* Add the mmv kernels for smaller sizes.
* Support more mmv kernels.
* Use the new kernels.
* Fix the call.
* Silly fix.
* Improve the testing.
* Fix for dmmv.
* Add another dedicated test for the batching mmv.
* Fix for the batch dim in the quantized matmul example.
* Enable more tests on cuda.
* Add a test for qmm with a batch.
* Fix the zeros-dim test on metal.
* add the sign unary operator
* remove uneeded import
* remove uneeded import
* undo formatting
* undo formatting
* remove unnecessary redefintion
* allow gradient to flow through for sign and round
* fix cpu ops to ensure that negzero and positive zero are handled properly
* clippy fixes
* Properly avoid gradient tracking.
* Use a branchless version.
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Co-authored-by: laurent <laurent.mazare@gmail.com>
* Avoid copying the data on squeeze and unsqueeze.
* Fix the quantized llama example.
* Unrelated fix for the quantized stable-lm example on cuda.
* Fix for mamba on cuda (unrelated to the PR).
* first attempt
* progress
* integrate into metal backend
* finish and get test passing
* add other dtype support
* update transpose1d dtypes supported
* first pass at implementation of maxpool2d
* Add definitions for other dtypes
* add tests for other dtypes
* Cosmetic tweaks + re-enable maxpool2d tests for metal.
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Co-authored-by: Laurent <laurent.mazare@gmail.com>
* Add a specialized kernel for copy2d.
* Move the cat operations.
* Avoid transpositions in cat.
* Bugfix.
* Bugfix for the cuda kernel.
* Add a benchmark.
* Add more testing.
* Test fix.
* Faster kernel.
* Add the missing kernel.
* Tweak the test.
* Add a metal kernel.
* Fix for the metal kernel.
* Get the tests to pass on metal.
* Also use this opportunity to fix the metal kernel for ELU.
* Add some bf16 kernels.
* Clippy fixes.
* use_resource API misunderstood. It is not additive. Several usages must be bit-ORed together.
* The seeding was incorrect and used the address instead of the value of the passed in seed.
* Add a check that likely exhibits failure to update the seed between generation of random tensors.
* Buffer overrun, the length given to the std::ptr::copy call was in bytes, and not 32-bit units.
* By default seed the RNG with a time-based value, so that different runs may produce different output, just like the CPU engine.
Use device.set_seed if determinism is warranted.
* Revert "By default seed the RNG with a time-based value, so that different runs may produce different output, just like the CPU engine. Use device.set_seed if determinism is warranted."
This reverts commit d7302de9
Discussion in https://github.com/huggingface/candle/pull/1811#issuecomment-1983079119
* The Metal random kernel failed to set element N/2 of tensors with N elements, N being even. The reason was that all threads but thread 0 all created 2 random samples, but thread 0 only one, i.e. an odd number. In order to produce an even number of samples, the early termination of thread 0 should only everr occur for odd sized tensors.
* Add a test catching any deterministic tensor element in rand and randn output.
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Co-authored-by: niklas <niklas@appli.se>
Co-authored-by: Ivar Flakstad <69173633+ivarflakstad@users.noreply.github.com>
* Add support for loading Fortran contiguous tensors
This commit introduces the ability to handle Fortran contiguous tensors in the tensor loading process. Previously, the code only supported loading tensors that were contiguous in memory, failing with an error for non-contiguous tensors. With this update, tensors identified as Fortran contiguous (column-major order) are now correctly handled by reversing their dimensions after loading. This enhancement ensures broader compatibility with different tensor layouts, improving the robustness of tensor loading operations.
- Check if a tensor is Fortran contiguous using the `is_fortran_contiguous` flag.
- For Fortran contiguous tensors, reverse the dimensions after loading to correctly represent their layout in memory.
- Continue to bail out with an error for tensors that are neither C contiguous nor Fortran contiguous, maintaining the previous behavior for non-contiguous tensors without explicit support.
This change addresses the issue of loading Fortran contiguous tensors, which was previously unsupported, thereby extending the functionality of the tensor loading mechanism to accommodate a wider variety of tensor layouts.
* Add reshape step to handle fortran contiguous case
* Skip fortran contiguous fix if rank is < 2
* Fail on rank 0, 1 if contiguous
* Metal quantized modifications proposal.
- Add a device param, wherever needed.
- Create new QMetal storage thing that implements QuantizedType.
- Update everywhere needed.
Fix Python.
Fixing examples.
Fix: fmt + clippy + stub.
Moving everything around.
Only missing the actual implems.
Fixing everything + adding dequantized kernels.
More work.
Fixing matmul.
Fmt + Clippy
Some clippy fixes.
Working state.
Q2K Metal -> Bugged (also present in GGML).
Q4K CPU -> Bugged (present previously, new test catch it).
Q5K CPU -> Bugged (present previously).
Q8_1 Both -> Never really implemented it seems
Q8K metal -> Never implemented in metal
Fixing Q2K bug (present in ggml).
* Cleanup.
* Fix the rebase.
* Removing the fences speeds everything up and *is* correct this time...
* Cleanup the fence.
* After rebase.
* Bad code removal.
* Rebase after phi2 merge + fix replit default to CPU.
* Making the CI happy.
* More happy tests.
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Co-authored-by: Nicolas Patry <nicolas@Nicolass-MacBook-Pro.local>
* Simpler repro for the neon optimization issue.
* Bugfix for q4k.
* Improve the fix, share the dot-prod bit.
* Clippy fixes.
* Fix for q6k.
* Also fix for q2k.
* Use the new shared dotprod.
* Add more testing.
* encode size of upsample in enum
* working convolution method for limited 2d kernels
* add test for sf 3 interpolation
* add higher dimensional tests, fix to work with multichannel input
* Remove commented out line.
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Co-authored-by: Laurent <laurent.mazare@gmail.com>
