Create BlockSparse Tensor #202

fmassa · 2022-02-02T18:36:52Z

What does this PR do?

This PR creates a BlockSparse Tensor (similar to SparseCSRTensor). This will ultimately enable to use the same code-path in the scaled_dot_product_attention, so that users just need to pass a block-sparse matrix in the mask to get the expected results (as is the case now with SparseCSRTensor). This is not yet entirely plugged, but will be done in a follow-up PR.

I haven't made the Blocksparse nn.Module use our new Tensor for now as it relies on additional information for the softmax which I would rather not add in the BlockSparseTensor API (all the different types of masking).

Note that for now the backward of masked_matmul is not passing, which is very weird as it was pretty-much a copy-paste of what we had before. This needs to be further investigated.

blefaudeux · 2022-02-04T03:39:36Z

Commenting on the intent already: nice, I think that it would be great to consolidate the attentions !

I can have a look at the tests which don't pass blocksparse wise, and @ptillet was wondering on whether updating the upstream blocksparse kernel in Triton. Worst case we could host it here, the assumption is that the existing known bug (faulty result when row full of zeros) is not too complicated to fix

blefaudeux · 2022-02-04T03:41:31Z

xformers/sparse/blocksparse_tensor.py

+from xformers.ops import masked_matmul
+
+
+class BlockSparseTensor(torch.Tensor):


I like the abstraction personally, I think that we'll need to be watertight on the mask description so that users understand well what are the options, but it consolidates the code nicely and makes a lot of sense (don't swap attentions when all you want is changing the mask)

I agree we will need further documentation and checks.

One thing that I'm still considering is what should the internal representation for the block-sparse be. For now we are just passing whatever triton expects (which internally gets converted to a combination of CSR and COO), but ultimately we would want to have some guidelines on what we should be doing.

Keeping combinations of CSR and COO is fine for block-sparse as it uses less memory per element (as it gets amortized by the block size), but the cost for generic sparsity might be higher so this will need to be weighted.

blefaudeux · 2022-02-04T03:46:30Z

tests/test_sparse_tensors.py

+
+    res_gt.sum().backward()
+    res._blocksparse_values.sum().backward()
+    # TODO: this is not passing!!!


is that only when a row is [0], or do you have other issues ?

The failures here are due to triton-lang/triton#419

@ptillet Are we planning on releasing a new version of 1.1.x sometime soon?

blefaudeux · 2022-02-04T03:47:31Z

tests/test_sparse_tensors.py

+    aa = a.clone()
+    bb = b.clone()
+
+    b = b.transpose(-2, -1)


something which could happen here (not sure) is that the kernel could assume contiguous tensors, and these are not. But even if that was the case it should probably be caught

We check in https://github.com/facebookresearch/xformers/pull/202/files#diff-cca707218d1b441069abce210ffe653d2719bd9fadc51cc90d15a33cd918a2bcR92-R93 that the tensor is contiguous, so we should be fine here

ptillet · 2022-02-04T04:20:01Z

Yep. FWIW the zero-row bug is almost surely an issue with the LUT computation rather than the Triton kernel/compiler.

blefaudeux · 2022-02-04T05:43:27Z

Yep. FWIW the zero-row bug is almost surely an issue with the LUT computation rather than the Triton kernel/compiler.

ohh interesting. Not sure to have the cycles but maybe that I can do a PR tomorrow, unless you can smash that before me ? Thoughts on passing non-contiguous tensors ? I had a quick look and I'm not sure that this case is caught

ptillet · 2022-02-04T08:21:16Z

I can definitely prioritize this. It would be a shame for such relatively minor bugs to turn people off the Triton blocksparse kernels. We've used those internally at OpenAI for a while now -- and so has been Anthropic. We found a bunch of buggs (mostly related to FP32) over the past few months but they've all been fixed in v2.0. What's left to do is probably the zero-row edge case and adding a bunch of asserts.

re:contiguous. I believe Triton blocksparse now converts to contiguous automatically when necessary. There was indeed a bug related to that, but it was fixed within a day of being reported (triton-lang/triton#419).

Gradients are modified in-place, and grad tensor is not checked to be contiguous, yielding wrong results

fmassa · 2022-02-04T12:22:29Z

BTW, @ptillet I found another potential issue with softmax_backward on triton.

Triton performs softmax in-place on the forward, and it also re-uses the grad_output for the location of grad_input in the backward.

While this memory optimization is nice in principle, it doesn't work if the grad_output is non-contiguous (as was the case when I was doing out.sum().backward().

In general, I think it would be preferable to let the user specify if they want to perform the operation in-place or not, as the current in-place operation blocks the user to be able to apply softmax on a leaf Variable

EDIT: looking at triton-lang/triton#419, the problem I'm facing is the same as the reported one, but for softmax, which still has this bug

codecov-commenter · 2022-02-04T12:38:27Z

Codecov Report

Merging #202 (08fb5a8) into main (43eb9c9) will decrease coverage by 0.00%.
The diff coverage is 91.95%.

@@            Coverage Diff             @@
##             main     #202      +/-   ##
==========================================
- Coverage   91.97%   91.97%   -0.01%     
==========================================
  Files          57       58       +1     
  Lines        2929     3128     +199     
==========================================
+ Hits         2694     2877     +183     
- Misses        235      251      +16

Flag	Coverage Δ
Python	`91.97% <91.95%> (-0.01%)`	⬇️

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files	Coverage Δ
xformers/sparse/blocksparse_tensor.py	`91.91% <91.91%> (ø)`
xformers/sparse/__init__.py	`100.00% <100.00%> (ø)`

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update 43eb9c9...08fb5a8. Read the comment docs.

fmassa · 2022-02-05T17:06:59Z

I believe this is ready to be merged.

The current implementation as it stands is fairly fragile as many configurations don't actually work (e.g., when non-contiguous tensors are involved). Some of this is already fixed in triton-lang/triton#419 , but the softmax issue is still present. I've been using the stock Triton 1.1.1 that has been released through pip, and there seems to be several bugs in it that made benchmarking different configurations harder than it should have been.

Nonetheless, on a V100 GPU using Triton 1.1.1, for the forward-pass only on fp32 (backwards will probably be much better but benchmarking it turned out to be harder due to random errors), there doesn't seem to be that much benefits from using Triton version the naive PyTorch operations that I have implemented on this PR.

Here are the results I got:

[------------------------------------- MHA -------------------------------------]
                                                            |  triton  |  pytorch
1 threads: ----------------------------------------------------------------------
      Z=  8, C=  8, H= 512, W= 512, L= 32, sparsity=0.5000  |    2.3   |     2.0
      Z=  8, C=  8, H= 512, W= 512, L= 32, sparsity=0.7000  |    1.2   |     1.5
      Z=  8, C=  8, H= 512, W= 512, L= 32, sparsity=0.9000  |    1.1   |     1.1
      Z= 32, C=  8, H= 512, W= 512, L= 32, sparsity=0.5000  |    5.3   |     6.6
      Z= 32, C=  8, H= 512, W= 512, L= 32, sparsity=0.7000  |    4.0   |     4.9
      Z= 32, C=  8, H= 512, W= 512, L= 32, sparsity=0.9000  |    2.5   |     2.9
      Z=  8, C=  8, H=2048, W=2048, L= 32, sparsity=0.5000  |   18.7   |    23.4
      Z=  8, C=  8, H=2048, W=2048, L= 32, sparsity=0.7000  |   12.8   |    15.0
      Z=  8, C=  8, H=2048, W=2048, L= 32, sparsity=0.9000  |    6.0   |     6.1
      Z=  8, C=  8, H=4096, W=4096, L= 32, sparsity=0.5000  |   72.4   |    90.5
      Z=  8, C=  8, H=4096, W=4096, L= 32, sparsity=0.7000  |   48.6   |    55.7
      Z=  8, C=  8, H=4096, W=4096, L= 32, sparsity=0.9000  |   21.0   |    20.8

Times are in milliseconds (ms).

I can post the benchmark script in the PR if it helps.

blefaudeux · 2022-02-05T21:13:44Z

I believe this is ready to be merged.

The current implementation as it stands is fairly fragile as many configurations don't actually work (e.g., when non-contiguous tensors are involved). Some of this is already fixed in triton-lang/triton#419 , but the softmax issue is still present. I've been using the stock Triton 1.1.1 that has been released through pip, and there seems to be several bugs in it that made benchmarking different configurations harder than it should have been.

Nonetheless, on a V100 GPU using Triton 1.1.1, for the forward-pass only on fp32 (backwards will probably be much better but benchmarking it turned out to be harder due to random errors), there doesn't seem to be that much benefits from using Triton version the naive PyTorch operations that I have implemented on this PR.

Here are the results I got:
[------------------------------------- MHA -------------------------------------]

                                                            |  triton  |  pytorch

1 threads: ----------------------------------------------------------------------

      Z=  8, C=  8, H= 512, W= 512, L= 32, sparsity=0.5000  |    2.3   |     2.0

      Z=  8, C=  8, H= 512, W= 512, L= 32, sparsity=0.7000  |    1.2   |     1.5

      Z=  8, C=  8, H= 512, W= 512, L= 32, sparsity=0.9000  |    1.1   |     1.1

      Z= 32, C=  8, H= 512, W= 512, L= 32, sparsity=0.5000  |    5.3   |     6.6

      Z= 32, C=  8, H= 512, W= 512, L= 32, sparsity=0.7000  |    4.0   |     4.9

      Z= 32, C=  8, H= 512, W= 512, L= 32, sparsity=0.9000  |    2.5   |     2.9

      Z=  8, C=  8, H=2048, W=2048, L= 32, sparsity=0.5000  |   18.7   |    23.4

      Z=  8, C=  8, H=2048, W=2048, L= 32, sparsity=0.7000  |   12.8   |    15.0

      Z=  8, C=  8, H=2048, W=2048, L= 32, sparsity=0.9000  |    6.0   |     6.1

      Z=  8, C=  8, H=4096, W=4096, L= 32, sparsity=0.5000  |   72.4   |    90.5

      Z=  8, C=  8, H=4096, W=4096, L= 32, sparsity=0.7000  |   48.6   |    55.7

      Z=  8, C=  8, H=4096, W=4096, L= 32, sparsity=0.9000  |   21.0   |    20.8



Times are in milliseconds (ms).
I can post the benchmark script in the PR if it helps.

I can check it out this evening, afk at the moment. FYI for Triton blocksparse we even block fp32 and force fp16, with a V100 fp32 cannot use tensor cores so the speed is really slow anyway. I don't think that people using fp32 and blocksparse overlap too much, I would focus on fp16 first ?

blefaudeux · 2022-02-05T21:13:48Z

I believe this is ready to be merged.

The current implementation as it stands is fairly fragile as many configurations don't actually work (e.g., when non-contiguous tensors are involved). Some of this is already fixed in triton-lang/triton#419 , but the softmax issue is still present. I've been using the stock Triton 1.1.1 that has been released through pip, and there seems to be several bugs in it that made benchmarking different configurations harder than it should have been.

Nonetheless, on a V100 GPU using Triton 1.1.1, for the forward-pass only on fp32 (backwards will probably be much better but benchmarking it turned out to be harder due to random errors), there doesn't seem to be that much benefits from using Triton version the naive PyTorch operations that I have implemented on this PR.

Here are the results I got:
[------------------------------------- MHA -------------------------------------]

                                                            |  triton  |  pytorch

1 threads: ----------------------------------------------------------------------

      Z=  8, C=  8, H= 512, W= 512, L= 32, sparsity=0.5000  |    2.3   |     2.0

      Z=  8, C=  8, H= 512, W= 512, L= 32, sparsity=0.7000  |    1.2   |     1.5

      Z=  8, C=  8, H= 512, W= 512, L= 32, sparsity=0.9000  |    1.1   |     1.1

      Z= 32, C=  8, H= 512, W= 512, L= 32, sparsity=0.5000  |    5.3   |     6.6

      Z= 32, C=  8, H= 512, W= 512, L= 32, sparsity=0.7000  |    4.0   |     4.9

      Z= 32, C=  8, H= 512, W= 512, L= 32, sparsity=0.9000  |    2.5   |     2.9

      Z=  8, C=  8, H=2048, W=2048, L= 32, sparsity=0.5000  |   18.7   |    23.4

      Z=  8, C=  8, H=2048, W=2048, L= 32, sparsity=0.7000  |   12.8   |    15.0

      Z=  8, C=  8, H=2048, W=2048, L= 32, sparsity=0.9000  |    6.0   |     6.1

      Z=  8, C=  8, H=4096, W=4096, L= 32, sparsity=0.5000  |   72.4   |    90.5

      Z=  8, C=  8, H=4096, W=4096, L= 32, sparsity=0.7000  |   48.6   |    55.7

      Z=  8, C=  8, H=4096, W=4096, L= 32, sparsity=0.9000  |   21.0   |    20.8



Times are in milliseconds (ms).
I can post the benchmark script in the PR if it helps.

I can check it out this evening, afk at the moment. FYI for Triton blocksparse we even block fp32 and force fp16, with a V100 fp32 cannot use tensor cores so the speed is really slow anyway. I don't think that people using fp32 and blocksparse overlap too much, I would focus on fp16 first ?

blefaudeux

LGTM, Thanks for this @fmassa , looking forward to the next steps ! I think that we need to cover fp16 here, but can be part of the next PR

blefaudeux · 2022-02-06T07:11:02Z

tests/test_sparse_tensors.py

+from xformers.sparse import BlockSparseTensor
+
+cuda_only = pytest.mark.skipif(not torch.cuda.is_available(), reason="requires CUDA")
+_devices = ["cpu", "cuda:0"] if torch.cuda.is_available() else ["cpu"]


I think that the tests should cover fp16 also, most people will use that in fp16 and for some gpus it will follow a different code path (v100 for instance)

I tried running on fp16 but I got consistent segfaults. This might be due to my version of triton, or something else I'm doing wrong. Anyway, I'll let the traceback here if it can be of use.

cc @ptillet

Traceback of the segfault

Thread 1 "python" received signal SIGSEGV, Segmentation fault. 0x00007ffef65bb34f in ?? () from /lib/x86_64-linux-gnu/libnvidia-ptxjitcompiler.so.1 (gdb) bt #0 0x00007ffef65bb34f in ?? () from /lib/x86_64-linux-gnu/libnvidia-ptxjitcompiler.so.1 #1 0x00007ffef65c375f in ?? () from /lib/x86_64-linux-gnu/libnvidia-ptxjitcompiler.so.1 #2 0x00007ffef650b1e4 in ?? () from /lib/x86_64-linux-gnu/libnvidia-ptxjitcompiler.so.1 #3 0x00007ffef650b34f in ?? () from /lib/x86_64-linux-gnu/libnvidia-ptxjitcompiler.so.1 #4 0x00007ffef64e503b in ?? () from /lib/x86_64-linux-gnu/libnvidia-ptxjitcompiler.so.1 #5 0x00007ffef64e5bca in ?? () from /lib/x86_64-linux-gnu/libnvidia-ptxjitcompiler.so.1 #6 0x00007ffef66b2f83 in ?? () from /lib/x86_64-linux-gnu/libnvidia-ptxjitcompiler.so.1 #7 0x00007ffef66b3027 in ?? () from /lib/x86_64-linux-gnu/libnvidia-ptxjitcompiler.so.1 #8 0x00007ffef63a9bf4 in ?? () from /lib/x86_64-linux-gnu/libnvidia-ptxjitcompiler.so.1 #9 0x00007ffef63b2578 in ?? () from /lib/x86_64-linux-gnu/libnvidia-ptxjitcompiler.so.1 #10 0x00007ffef63b67c2 in ?? () from /lib/x86_64-linux-gnu/libnvidia-ptxjitcompiler.so.1 #11 0x00007ffef63b7c2c in ?? () from /lib/x86_64-linux-gnu/libnvidia-ptxjitcompiler.so.1 #12 0x00007ffef63ab05c in __cuda_CallJitEntryPoint () from /lib/x86_64-linux-gnu/libnvidia-ptxjitcompiler.so.1 #13 0x00007fff37951942 in ?? () from /lib/x86_64-linux-gnu/libcuda.so #14 0x00007fff379a010d in ?? () from /lib/x86_64-linux-gnu/libcuda.so #15 0x00007fff37733d7a in ?? () from /lib/x86_64-linux-gnu/libcuda.so #16 0x00007fff376e248e in ?? () from /lib/x86_64-linux-gnu/libcuda.so #17 0x00007fff377a617c in ?? () from /lib/x86_64-linux-gnu/libcuda.so #18 0x00007fff0d671216 in triton::driver::dispatch::cuModuleLoadData(CUmod_st**, void const*) () from /private/home/fmassa/.conda/envs/xformers/lib/python3.8/site-packages/triton/_C/libtriton.so #19 0x00007fff0d6ae865 in cu_load_binary(std::string const&, std::map<std::string, pybind11::object, std::less<std::string>, std::allocator<std::pair<std::string const, pybind11::object> > >&, unsigned long, unsigned long) () from /private/home/fmassa/.conda/envs/xformers/lib/python3.8/site-packages/triton/_C/libtriton.so #20 0x00007fff0d6b21f5 in pybind11::cpp_function::initialize<init_triton_codegen(pybind11::module&&)::{lambda(backend_t, std::string const&, std::map<std::string, pybind11::object, std::less<std::string>, std::allocator<std::pair<std::string const, pybind11::object> > >&, unsigned long, unsigned lo ng)#2}, std::tuple<unsigned long, unsigned long>, backend_t, std::string const&, std::map<std::string, pybind11::object, std::less<std::string>, std::allocator<std::pair<std::string const, pybind11::object> > >&, unsigned long, unsigned long, pybind11::name, pybind11::scope, pybind11::sibling, pybi nd11::return_value_policy>(init_triton_codegen(pybind11::module&&)::{lambda(backend_t, std::string const&, std::map<std::string, pybind11::object, std::less<std::string>, std::allocator<std::pair<std::string const, pybind11::object> > >&, unsigned long, unsigned long)#2}&&, std::tuple<unsigned long , unsigned long> (*)(backend_t, std::string const&, std::map<std::string, pybind11::object, std::less<std::string>, std::allocator<std::pair<std::string const, pybind11::object> > >&, unsigned long, unsigned long), pybind11::name const&, pybind11::scope const&, pybind11::sibling const&, pybind11::r eturn_value_policy const&)::{lambda(pybind11::detail::function_call&)#3}::_FUN(pybind11::detail::function_call) () from /private/home/fmassa/.conda/envs/xformers/lib/python3.8/site-packages/triton/_C/libtriton.so #21 0x00007fff0d6ab6b2 in pybind11::cpp_function::dispatcher(_object*, _object*, _object*) () from /private/home/fmassa/.conda/envs/xformers/lib/python3.8/site-packages/triton/_C/libtriton.so #22 0x00005555556a8348 in cfunction_call_varargs (kwargs=<optimized out>, args=<optimized out>, func=0x7fff3751ed60) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:743 #23 PyCFunction_Call (func=0x7fff3751ed60, args=<optimized out>, kwargs=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:773 #24 0x0000555555697dbc in _PyObject_MakeTpCall (callable=0x7fff3751ed60, args=<optimized out>, nargs=<optimized out>, keywords=0x0) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:159 #25 0x0000555555723666 in _PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x7fff29043b40, callable=0x7fff3751ed60) at /tmp/build/80754af9/python_1618343417471/work/Include/cpython/abstract.h:125 #26 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=0x5555558f4850) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:4963 #27 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:3469 #28 0x00005555556eee3f in function_code_fastcall (globals=<optimized out>, nargs=3, args=<optimized out>, co=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:284 #29 _PyFunction_Vectorcall (kwnames=0x0, nargsf=<optimized out>, stack=0x7fffffffa300, func=0x7fff28e98040) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:411 #30 _PyObject_FastCallDict (kwargs=<optimized out>, nargsf=<optimized out>, args=0x7fffffffa300, callable=0x7fff28e98040) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:96 #31 _PyObject_Call_Prepend (callable=0x7fff28e98040, obj=<optimized out>, args=<optimized out>, kwargs=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:888 #32 0x00005555556eef9a in slot_tp_init (self=0x7fff28f9b910, args=0x7fff28e14440, kwds=0x0) at /tmp/build/80754af9/python_1618343417471/work/Objects/typeobject.c:6790 #33 0x0000555555697d2e in type_call (kwds=0x0, args=0x7fff28e14440, type=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Objects/typeobject.c:994 #34 _PyObject_MakeTpCall (callable=0x555558cf9240, args=<optimized out>, nargs=<optimized out>, keywords=0x0) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:159 #35 0x000055555571f545 in _PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x5555f36a2098, callable=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Include/cpython/abstract.h:125 #36 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=0x5555558f4850) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:4963 #37 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:3500 #38 0x00005555556ed821 in PyEval_EvalFrameEx (throwflag=0, f=0x5555f36a1e10) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:741 #39 _PyEval_EvalCodeWithName (_co=<optimized out>, globals=<optimized out>, locals=<optimized out>, args=<optimized out>, argcount=<optimized out>, kwnames=<optimized out>, kwargs=0x7fff28f84988, kwcount=<optimized out>, kwstep=1, defs=0x0, defcount=0, kwdefs=0x7fff28f009c0, closure=0x0, name=0x7ffff78cc1f0, qualname=0x7fff29b4b8b0) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:4298 #40 0x00005555556ee0a3 in _PyFunction_Vectorcall (func=<optimized out>, stack=0x7fff28f848f0, nargsf=<optimized out>, kwnames=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:436 #41 0x00005555556eec71 in _PyObject_FastCallDict (kwargs=0x7fff29bf0ac0, nargsf=19, args=0x7fff21cbbe90, callable=0x7fff28e985e0) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:104 #42 _PyObject_Call_Prepend (callable=0x7fff28e985e0, obj=<optimized out>, args=<optimized out>, kwargs=0x7fff29bf0ac0) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:888 #43 0x00005555556eef0a in slot_tp_call (self=0x7fff28f9b550, args=0x7fff21ceb040, kwds=0x7fff29bf0ac0) at /tmp/build/80754af9/python_1618343417471/work/Objects/typeobject.c:6556 #44 0x00005555556985fb in PyObject_Call (callable=0x7fff28f9b550, args=0x7fff21ceb040, kwargs=0x7fff29bf0ac0) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:246 #45 0x00005555557210b6 in do_call_core (kwdict=0x7fff29bf0ac0, callargs=0x7fff21ceb040, func=0x7fff28f9b550, tstate=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:5010 #46 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:3559 #47 0x00005555556ed821 in PyEval_EvalFrameEx (throwflag=0, f=0x7fff29c6d800) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:741 #48 _PyEval_EvalCodeWithName (_co=<optimized out>, globals=<optimized out>, locals=<optimized out>, args=<optimized out>, argcount=<optimized out>, kwnames=<optimized out>, kwargs=0x7fff29083740, kwcount=<optimized out>, kwstep=1, defs=0x0, defcount=0, kwdefs=0x0, closure=0x7fff28f3c8c0, name=0x7ffff76b36b0, qualname=0x7fff28e31490) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:4298 #49 0x00005555556ee0a3 in _PyFunction_Vectorcall (func=<optimized out>, stack=0x7fff290836b0, nargsf=<optimized out>, kwnames=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:436 #50 0x0000555555698693 in PyVectorcall_Call (kwargs=<optimized out>, tuple=<optimized out>, callable=0x7fff28f9ae50) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:200 #51 PyObject_Call (callable=0x7fff28f9ae50, args=<optimized out>, kwargs=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:228 #52 0x00005555557210b6 in do_call_core (kwdict=0x7fff29bf0400, callargs=0x7fff2900ab80, func=0x7fff28f9ae50, tstate=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:5010 #53 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:3559 #54 0x00005555556ed270 in PyEval_EvalFrameEx (throwflag=0, f=0x7fff29b74440) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:741 #55 _PyEval_EvalCodeWithName (_co=<optimized out>, globals=<optimized out>, locals=<optimized out>, args=<optimized out>, argcount=<optimized out>, kwnames=0x7fff21cda598, kwargs=0x7fff29083678, kwcount=<optimized out>, kwstep=1, defs=0x0, defcount=0, kwdefs=0x0, closure=0x0, name=0x7ffff78cc1f0, qualname=0x7fff28e1b760) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:4298 #56 0x00005555556ee0a3 in _PyFunction_Vectorcall (func=<optimized out>, stack=0x7fff290835e0, nargsf=<optimized out>, kwnames=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:436 #57 0x00005555556eec71 in _PyObject_FastCallDict (kwargs=0x7fff3751be80, nargsf=19, args=0x7fff21cbbdf0, callable=0x7fff28e98700) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:104 #58 _PyObject_Call_Prepend (callable=0x7fff28e98700, obj=<optimized out>, args=<optimized out>, kwargs=0x7fff3751be80) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:888 #59 0x00005555556eef0a in slot_tp_call (self=0x7fff28f9b760, args=0x7fff2900a700, kwds=0x7fff3751be80) at /tmp/build/80754af9/python_1618343417471/work/Objects/typeobject.c:6556 #60 0x0000555555697dbc in _PyObject_MakeTpCall (callable=0x7fff28f9b760, args=<optimized out>, nargs=<optimized out>, keywords=0x7fff28e79160) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:159 #61 0x00005555557202ab in _PyObject_Vectorcall (kwnames=0x7fff28e79160, nargsf=<optimized out>, args=<optimized out>, callable=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Include/cpython/abstract.h:125 #62 call_function (kwnames=0x7fff28e79160, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:4963 #63 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:3515 #64 0x00005555556edfcb in function_code_fastcall (globals=<optimized out>, nargs=11, args=<optimized out>, co=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:284 #65 _PyFunction_Vectorcall (func=<optimized out>, stack=0x5555e73fccb0, nargsf=<optimized out>, kwnames=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:411 --Type <RET> for more, q to quit, c to continue without paging-- #66 0x00005555556575db in _PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x5555e73fccb0, callable=0x7fff28e7f4c0) at /tmp/build/80754af9/python_1618343417471/work/Include/cpython/abstract.h:127 #67 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=0x5555558f4850) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:4963 #68 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:3500 #69 0x00005555556edfcb in function_code_fastcall (globals=<optimized out>, nargs=21, args=<optimized out>, co=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:284 #70 _PyFunction_Vectorcall (func=<optimized out>, stack=0x7fff29083538, nargsf=<optimized out>, kwnames=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:411 #71 0x00005555556f3a22 in PyVectorcall_Call (kwargs=0x0, tuple=<optimized out>, callable=0x7fff28ff4a60) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:200 #72 PyObject_Call (kwargs=0x0, args=<optimized out>, callable=0x7fff28ff4a60) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:228 #73 PyEval_CallObjectWithKeywords (kwargs=0x0, args=<optimized out>, callable=0x7fff28ff4a60) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:810 #74 PyObject_CallObject (callable=0x7fff28ff4a60, args=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:818 #75 0x00007ffff5c9b608 in THPFunction_apply(_object*, _object*) () from /private/home/fmassa/.conda/envs/xformers/lib/python3.8/site-packages/torch/lib/libtorch_python.so #76 0x00005555556a83d0 in cfunction_call_varargs (kwargs=<optimized out>, args=<optimized out>, func=0x7fff290a5ea0) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:758 #77 PyCFunction_Call (func=0x7fff290a5ea0, args=<optimized out>, kwargs=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:773 #78 0x0000555555697dbc in _PyObject_MakeTpCall (callable=0x7fff290a5ea0, args=<optimized out>, nargs=<optimized out>, keywords=0x0) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:159 #79 0x0000555555723666 in _PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x55555832ddb0, callable=0x7fff290a5ea0) at /tmp/build/80754af9/python_1618343417471/work/Include/cpython/abstract.h:125 #80 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=0x5555558f4850) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:4963 #81 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:3469 #82 0x00005555556eee3f in function_code_fastcall (globals=<optimized out>, nargs=3, args=<optimized out>, co=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:284 #83 _PyFunction_Vectorcall (kwnames=0x0, nargsf=<optimized out>, stack=0x7fffffffb670, func=0x7fff28ff4ca0) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:411 #84 _PyObject_FastCallDict (kwargs=<optimized out>, nargsf=<optimized out>, args=0x7fffffffb670, callable=0x7fff28ff4ca0) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:96 #85 _PyObject_Call_Prepend (callable=0x7fff28ff4ca0, obj=<optimized out>, args=<optimized out>, kwargs=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:888 #86 0x00005555556eef0a in slot_tp_call (self=0x7ffff7820490, args=0x7ffff772e8c0, kwds=0x0) at /tmp/build/80754af9/python_1618343417471/work/Objects/typeobject.c:6556 #87 0x0000555555697dbc in _PyObject_MakeTpCall (callable=0x7ffff7820490, args=<optimized out>, nargs=<optimized out>, keywords=0x0) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:159 #88 0x0000555555723666 in _PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x7fff29084580, callable=0x7ffff7820490) at /tmp/build/80754af9/python_1618343417471/work/Include/cpython/abstract.h:125 #89 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=0x5555558f4850) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:4963 #90 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:3469 #91 0x00005555556ee36b in function_code_fastcall (globals=<optimized out>, nargs=4, args=<optimized out>, co=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:284 #92 _PyFunction_Vectorcall (kwnames=<optimized out>, nargsf=<optimized out>, stack=0x555558b50d48, func=0x7fff28fe21f0) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:411 #93 _PyObject_Vectorcall (kwnames=<optimized out>, nargsf=<optimized out>, args=0x555558b50d48, callable=0x7fff28fe21f0) at /tmp/build/80754af9/python_1618343417471/work/Include/cpython/abstract.h:127 #94 method_vectorcall (method=<optimized out>, args=0x555558b50d50, nargsf=<optimized out>, kwnames=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Objects/classobject.c:60 #95 0x0000555555657a61 in _PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x555558b50d50, callable=0x7fff2a6deb40) at /tmp/build/80754af9/python_1618343417471/work/Include/cpython/abstract.h:127 #96 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=0x5555558f4850) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:4963 #97 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:3469 #98 0x00005555556ed270 in PyEval_EvalFrameEx (throwflag=0, f=0x555558b50b90) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:741 #99 _PyEval_EvalCodeWithName (_co=<optimized out>, globals=<optimized out>, locals=<optimized out>, args=<optimized out>, argcount=<optimized out>, kwnames=0x0, kwargs=0x7fff2a043428, kwcount=<optimized out>, kwstep=1, defs=0x7fff28f1e258, defcount=2, kwdefs=0x0, closure=0x0, name=0x7fff374d4b70, qualname=0x7fff28fc5510) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:4298 #100 0x00005555556ee480 in _PyFunction_Vectorcall (kwnames=<optimized out>, nargsf=<optimized out>, stack=0x7fff2a043400, func=0x7fff28fe2550) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:436 #101 _PyObject_Vectorcall (kwnames=<optimized out>, nargsf=<optimized out>, args=0x7fff2a043400, callable=0x7fff28fe2550) at /tmp/build/80754af9/python_1618343417471/work/Include/cpython/abstract.h:127 #102 method_vectorcall (method=<optimized out>, args=0x7fff2a043408, nargsf=<optimized out>, kwnames=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Objects/classobject.c:60 #103 0x00005555556575db in _PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x7fff2a043408, callable=0x7fff29df0540) at /tmp/build/80754af9/python_1618343417471/work/Include/cpython/abstract.h:127 #104 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=0x5555558f4850) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:4963 #105 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:3500 #106 0x00005555556ed270 in PyEval_EvalFrameEx (throwflag=0, f=0x7fff2a043240) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:741 #107 _PyEval_EvalCodeWithName (_co=<optimized out>, globals=<optimized out>, locals=<optimized out>, args=<optimized out>, argcount=<optimized out>, kwnames=0x0, kwargs=0x7fff36373c00, kwcount=<optimized out>, kwstep=1, defs=0x0, defcount=0, kwdefs=0x0, closure=0x0, name=0x7ffff77364e0, qualname=0x7ffff77364e0) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:4298 #108 0x00005555556ee0a3 in _PyFunction_Vectorcall (func=<optimized out>, stack=0x7fff36373bd8, nargsf=<optimized out>, kwnames=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:436 #109 0x0000555555657a61 in _PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x7fff36373bd8, callable=0x7fff3743b040) at /tmp/build/80754af9/python_1618343417471/work/Include/cpython/abstract.h:127 #110 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=0x5555558f4850) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:4963 #111 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:3469 #112 0x00005555556ed270 in PyEval_EvalFrameEx (throwflag=0, f=0x7fff36373a40) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:741 #113 _PyEval_EvalCodeWithName (_co=<optimized out>, globals=<optimized out>, locals=<optimized out>, args=<optimized out>, argcount=<optimized out>, kwnames=0x0, kwargs=0x7fff2a0ba1f8, kwcount=<optimized out>, kwstep=1, defs=0x7fff28fc85f8, defcount=1, kwdefs=0x0, closure=0x0, name=0x7ffff77322f0, qualname=0x7ffff77322f0) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:4298 #114 0x00005555556ee0a3 in _PyFunction_Vectorcall (func=<optimized out>, stack=0x7fff2a0ba1e0, nargsf=<optimized out>, kwnames=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:436 #115 0x00005555556575db in _PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x7fff2a0ba1e0, callable=0x7fff28fc7790) at /tmp/build/80754af9/python_1618343417471/work/Include/cpython/abstract.h:127 #116 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=0x5555558f4850) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:4963 #117 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:3500 #118 0x00005555556edfcb in function_code_fastcall (globals=<optimized out>, nargs=4, args=<optimized out>, co=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:284 #119 _PyFunction_Vectorcall (func=<optimized out>, stack=0x7ffff78185b0, nargsf=<optimized out>, kwnames=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Objects/call.c:411 #120 0x00005555556575db in _PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x7ffff78185b0, callable=0x7fff29ec93a0) at /tmp/build/80754af9/python_1618343417471/work/Include/cpython/abstract.h:127 #121 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=0x5555558f4850) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:4963 #122 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:3500 #123 0x00005555556ed270 in PyEval_EvalFrameEx (throwflag=0, f=0x7ffff7818440) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:741 #124 _PyEval_EvalCodeWithName (_co=<optimized out>, globals=<optimized out>, locals=<optimized out>, args=<optimized out>, argcount=<optimized out>, kwnames=0x0, kwargs=0x0, kwcount=<optimized out>, kwstep=2, defs=0x0, defcount=0, kwdefs=0x0, closure=0x0, name=0x0, qualname=0x0) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:4298 #125 0x0000555555782543 in PyEval_EvalCodeEx () at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:4327 #126 PyEval_EvalCode (co=<optimized out>, globals=<optimized out>, locals=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Python/ceval.c:718 #127 0x00005555557825e4 in run_eval_code_obj (co=0x7ffff77c4c90, globals=0x7ffff787a9c0, locals=0x7ffff787a9c0) at /tmp/build/80754af9/python_1618343417471/work/Python/pythonrun.c:1165 #128 0x00005555557a8854 in run_mod (mod=<optimized out>, filename=<optimized out>, globals=0x7ffff787a9c0, locals=0x7ffff787a9c0, flags=<optimized out>, arena=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Python/pythonrun.c:1187 #129 0x0000555555669390 in pyrun_file (fp=0x5555558f0340, filename=0x7ffff7848eb0, start=<optimized out>, globals=0x7ffff787a9c0, locals=0x7ffff787a9c0, closeit=1, flags=0x7fffffffc5b8) at /tmp/build/80754af9/python_1618343417471/work/Python/pythonrun.c:1084 #130 0x000055555566c0d2 in pyrun_simple_file (flags=0x7fffffffc5b8, closeit=1, filename=0x7ffff7848eb0, fp=0x5555558f0340) at /tmp/build/80754af9/python_1618343417471/work/Python/pythonrun.c:439 #131 PyRun_SimpleFileExFlags (fp=0x5555558f0340, filename=<optimized out>, closeit=1, flags=0x7fffffffc5b8) at /tmp/build/80754af9/python_1618343417471/work/Python/pythonrun.c:472 #132 0x000055555566cbf0 in pymain_run_file (cf=0x7fffffffc5b8, config=0x5555558f39b0) at /tmp/build/80754af9/python_1618343417471/work/Modules/main.c:391 #133 pymain_run_python (exitcode=0x7fffffffc5b0) at /tmp/build/80754af9/python_1618343417471/work/Modules/main.c:616 #134 Py_RunMain () at /tmp/build/80754af9/python_1618343417471/work/Modules/main.c:695 #135 0x00005555557aba09 in Py_BytesMain (argc=<optimized out>, argv=<optimized out>) at /tmp/build/80754af9/python_1618343417471/work/Modules/main.c:1141 --Type <RET> for more, q to quit, c to continue without paging-- #136 0x00007ffff7db40b3 in __libc_start_main (main=0x55555566d460 <main>, argc=2, argv=0x7fffffffc7b8, init=<optimized out>, fini=<optimized out>, rtld_fini=<optimized out>, stack_end=0x7fffffffc7a8) at ../csu/libc-start.c:308 #137 0x000055555573afe5 in _start () at ../sysdeps/x86_64/elf/start.S:103

fmassa · 2022-02-06T16:33:29Z

Merging, I'll iterate on improving the overall pipeline on follow-up PRs

ptillet · 2022-02-07T19:51:39Z

FYI, I've merged a bunch of fixes in triton blocksparse that should take care of the issues mentioned (and also improve performance on triangular matrices)

…oder [CI] Unit test vs. Pytorch Encoder and Decoder 1/2

fmassa added 5 commits January 26, 2022 06:37

Add initial version of Blocksparse tensor

6b0ff2a

Add initial tests plus improvements

ef27f63

Lint

1faec9d

headers

a04c6d1

Add more tests

4db37a9

fmassa requested review from blefaudeux and dianaml0 February 2, 2022 18:36

facebook-github-bot added the CLA Signed This label is managed by the Facebook bot. Authors need to sign the CLA before a PR can be reviewed. label Feb 2, 2022

fmassa marked this pull request as draft February 2, 2022 18:37

blefaudeux reviewed Feb 4, 2022

View reviewed changes

Fix sparse_sotmax_backward test

6667da9

Gradients are modified in-place, and grad tensor is not checked to be contiguous, yielding wrong results

fmassa added 2 commits February 5, 2022 05:28

Add CPU implementation

0bf0059

Remove unused line

08fb5a8

fmassa marked this pull request as ready for review February 5, 2022 15:07

blefaudeux approved these changes Feb 6, 2022

View reviewed changes

fmassa merged commit 1ba0f5c into main Feb 6, 2022

fmassa deleted the blocksparse_refactoring_v2 branch February 6, 2022 16:33

fmassa mentioned this pull request Feb 6, 2022

[feat]Adding a simple blockwise attention #192

Closed

10 tasks

fmassa mentioned this pull request Feb 6, 2022

Cleanup sparse tests #208

Merged

blefaudeux mentioned this pull request Feb 7, 2022

[fix] blocksparse sanity checks #207

Merged

10 tasks

xwhan pushed a commit to xwhan/xformers that referenced this pull request Feb 8, 2022

Merge pull request facebookresearch#202 from fairinternal/pytorch_enc…

ce63eb3

…oder [CI] Unit test vs. Pytorch Encoder and Decoder 1/2

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Create BlockSparse Tensor #202

Create BlockSparse Tensor #202

fmassa commented Feb 2, 2022

blefaudeux commented Feb 4, 2022

blefaudeux Feb 4, 2022

fmassa Feb 4, 2022

blefaudeux Feb 4, 2022

fmassa Feb 4, 2022 •

edited

Loading

blefaudeux Feb 4, 2022

fmassa Feb 4, 2022

ptillet commented Feb 4, 2022

blefaudeux commented Feb 4, 2022

ptillet commented Feb 4, 2022 •

edited

Loading

fmassa commented Feb 4, 2022 •

edited

Loading

codecov-commenter commented Feb 4, 2022 •

edited

Loading

fmassa commented Feb 5, 2022 •

edited

Loading

blefaudeux commented Feb 5, 2022

blefaudeux commented Feb 5, 2022

blefaudeux left a comment

blefaudeux Feb 6, 2022

fmassa Feb 6, 2022

fmassa commented Feb 6, 2022

ptillet commented Feb 7, 2022

		from xformers.ops import masked_matmul


		class BlockSparseTensor(torch.Tensor):

Create BlockSparse Tensor #202

Create BlockSparse Tensor #202

Conversation

fmassa commented Feb 2, 2022

What does this PR do?

blefaudeux commented Feb 4, 2022

blefaudeux Feb 4, 2022

Choose a reason for hiding this comment

fmassa Feb 4, 2022

Choose a reason for hiding this comment

blefaudeux Feb 4, 2022

Choose a reason for hiding this comment

fmassa Feb 4, 2022 • edited Loading

Choose a reason for hiding this comment

blefaudeux Feb 4, 2022

Choose a reason for hiding this comment

fmassa Feb 4, 2022

Choose a reason for hiding this comment

ptillet commented Feb 4, 2022

blefaudeux commented Feb 4, 2022

ptillet commented Feb 4, 2022 • edited Loading

fmassa commented Feb 4, 2022 • edited Loading

codecov-commenter commented Feb 4, 2022 • edited Loading

Codecov Report

fmassa commented Feb 5, 2022 • edited Loading

blefaudeux commented Feb 5, 2022

blefaudeux commented Feb 5, 2022

blefaudeux left a comment

Choose a reason for hiding this comment

blefaudeux Feb 6, 2022

Choose a reason for hiding this comment

fmassa Feb 6, 2022

Choose a reason for hiding this comment

fmassa commented Feb 6, 2022

ptillet commented Feb 7, 2022

fmassa Feb 4, 2022 •

edited

Loading

ptillet commented Feb 4, 2022 •

edited

Loading

fmassa commented Feb 4, 2022 •

edited

Loading

codecov-commenter commented Feb 4, 2022 •

edited

Loading

fmassa commented Feb 5, 2022 •

edited

Loading