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* ggml : Q4_2 ARM
* ggml : add ggml_is_quantized()
* llama : update llama_type_name() with Q4_2 entry
* ggml : speed-up q4_2
- 4 threads: ~100ms -> ~90ms
- 8 threads: ~55ms -> ~50ms
* ggml : optimize q4_2 using vmlaq_n_f32 + vmulq_n_f32
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Had a background process that was messing with the timings
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On my Mac, the direct Q4_1 product is marginally slower
(~69 vs ~55 us for Q4_0). The SIMD-ified ggml version
is now almost 2X slower (~121 us).
On a Ryzen 7950X CPU, the direct product for Q4_1 quantization
is faster than the AVX2 implementation (~60 vs ~62 us).
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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this came up when trying to convert the gpt4all-lora-unfiltered-quantized.bin file
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* Replaced static initialization of complex objects with a initialization on first use. This prevents an undefined behavior on program run, for example, crash in Release build, works in Debug build
* replaced use of auto with exact type to avoid using -std=c++14
* Made the assessors functions for static maps be static const
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Calling `mmap.mmap` on Windows apparently resets the file offset of the
raw file object (and makes the BufferedReader return a *negative* file
offset). For safetensors, avoid using the file offset after calling
mmap. For GGML format, explicitly save and restore the offset.
Fixes #966.
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* ggml : add Q8_0 quantization for intermediate results
* quantize-stats : fix test + add it to Makefile default
* Q8: use int8_t, AVX/AVX2 optimizations
* ggml : fix quantize_row_q8_0() ARM_NEON rounding
* minor : updates after rebase to latest master
* quantize-stats : delete obsolete strings
* ggml : fix q4_1 dot func
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Co-authored-by: Stephan Walter <stephan@walter.name>
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This reverts commit f4d277ae17247ee51129ef1a9ff74d377cc90b1b.
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Avoid duplication of type names in utils
Co-authored-by: Håkon H. Hitland <haakon@likedan.net>
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* Add support for configs, add configurable prefixes / suffixes, deprecate instruct mode, add stop prompt
* Add multiline mode, update text input.
* bugfix
* update implementation
* typos
* Change --multiline implementation to be toggled by EOF.
* bugfix
* default multiline mode
* add more configs
* update formating
* update formatting
* apply suggestions
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* GGML map ops proof of concept.
* Various cleanups.
Add handling for task setting.
Add handling for ggml_compute_backward.
Rename functions to ggml_map_unary_f32 and ggml_map_binary_f32
Fix compiler warnings related to casting function pointers and `void *`
Reorder functions and definitions based on the GGML op number.
Use typedefs for map op function pointer types.
* Fix position of map ops cases in ggml_compute_forward
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after #545 we do not need torch, tqdm and requests in the dependencies
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Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
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* Add support to batch size for perplexity
* Revert "Fix memory allocation issues and seg faults"
This reverts commit 4870e455b3653f7d7769fa5772b2c90ffad088df.
* update from merge
* Remove perplexity from main
* updates
* Update batch size for efficiency
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* ggml : speed-up q4_1 ARM_NEON by ~5%
* ggml : implement vaddvq when missing
* ggml : implement vminvq and vmaxvq when missing
* ggml : implement vzip when missing
* ggml : fix comment
* ggml : try to use correct ifdef
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Hide it behind an #ifdef
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which allows us to use aligned_alloc or _aligned_malloc functions
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* chore: add nodejs binding
* chore: add nodejs binding
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