will be released at the middle of year. But I'm not sure if MXNet has plan to support that. Thanks, -tao -----Original Message----- From: Srivastava, Rohit Kumar [mailto:[email protected]] Sent: Sunday, May 19, 2019 7:23 AM To: [email protected] Subject: Re: [RFC] Support for creation of Large Tensors in MXNet Hi Tao, There are already couple of operators implemented in MXNet that are currently supporting Tensors with size over ~4.5 billion. In the meantime core MXNet can move ahead with providing initial support for such large tensors so MXNet customers can start using it. Good to hear MKLDNN will provide support for such cases. Do you have a timeline as to when this feature will be released ? -Rohit On 4/29/19, 7:18 PM, "Lv, Tao A" <[email protected]> wrote: Thank you Lin! I would expect the current MKL-DNN implementation already supports the scenario you mentioned here. Can be verified by this issue: https://github.com/apache/incubator-mxnet/issues/13451 But as I said before, since we support flatten or reshape operators, so it's possible for users to convert a tensor with large element size to a tensor with large dimension size. It possibly will cause issue there. To cover more cases, MKL-DNN is going to support INT64 dimension size in its coming 1.0 major release. -tao -----Original Message----- From: Lin Yuan [mailto:[email protected]] Sent: Tuesday, April 30, 2019 12:56 AM To: [email protected] Subject: Re: [RFC] Support for creation of Large Tensors in MXNet Tao, - what's the max size of dimensionality? Which data type is used to define dimensionality (ndims)? We assume the max size of dimensionality is relatively small. Hence `int` data type is used to define ndim - what's the max size of each dimension? Which data type is used to define dimension size (shape[x])? Currently, we assume the max size of each dimension is not going to exceed 2^31 in real applications. Hence the data type is `int32_t` - what's the max size of total elements? Which data type is used to define element size (Prod(shape))? We assume the total number of elements in a tensor can be larger than 2^32 in some applications such as deep graph library. We use the data type `int64_t` to represent the total element size. Currently due to performance regression in some operators (such as transpose), we used a compiler flag to set this data type to `int32_t` by default. Once we have ways to mitigate the performance regression, we will set the default data type to `int64_t`, which is part of the effort in this project that Rohit proposed. What is the plan in MKLDNN to support large tensors? We may want to coordinate the progress since many operators are using MKLDNN implementation in CPU now. Many Thanks, Lin On Sun, Apr 28, 2019 at 7:52 PM Lv, Tao A <[email protected]> wrote: > Thank you for bringing this topic to dev, Rohit. > > Regarding large tensor, can you articulate: > - what's the max size of dimensionality? Which data type is used to > define dimensionality (ndims)? > - what's the max size of each dimension? Which data type is used to > define dimension size (shape[x])? > - what's the max size of total elements? Which data type is used to > define element size (Prod(shape))? > > For me, any of these three can be *large*. > > -----Original Message----- > From: Srivastava, Rohit Kumar > [mailto:[email protected]] > Sent: Saturday, April 27, 2019 7:33 AM > To: [email protected] > Subject: [RFC] Support for creation of Large Tensors in MXNet > > Dear Community, > > Currently MXNet supports creation of Tensors containing up to 2^32 > elements. However there are cases where tensors of size over 5 billion > is required > > We plan to support creation of large tensors on MXNet. A design > proposal is ready for review: > https://cwiki.apache.org/confluence/display/MXNET/Large+Tensor+Support > > We will appreciate any help and feedbacks from the community. > > Thank you! > > Rohit >
