* 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>
* Update the Phi model to use the updated architecture.
* Add more of the phi model.
* Repeat KV + caching.
* Apply the rotary embeddings.
* Add support for the new phi model in the phi example.
* Fix a couple glitches.
* Fix a couple more glitches.
* Use cfg to seperate benchmark results based on features
* Add metal where_cond for f16 and bf16. Add benchmark
* Remove allow pragma
* Avoid some unnecessary returns.
* Improve benchmarks layout
* Updated feature separated benchmarks
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Co-authored-by: Laurent <laurent.mazare@gmail.com>
* Use cfg to seperate benchmark results based on features
* Remove allow pragma
* Avoid some unnecessary returns.
* Improve benchmarks layout
* Derive bench_name from actual device
* Run CPU benchmarks even when GPU feature is enabled
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Co-authored-by: Laurent <laurent.mazare@gmail.com>
* Add RepVGG model.
* Add RepVGG README
* Extract var to top level
* Replace hashmap with a match
* Add a variant for the model kind + avoid some unnecessary config cloning.
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Co-authored-by: Laurent <laurent.mazare@gmail.com>
* Add relu kernel for metal
* Copy error messages proposed in #1491
* Revert non relu changes
* Fix name changes
* Fix the last of us (:
* Fix copy and paste mistakes
* Fix typo
* Revert order changes
* Revert order change
* Add deleted functions back
* Run rustfmt
* feat: add dependabot to the project
* feat: add let's accept patches/fix from other libs
* Revert "feat: add let's accept patches/fix from other libs"
This reverts commit d31a956f81.
* 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.
* add one-hot encoding
* one_hot: improve error handling, use generic to_vecN::<D>
Bails if the index value is equal to or greater than the depth value,
which would result in an out-of-bounds error.
A redundant check is added to ensure the index value does not exceed
the length of the one-hot matrix size, which would also result in an
out-of-bounds error.
Bails if the index value is less than -1. If the index value is -1,
then it ignores the setting of the on_value for the index value. Only
values that are less than -1 are considered errors.
* one-hot: use two generics, one_hot::<I, O>, for input and output data types
Separating the input and output data types allows the input tensor
indices to be a different data type than the output encoded tensor data type.
For example, one_hot::<i64, u8>(...) will take an input tensor of i64 values
and encode the output tensor using u8 values.
The generic I::DTYPE must match the data type of the input indices, otherwise
the method will bail.
Additionally, this method adds an `allow_f64` option to enable the input indices
data type to be f64 values. f64 values are disabled by default.
TODO: indices data type and the generic I data type are currently not compile-time
checked.
* one_hot: remove input generic, use indices dtype matching
This commit removes the to_f64() type cast and explicitly
matches the DType from the input tensor. Currently, only U8,
U32 and I64 is supported for input tensors.
The match arms on the dtype is verbose. It would be nice
to use a generic type with the WithDtype traitbound to
pass to the to_vecN method and then return an inner value.
Open to suggestions for better approaches here to reduce
the match arm verbosity.
* one_hot: use flat_map iterator over dims instead of nested for loop
This commit replaces the nested for loops with an flat map iter over
the dimensions of the input tensor.
This commit also adds a test for a rank 3 input tensor.
* one_hot: use mandatory on/off-values, remove const msgs
This commit also updates doc tests, comments and test cases.
* Small cleanups.
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Co-authored-by: laurent <laurent.mazare@gmail.com>
