OlderChanges.md

Older changes

This page contains older changes, that have been moved from the Recent Changes section on the main page.

For the most recent changes please see Recent Changes

• 23rd July:
  • Fixed memory leak on Intel HD Graphics
• 22th July:
  • Performance improvement:
    • All per-element operations are around 2-5 faster on NVIDIA and AMD now
    • In the specific, this means that times for Karpathy's char-rnn are around 2-3 times faster on NVIDIA and AMD cards, compared to before
  • colesbury's pull request #176 ported to cltorch, 'Allow CudaTensors as indices'
  • andresy's pull request #203 ported to cltorch, 'expose retain and free for CudaStorage/CudaTensor'
• 19th July:
  • Upgrade EasyCL version
  • Need to explicitly enable timing now (just in case impacts performance)
  • DumpTimings now shows count of number of calls, as well as timings
• 18th July:
  • Added custom user kernels
• 16th July:
  • Did some cleaning:
    • source code now all in src directory, to keep the front page on github clean
    • moved a bunch of stuff from this page to other pages, ie older changes, and list of what works
    • 20x speed boost for Apply kernel, and char-rnn, on Intel HD5500 GPU
• 15th July:
  • can pass point ClTensor now also to :lt(), :gt(), :le(), :ge(), :eq(), :ne()
  • added profiling:
    • cltorch.setProfiling(1) to enable (has a performance hit obviously, whilst enabled)
    • cltorch.dumpProfiling() to dump timings since last dump
      • timings are cumulative over kernel filename/kernelname combination
• 14th July:
  • created point tensors:
    • :sum() can return a point tensor, which stays on the GPU, eliminating gpu pipeline stall, see presentation above
    • add(), csub(), mul and div can all accept a point tensor in place of their scalar argument
  • :prod() can return a point tensor too now, as can :max(), :min(), :all(), and :any()
  • can pass point ClTensor also to :fill() now
• 13th July:
  • possible to use tensors without :setDevice() to same device as them first. Tested with :sum(), :sum(1), and :sum(2) for now
• 12th July:
  • add cltorch.about(), to provide build information
• 10th July:
  • added cmin, cmax, for tensors and scalars (as per https://github.com/torch/cutorch/pull/198/files )
• 5th July:
  • fixed some Mac build/load issues, so builds/loads on Mac now (thank you to mlajtos, szagouyko, centime, luo123n, and pdhvip for their enormous help with fixing this :-) )
  • getDeviceProperties and so on now only show GPU and APU devices, ignores pure CPU devices (which pure CPU devices are not supported by cltorch at this time)
  • added cltorch.test(), which runs unit tests
• 4th July:
  • torch.save and torch.load implemented
• 27th June:
  • fixed more bugs involving Tensor copy. Hopefully should be fixed permanently now :-P
  • added cltorch.dumpTimings(), which will dump cumulative timings for various parts of the engine. It's mostly for usage by maintainers / optimizers.
  • massive optimization for anything involving apply, reduce, reduceall, index etc => this makes the ltsm script at karpathy/char-rnn run significantly faster when using OpenCL now :-)
• 26th June:
  • add addcmul, and unit test
  • add addcdiv, and unit test
  • added apply2 and apply3 as synonyms for map and map2
  • can use x, y, z instead of *out, *in1 and *in2, in apply, map, etc
  • fix a buffer copy bug (note: implies updating EasyCL, and rebuilding EasyCL, see notes on updating above)
• 25th June:
  • added bernoulli (generates on host-side for now, but I guess this is fast enough for many things?)
• 24th June:
  • added tests for gather, and removed some spam
  • added scatter (for both tensor or float source)
• 23rd June:
  • Fixed bug where operations such as apply and map on tensors with non-zero offset didnt work correctly (ie, fill etc after narrow or similar)
  • Added gather
• 22nd June:
  • Under the hood:
    • Moved marking a buffer dirty, ie modified on the GPU, from THClTensorMathBlas.cpp to THClBlas.cpp
      • This fixes a bug in clnn, where the results of a convolutional layer were not being written back to the output tensor
  • tests pass now on an AMD gpu (actually I managed to scrounge access to a W9100 :-D )
• 21st June:
  • Under the hood:
    • Upgraded new THClKernels class to handle THClTensorInfo
    • migrated Reduce, ReduceAll, etc to use THClKernels
    • upgraded EasyCL to handle uint, long, ulong
  • added cltorch.finish() and cltorch.synchronize(), both do same thing, which is a clFinish(), on current device
  • made it possible to require both cutorch and cltorch, as long as one requires cutorch followed by cltorch, in that order
• 20th June:
  • rename new sub method to csub so doesnt collide with existing sub
  • added cltorch.setTrace(1|0), which prints out every allocate or copy of gpu buffers (named 'wrapper's)
  • removed set and get methods, because cause repeated gpu buffer copy (actually, get not too bad, but does copy whole buffer; set copies whole buffer, repeatedly :-P )
  • modifed ClStorage.__string__ to first copy whole storage to FloatStorage, once, then convert this to string, rather than using now non-existent get
  • torch.ClTensor{3,5,2} will now first create this as a FloatTensor then call copy on this, to convert whole Tensor/Storage to ClTensor (avoids repeated set calls)
  • added normall, ie can do torch.norm(c), torch.norm(c, exponent)
  • added prod, prod(1), prod(2)
  • max(1) and min(1) now return the indices too, as well as the max. Ditto for dimension 2.
  • added :all() and :any()
  • added :indexFill()
  • added :indexCopy()
  • added :indexSelect()
  • added torch.cumsum(x,2) and torch.cumsum(x,1)
  • added torch.cumprod(x,2) and torch.cumprod(x,1)
  • Under the hood:
    • created new THClKernels class:
      • handles THClTensor kernel input
      • provides run method that takes a dim3 grid and block input, as for cutorch kernel launches
      • migrated TensorIndexed to use THClKernels
• 19th June:
  • fixed a compile bug in EasyCL, when lua5.2/5.3 header files are present (not tested yet)
  • added a:sub(b) method, which does element-wise subtraction of b from a, and puts results in a
  • migrated to new version of EasyCL, with one fewer waitforevents, to try to boost perf a bit
  • added apply, map, map2 :-) (which run on GPU, at full speed)
  • added 2-pass reduceall, ie can do reduceall on much larger tensors now
• 18th June:
  • fixed a bug in clBLAS sger that meant that sger crashed on even tiny 5x5 matrices on nvidia, using either rowmajor or columnmajor :-) clMathLibraries/clBLAS#109
  • note that you will need to git submodule update, and rm -Rf build/clBLAS, in order to pick up the new version of clBLAS
  • moved clBLAS initialization code out of inner loops => huge speed boost
  • added :neg() operator, which negates the tensor (like - but without reallocation, I think)
• 15th-17th June:
  • pow(x,y) no longer returns undefined values for x containing, or being, negative
  • pow(x,y) now uses pown when y is an exact integer scalar (ie where (float)((int)y) == y)
  • when no opencl-enabled devices enabled, now raise a THError, with a clear error message, rather than throwing a C++ exception, with no error message output
  • under the hood: added cltorch.getState()
  • renamed libTHCL.so to libTHCl.so
  • added THCl include files to install section
  • masked fill works now
  • torch.addr works now
• 15th June:
  • C:t() working
• 14th June:
  • ReduceAll working :-) For now means: sometensor:sum() works
  • sometensor:sum(1) and sometensor:sum(2) working too now :-)
  • A:min(), A:max() added
  • created unit tests, in test directory, cltorch-unit-tensor.lua which pass
• 13th June:
  • added cltorch.setDevice/cltorch.getDevice, see test-device.lua for an example
  • added EasyCL includes to EasyCL install section, to remove build errors with "EasyCL.h" not found, etc