THIS IS A TEST INSTANCE. ALL YOUR CHANGES WILL BE LOST!!!!
JIRA link to this project: https://issues.apache.org/jira/browse/MXNET-1184
Problem
Current MXNet only supports maximal tensors size around 4 billon (2^32). This is because uint32_t is used as default data type for tensor size as well as indexing variables. This limitation has created many problems when larger tensors are used in the model.
A naive solution to this problem is to replace all uint32_t in the MXNet backend source code by int64_t. This solution is not viable, however, because many data structures use uint32_t as data type for its members. Unnecessarily replacing these variables to int64_t will increase the memory consumption causing another limitation. Second, MXNet has many submodule dependencies. Updating the variable types in MXNet repository is not enough. We also need to make sure different libraries, such as MKLDNN, MShadow etc. supports the int64_t integer data type. Third, many front end APIs assumes unsigned 32-bit integer interface. Only updating the interface in C/C++ will cause all the language bindings to fail.
Therefore, we need a systematic approach to enhance MXNet to support large tensors.
Operators to be supported
This project is to enable MXNet to support large tensors. It should also provide guideline for future developers of the correct data type to choose when defining an integer variables in the MXNet backend. We should also provide a performance benchmark at operator level as well as model level between 64-bit integer and 32-bit integers. Moreover, we need to provide a mechanism to prevent future PRs breaking this support.
The following epic keeps track of operators that have been supported and the ones to be supported:
If support for additional operators is required then please add a task to the JIRA epic.
Open Questions
- MKLDNN support
- CuDNN support
Proposed Approach
To support large tensor operations in MXNet backend, we need to update the followings:
1) Support large tensor size in NDArray data structure. We need to make sure the data structure of a tensor can hold sufficiently large number of elements.
2) Allow index loop to go beyond 2^31:
In CPU operator implementation, the kernel always use a Map() function to process each data element. The indexing variable need to use int64_t
A PR has been submitted to address a subset of the operators:
https://github.com/apache/incubator-mxnet/pull/13418
3) Update different API interfaces
This involves the API interface between MXNet backend and different front end language.
There are two defined data types used in MXNET backend in addition to the native integer types: index_t, and dim_t. An earlier PR has been submitted to use int64_t for index_t and dim_t:
https://github.com/apache/incubator-mxnet/pull/11742
https://github.com/dmlc/mshadow/pull/348
The shape size currently is also using uint32_t. Although the total number of elements can go above 4 billion, it is very rare for one dimension to go above that number. Therefore, we may be able to keep the shape size in uint32_t.
Future Development Guideline
We should also document the guideline for future development:
- Use index_t for indexing elements
- Use dim_t for dimension size
- Never use unsigned and avoid using uint32_t to declare a non negative number that is not exceeding 4 billion (see: https://google.github.io/styleguide/cppguide.html#Integer_Types)
Challenges
- How to address this problem across all submodules
- How to address this problem across all language bindings
- GPU and MKLDNN support
- This change potentially suffers from memory fragmentation. Since allocation on very large contiguous chunks of memory is not easy for OS. It uses virtual memory to achieve this. But this is also limited to the page size used by OS to allocate memory on Physical Memory. If the memory is too fragmented that OS cannot even allocate memory of the size of smallest page chunk then it will give memory allocation error.
- Approach to mitigate this problem: A new data structure based on the requirements of customer's that require creation of very large tensors. The new data structure can be optimized for either random access or contiguous access and should address the problem of memory fragmentation by allocating memory of smaller sized chunks.
Backward compatibility
We should support all existing operators with uint32_t data types.
Performance Considerations
Since this only changes the data type of indexing variables, not the data type of elements themselves, we do not expect obvious performance impact in CPU. However, there may be performance impact in GPU and we need to verify that.
Test Plan
- Add nightly test to test all existing operators with tensor size over 5 billion. To test each operator in Python, we can leverage the existing check_speed() utility function.