bfa7c8fc01
Implement the module trait directly for QMatMul. ( #1372 )
2023-11-25 10:09:45 +00:00
cddfc3944c
Add the q8k vec-dot multiplication. ( #1019 )
2023-10-02 21:53:34 +01:00
089fc3b584
Improve the quantized whisper setup. ( #1018 )
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* Improve the quantized whisper setup.
* Fix the config file paths.
* Use the standard matmul where possible.
2023-10-02 17:17:46 +01:00
263a172202
Improve the testing of the optimized quantized vec-dot ops ( #1016 )
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* Expose the unopt functions for testing.
* Better testing of the optimized quantized computations.
2023-10-02 09:50:43 +01:00
4e55aaa51f
Simd128 version of the q2k-q8k vecdot product. ( #1011 )
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* Sketch the simd128 version of q2k vecdot.
* Use a single accumulator.
* Simdify the q2k-q8k vecdot product.
* Cosmetic change.
2023-09-30 20:12:41 +01:00
5320aa6b7d
Move the test-utils bits to a shared place. ( #619 )
2023-08-27 09:42:22 +01:00
a8b39dd7b7
Fix for q5_1 quantization. ( #617 )
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* Fix for q5_1 quantization.
* Fix some typos.
2023-08-27 08:31:18 +01:00
fa0d75b18d
Quantization tests + fix some issues. ( #616 )
2023-08-27 08:17:38 +01:00
c72eb3d75b
Add reference implementation for q4k and q5k ( #586 )
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* add `q2k` vec-dot
* `q3k` vec-dot + quantization bugfix
* `q4k` vec-dot
* `q5k` vec-dot
* Validate against GGML unit test results.
* Remove some more `transmutes`
2023-08-26 12:07:54 +01:00
d2f42ab086
Referenze implementations of q2k and q3k vec-dot functions ( #580 )
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* add `q2k` vec-dot
* `q3k` vec-dot + quantization bugfix
2023-08-24 12:35:54 +01:00
7478dda255
Cosmetic tweaks. ( #570 )
2023-08-23 15:45:40 +01:00
075b505480
Mirror GGML's unit tests ( #569 )
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* Add ggml unit tests
* simplify random matmul test for other test cases
2023-08-23 15:25:17 +01:00
352383cbc3
Add quantization support for q2k, q3k, q4k and q5k ( #524 )
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* first q2 implementation
* First Q4K and Q5K implementations
* fix `q2k` and `q5k`
* Some first cleanups
* run `clippy` on tests
* finally implement `q3k`
* deactivate `q3k` test on macos
* also disable the test on linux
* Fix floating bits in `q3k` dequantization
* Refactoring pass + reorder quants in file
* `fmt`
* Re-add `src` asserts and redefine `dst`
2023-08-22 15:04:55 +01:00
a22b1bed7b
Tensor -> QTensor conversion ( #496 )
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* Sketch some qmatmul test.
* Add the quantization function.
* More testing.
* Make the test smaller and faster.
* Add some shape checking.
2023-08-18 08:19:20 +01:00
557b2c28dd
Q6K quantization ( #495 )
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* Print the detected arch options.
* Add the q6k quantization.
* Add a currently broken test.
* Bugfix.
* Bugfix.
* Another bugfix.
* Another bugfix + get the test to work.
2023-08-17 22:22:57 +01:00
306c8eee7a
AVX version of the vecdot for q4_0. ( #474 )
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* AVX version of the vecdot for q4_0.
* Tweak the avx bits.
* Add a qmatmul benchmark.
* Fix the quantized test.
2023-08-17 07:03:32 +01:00
098909de40
Add vecdot for q6k-q8k. ( #476 )
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* Add vecdot for q6k-q8k.
* Add some testing for q8k.
* Use QMatMul for the output layer.
2023-08-16 20:59:40 +01:00
575e88a999
Add a quantized test that use negative values. ( #470 )
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* Add a quantized test that use negative values.
* Add a default tokenizer.
2023-08-16 16:32:58 +01:00
3071134788
Get the ggml based llama to generate some text. ( #464 )
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* Add more stats to the ggml example.
* Build a quantized model from the file content.
* Move the tensor retrieval in the main crate.
* Start adding the forward pass.
* Add more to the forward pass of the quantized llama.
* Apply the attention layers.
* Add the sampling loop.
* Get the sampling loop to work.
* Minor tweak.
* Add a quantize/dequantize test.
* Bugfix.
* Add a comment + swap the order.
* Bugfixes.
2023-08-16 12:41:07 +01:00
965597a873
Add a test for qmatmul. ( #459 )
2023-08-16 06:36:27 +01:00
e68b2accb4
Split out the quantized file. ( #456 )
2023-08-15 20:26:27 +01:00
