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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* lora : fix llama conversion script with ROPE_FREQS
* convert : refactor rope_freqs generation
This should also fix vocab-only conversion for Phi-3.
* convert : adapt MiniCPM3 to separate rope_freqs insertion
MiniCPM3's tokenizer is treated as a SentencePiece tokenizer to avoid
having to run its custom Python code which mixes tokenization
in the same file as tool calls.
gguf-py : add long and short RoPE factors to tensor mappings
Empty, but the key names are used to populate the mappings.
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Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Co-authored-by: Francis Couture-Harpin <git@compilade.net>
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* conflict resolution
* Changes to make work and add longrope support
* Changes to n_attention_wv rule
* Untested support of 253B
* DeciLMCausalModel now reads rope_theta from config.json properly
* Remove errant Granite mentions
* Better n_attention_vw rule
* Update vocab.py
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Co-authored-by: Yee Man Chan <ymchan@gmail.com>
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* Update GGMLQuantizationType
* Update LlamaFileType
* Update GGML_QUANT_SIZES
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* add support for bitnet2b_2501 model
* Fixes
* Support both model names
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Co-authored-by: potassiummmm <zhou.hansong@outlook.com>
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* WIP Gemma3: not working
* gemma3: build_gemma3 seems to be working now
* Revert changes to convert_hf_to_gguf.py
It wasn't working, so I guess, it is better to leave the
conversion up tp upstream.
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* Deepseek MLA Optimizations
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* Make MLA optional
* Remove some unnecessary copies in the MLA attention
* Deepseek MLA Optimizations V2 (#195)
* Avoid allocating MHA KV cache when MLA is turned on
* Added missing gguf-py file
* Added final optimizations
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* Make sure we do have wk_b and wv_b before enabling MLA
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Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Use type_k and type_v to set the types of the MLA caches
They were hard-coded at f16.
On my Ryzen-7950X with native bf16 support I get a fairly
significant PP performance boost with bf16 KV-cache:
PP-4096 = 320 t/s up from 292 t/s with fp16 KV-cache.
* Better gemm strategy when nth > nhead
It gives a ~10% PP performance boost for DeepSeek-Lite with 32 threads
(with or without MLA).
Before this commit, when nth > nhead heads were processed
sequentially with all nth threads participating in each
matrix multiplication. Now we ind the gcd of nhead and
nth and split threads into nth/gcd groups, each group
processing nhead/gcd heads.
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Co-authored-by: Saood Karim <saood05@gmail.com>
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
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* Merge mainline
* Fix after merge
* Remove CI check
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* Merging mainline - WIP
* Merging mainline - WIP
AVX2 and CUDA appear to work.
CUDA performance seems slightly (~1-2%) lower as it is so often
the case with llama.cpp/ggml after some "improvements" have been made.
* Merging mainline - fix Metal
* Remove check
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* update: convert-hf-to-gguf.py to support Qwen2-57B-A14B
* fix: QWEN2MOE support for expert_feed_forward_length
previously, expert ff was taken from n_ff (intermediate size) but it is now properly taken from LLM_KV_EXPERT_FEED_FORWARD_LENGTH
n_ff_exp and n_ff_shared_exp are now properly calculated
* update: convert-hf-to-gguf.py cleanup for Qwen2MoeForCausalLM
* fix: QWEN2MOE support for expert_feed_forward_length
previously, expert ff was taken from n_ff (intermediate size) but it is now properly taken from LLM_KV_EXPERT_FEED_FORWARD_LENGTH
n_ff_exp and n_ff_shexp are now properly calculated
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* gguf-dump.py: add --markdown dump output
* gguf-dump.py: Add toc
* gguf-dump.py: use standard tensor name lookup. Also add tensor ID field
* gguf-dump.py: Add tensor overview count
* gguf-dump.py: fix array preview
* gguf-dump.py: markdownTableWithAlignmentSupport() added
* Add type hints and spacing
Co-authored-by: compilade <git@compilade.net>
* gguf-dump.py: prettyfy dimention
* gguf-dump: right align element count
* gguf-dump.py: element count autosizing
* Apply suggestions from code review
Co-authored-by: compilade <git@compilade.net>
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Co-authored-by: compilade <git@compilade.net>
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Main changes of this PR is to consolidate GGUFWriter.add_key and GGUFWriter.add_val into GGUFWriter.add_key_value.
In addition use_temp_file is now opt-in instead of opt-out defaulting to False.
Also GGUFWriter now does not require output file name until when actually writing to it.
And GGUFWriter doesn't really need to eagerly prepare the data layout of the metadata
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* feat: add changes to handle jina v2 base code
* fix: do not complicate things
* fix: fix the usage of the code model
* fix: fix comments
* fix: fix linting issues
* fix: remove ollama patches
* style : minor
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Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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* support lm_head
* remove the code block
---------
Co-authored-by: zhangkaihuo <zhangkaihuo@modelbest.cn>
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* Move convert.py to examples/convert-no-torch.py
* Fix CI, scripts, readme files
* convert-no-torch -> convert-legacy-llama
* Move vocab thing to vocab.py
* Fix convert-no-torch -> convert-legacy-llama
* Fix lost convert.py in ci/run.sh
* Fix imports
* Fix gguf not imported correctly
* Fix flake8 complaints
* Fix check-requirements.sh
* Get rid of ADDED_TOKENS_FILE, FAST_TOKENIZER_FILE
* Review fixes
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* common : increase max number of experts to 160
* common : add tensors ATTN_Q_A, ATTN_Q_A_NORM, ATTN_Q_B, ATTN_KV_A_MQA, ATTN_KV_A_NORM, ATTN_KV_B needed by DeepSeek-V2 MLA (multi-head latent attention) architecture
* common : add model header parameters: leading_dense_block_count, expert_feed_forward_length, expert_shared_count, expert_weights_scale, attention.q_lora_rank, attention.kv_lora_rank, rope.scaling.yarn_log_multiplier
* convert-hf : add model conversion support for DeepseekV2ForCausalLM
* llama : add model types for DeepSeek-V2 and DeepSeek-V2-Lite models
* llama : add two new llm_build_moe_ffn() arguments: scale_w (whether to scale weights of selected MoE experts) and w_scale (numerical value of the scaling factor)
* llama : add inference support for LLM_ARCH_DEEPSEEK2
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Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
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* gguf-py : fix and simplify quantized shape round-trip
* gguf-py : remove unused import
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* common : increase max number of experts to 128
* common : add tensor LLM_TENSOR_FFN_NORM_EXPS for normalization before MoE that runs in parallel to attention + ffn
* gguf-py : add architecture-specific block mappings that override selected general block mappings
* convert-hf : add model conversion support for ArcticForCausalLM
* convert-hf : use added_tokens_decoder from tokenizer_config.json to redefine tokens from SentencePiece model (only for ArcticForCausalLM)
* llama : add inference support for LLM_ARCH_ARCTIC
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Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
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* ggml : drop support for QK_K=64
ggml-ci
* opencl : restore QK_K=256 define
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* add phi3 128k support in convert-hf-to-gguf
* add phi3 128k support in cuda
* address build warnings on llama.cpp
* adjust index value in cuda long rope freq factors
* add long rope support in ggml cpu backend
* make freq factors only depend on ctx size
* remove unused rope scaling type 'su' frin gguf converter
* fix flint warnings on convert-hf-to-gguf.py
* set to the short freq factor when context size is small than trained context size
* add one line of comments
* metal : support rope freq_factors
* ggml : update ggml_rope_ext API to support freq. factors
* backends : add dev messages to support rope freq. factors
* minor : style
* tests : update to use new rope API
* backends : fix pragma semicolons
* minor : cleanup
* llama : move rope factors from KV header to tensors
* llama : remove tmp assert
* cuda : fix compile warning
* convert : read/write n_head_kv
* llama : fix uninitialized tensors
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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* llama : remove Persimmon
* requirements : remove
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* convert-hf : support q8_0 conversion
* convert-hf : add missing ftype
This was messing with the checksums otherwise.
* convert-hf : add missing ftype to Baichuan and Xverse
I didn't notice these on my first pass.
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* convert-hf : support bfloat16 conversion
* gguf-py : flake8 fixes
* convert-hf : add missing space after comma
* convert-hf : get bit-exact same output as ./quantize
The quantization version was missing.
* convert-hf : don't round bf16 NANs
* convert-hf : save some memory with np.int16 intermediate bf16 weights
* convert-hf : more closely match llama.cpp with which weights to keep in f32
* convert-hf : add --outtype auto-f16
A reason for this to exist is for model quantizers who want an initial
GGUF with the most fidelity to the original model while still using
a 16-bit float type instead of 32-bit floats.
* convert-hf : remove a semicolon because flake8 doesn't like it
It's a reflex from when programming in C/C++, I guess.
* convert-hf : support outtype templating in outfile name
* convert-hf : rename --outtype auto-f16 to --outtype auto
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* feat: first things to do
* feat: create tensors for Jina architecture
* fix: use other tensors
* feat: embedding gets results
* fix: fix usage of ALIBI
* fix: clean prints
* fix: do some cleanup unused vars
* fix: revert changes to Makefile and CMakeLists
* fix: revert some changes
* fix: fix small detail
* fix: fix convert formatting
* fix: fix linting and editor
* feat: set proper vocab settings
* fix: JinaBertForMaskedLM registration
* feat: support q_normalization and k_normalization in Jina arch
* feat: handle gpt2 tokenizer with Jina architecture
* feat: example comments in embedding
* feat: rename Jina Bert to Jina Bert V2
* fix: add some changes as per review
* feat: proper KQ_pos for Jina embeddings
* feat: add capacity to load models ES and DE for Spanish
* llama : fix pre-tokenizers
* ggml : full ALiBi support
* ggml : update ggml_soft_max_ext() CUDA, SYCL
* ggml : ggml_flash_attn_ext() support ALiBi (CPU)
* ggml : ggml_flash_attn_ext() support ALiBi (Metal)
* ggml : fix warning
* ggml : ggml_flash_attn_ext() support ALiBi (CUDA)
ggml-ci
* minor : clean-up
* embedding : add warning about missing SEP
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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* ggml : full ALiBi support
* ggml : update ggml_soft_max_ext() CUDA, SYCL
* ggml : ggml_flash_attn_ext() support ALiBi (CPU)
* ggml : ggml_flash_attn_ext() support ALiBi (Metal)
* ggml : fix warning
* ggml : ggml_flash_attn_ext() support ALiBi (CUDA)
ggml-ci
* ggml : fix assert message
* vulkan : add dev notes
* ggml : require mask when using ALiBi
ggml-ci
* convert : fix convert for refact models
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* Add special token modification capability
To be able to fix/amend special tokens in a GGUF let's add two new arguments:
* `--special-token <name> <value>` where `<name>` can be bos, eos, prefix, middle, etc. while `<value>` is the token value, f.ex. `"<｜fim▁begin｜>"`
* `--special-token-by-id <name> <id>` where `<id>` is the ID of the token, f.ex. 32006
So, in order to f.ex. add fill-in-middle tokens to a GGUF you would do the following:
```bash
python3 gguf-new-metadata.py input.gguf output.gguf --special-token prefix "<｜fim▁begin｜>" --special-token middle "<｜fim▁hole｜>" --special-token suffix "<｜fim▁end｜>"
```
* improve help text
* flake--
* fix multiple tokens warning
* make script executable
* switch to namedtuple, no need to dataclass
* typing++
* add progress bar
* Add special token modification capability
To be able to fix/amend special tokens in a GGUF let's add two new arguments:
* `--special-token <name> <value>` where `<name>` can be bos, eos, prefix, middle, etc. while `<value>` is the token value, f.ex. `"<｜fim▁begin｜>"`
* `--special-token-by-id <name> <id>` where `<id>` is the ID of the token, f.ex. 32006
So, in order to f.ex. add fill-in-middle tokens to a GGUF you would do the following:
```bash
gguf-new-metadata.py input.gguf output.gguf --special-token prefix "<｜fim▁begin｜>" --special-token middle "<｜fim▁end｜>" --special-token suffix "<｜fim▁hole｜>"
```
(yes, fim_end is the `middle` token, because completion is a `prefix`/`suffix`/`middle` sequence (where `middle` is unfilled))
or
```bash
gguf-new-metadata.py input.gguf output.gguf --special-token prefix "<fim_prefix>" --special-token middle "<fim_middle>" --special-token suffix "<fim_suffix>"
```
etc...
NB: The tokens have to exist already, trying to add non-existent token name/IDs will be ignored (with a warning), while non-existent values will fail (with an error).
* improve help text
* flake--
* fix multiple tokens warning
* make script executable
* switch to namedtuple, no need to dataclass
* typing++
* add progress bar
* fail on invalid token id
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* convert-hf : begin refactoring write_tensor
* convert : upgrade to sentencepiece v0.2.0
* convert-hf : remove unused n_dims in extra_*_tensors
* convert-hf : simplify MoE weights stacking
* convert-hf : flake8 linter doesn't like semicolons
* convert-hf : allow unusual model part names
For example, loading `model-00001-of-00001.safetensors` now works.
* convert-hf : fix stacking MoE expert tensors
`torch.stack` and `torch.cat` don't do the same thing.
* convert-hf : fix Mamba conversion
Tested to work even with a SentencePiece-based tokenizer.
* convert : use a string for the SentencePiece tokenizer path
* convert-hf : display tensor shape
* convert-hf : convert norms to f32 by default
* convert-hf : sort model part names
`os.listdir` is said to list files in arbitrary order.
Sorting the file names should let "model-00009-of-00042.safetensors"
be loaded before "model-00010-of-00042.safetensors".
* convert-hf : use an ABC for Model again
It seems Protocol can't be used as a statically type-checked ABC,
because its subclasses also can't be instantiated. (why did it seem to work?)
At least there's still a way to throw an error when forgetting to define
the `model_arch` property of any registered Model subclasses.
* convert-hf : use a plain class for Model, and forbid direct instantiation
There are no abstract methods used anyway,
so using ABC isn't really necessary.
* convert-hf : more consistent formatting of cmdline args
* convert-hf : align the message logged for converted tensors
* convert-hf : fix Refact conversion
* convert-hf : save memory with lazy evaluation
* convert-hf : flake8 doesn't like lowercase L as a variable name
* convert-hf : remove einops requirement for InternLM2
* convert-hf : faster model parts loading
Instead of pre-loading them all into a dict, iterate on the tensors
in the model parts progressively as needed in Model.write_tensors
Conversion for some architectures relies on checking for the presence
of specific tensor names, so for multi-part models, the weight map is read
from the relevant json file to quickly get these names up-front.
* convert-hf : minor changes for consistency
* gguf-py : add tqdm as a dependency
It's small, and used for a progress bar
in GGUFWriter.write_tensors_to_file
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* Introduce bfloat16 support
Many models on Hugging Face (e.g. Mistral, TinyLLaMA) use bfloat16 as
their canonical floating point format.
┌sign
│
│ ┌exponent
│ │
│ │ ┌mantissa
│ │ │
│┌──┴───┐┌─┴───┐
0b0000000000000000 brain16
This encoding has the same number of exponent bits as float32. That
makes conversion relatively straightforward, even in the absence of
hardware support. For example, converting brain16 to binary32 means
simply shifting 16 bits to the left.
┌sign
│
│ ┌exponent
│ │
│ │ ┌mantissa
│ │ │
│┌──┴───┐┌─┴───────────────────┐
0b00000000000000000000000000000000 IEEE binary32
The issue is that converting bf16 to fp16 can result in information
loss. Only 13% of bf16 numbers can be precisely represented in fp16
which in practice ends up being 99.71% of Mistral 7b v0.2's weights
however there is currently no way other than fp32 to get the others
┌sign
│
│ ┌exponent
│ │
│ │ ┌mantissa
│ │ │
│┌─┴─┐┌─┴──────┐
0b0000000000000000 IEEE binary16
This change fixes that, by adding a bf16 data type to GGML. Support
for CPU inference has been implemented along with optimizations for
the AVX2, AVX512, and AVX512BF16 ISAs. Perplexity on Mistral 7b 0.2
improves somewhere around -0.0024 to -0.0046 compared to using fp16
* Remove GGML code that's not needed
* Minimize the GGML API surface area for BF16
* Remove bf16 luts
* Make the GGML header look nicer
* Fix documentation
* Apply ggerganov's fixes for test-backend-ops
* Add BF16 code for new ggml_validate_row_data() function
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* convert.py: add python logging instead of print()
* convert.py: verbose flag takes priority over dump flag log suppression
* convert.py: named instance logging
* convert.py: use explicit logger id string
* convert.py: convert extra print() to named logger
* convert.py: sys.stderr.write --> logger.error
* *.py: Convert all python scripts to use logging module
* requirements.txt: remove extra line
* flake8: update flake8 ignore and exclude to match ci settings
* gh-actions: add flake8-no-print to flake8 lint step
* pre-commit: add flake8-no-print to flake8 and also update pre-commit version
* convert-hf-to-gguf.py: print() to logger conversion
* *.py: logging basiconfig refactor to use conditional expression
* *.py: removed commented out logging
* fixup! *.py: logging basiconfig refactor to use conditional expression
* constant.py: logger.error then exit should be a raise exception instead
* *.py: Convert logger error and sys.exit() into a raise exception (for atypical error)
* gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar
* verify-checksum-model.py: This is the result of the program, it should be printed to stdout.
* compare-llama-bench.py: add blank line for readability during missing repo response
* reader.py: read_gguf_file() use print() over logging
* convert.py: warning goes to stderr and won't hurt the dump output
* gguf-dump.py: dump_metadata() should print to stdout
* convert-hf-to-gguf.py: print --> logger.debug or ValueError()
* verify-checksum-models.py: use print() for printing table
* *.py: refactor logging.basicConfig()
* gguf-py/gguf/*.py: use __name__ as logger name
Since they will be imported and not run directly.
* python-lint.yml: use .flake8 file instead
* constants.py: logger no longer required
* convert-hf-to-gguf.py: add additional logging
* convert-hf-to-gguf.py: print() --> logger
* *.py: fix flake8 warnings
* revert changes to convert-hf-to-gguf.py for get_name()
* convert-hf-to-gguf-update.py: use triple quoted f-string instead
* *.py: accidentally corrected the wrong line
* *.py: add compilade warning suggestions and style fixes
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* merged the changes from deepseeker models to main branch
* Moved regex patterns to unicode.cpp and updated unicode.h
* Moved header files
* Resolved issues
* added and refactored unicode_regex_split and related functions
* Updated/merged the deepseek coder pr
* Refactored code
* Adding unicode regex mappings
* Adding unicode regex function
* Added needed functionality, testing remains
* Fixed issues
* Fixed issue with gpt2 regex custom preprocessor
* unicode : fix? unicode_wstring_to_utf8
* lint : fix whitespaces
* tests : add tokenizer tests for numbers
* unicode : remove redundant headers
* tests : remove and rename tokenizer test scripts
* tests : add sample usage
* gguf-py : reader prints warnings on duplicate keys
* llama : towards llama3 tokenization support (wip)
* unicode : shot in the dark to fix tests on Windows
* unicode : first try custom implementations
* convert : add "tokenizer.ggml.pre" GGUF KV (wip)
* llama : use new pre-tokenizer type
* convert : fix pre-tokenizer type writing
* lint : fix
* make : add test-tokenizer-0-llama-v3
* wip
* models : add llama v3 vocab file
* llama : adapt punctuation regex + add llama 3 regex
* minor
* unicode : set bomb
* unicode : set bomb
* unicode : always use std::wregex
* unicode : support \p{N}, \p{L} and \p{P} natively
* unicode : try fix windows
* unicode : category support via std::regex
* unicode : clean-up
* unicode : simplify
* convert : add convert-hf-to-gguf-update.py
ggml-ci
* lint : update
* convert : add falcon
ggml-ci
* unicode : normalize signatures
* lint : fix
* lint : fix
* convert : remove unused functions
* convert : add comments
* convert : exercise contractions
ggml-ci
* lint : fix
* cmake : refactor test targets
* tests : refactor vocab tests
ggml-ci
* tests : add more vocabs and tests
ggml-ci
* unicode : cleanup
* scripts : ignore new update script in check-requirements.sh
* models : add phi-3, mpt, gpt-2, starcoder
* tests : disable obsolete
ggml-ci
* tests : use faster bpe test
ggml-ci
* llama : more prominent warning for old BPE models
* tests : disable test-tokenizer-1-bpe due to slowness
ggml-ci
---------
Co-authored-by: Jaggzh <jaggz.h@gmail.com>
Co-authored-by: Kazim Abrar Mahi <kazimabrarmahi135@gmail.com>
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* not allow adding duplicated tensor name
* no duplicated tensor while reading gguf
* typo
* throw exception inside llama_model_loader
Co-authored-by: slaren <slarengh@gmail.com>
---------
Co-authored-by: slaren <slarengh@gmail.com>
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* add explicit phi3 support
* add explicit phi3 support
* remove unused code
* convert : add BOS token
* llama : match EOT token <|end|>
* llama : minor / style
* llama : tabs -> spaces
* convert : fix lint checks
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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* implement olmo architecture
* remove unused variable
* remove unused moe branch
* remove check for weight
* remove superfluous moe, bias and rope tensors
* clarified comment
* fix clamp_kqv setting
* remove obsolete parameter name filter
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* Support converting models with multiple chat templates
Adds the following metadata:
* tokenizer.chat_templates
* tokenizer.chat_template.<name1>
* tokenizer.chat_template.<name2>
* tokenizer.chat_template.<...>
Where `tokenizer.chat_templates` is an array of the template names (except `default`), `default` is added to the regular `tokenizer.chat_template`.
* replace filtered characters with underscore
* New script to add/modify/remove metadata
This scripts creates a copy of a GGUF file and allows you to add/modify/remove metadata in the process.
Most importantly this allows you to update chat templates, either as a string or directly from an updated tokenizer_config.json file.
* Add files via upload
add new script to project/readme
* flake--
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* StableLM2 12B support for huggingface -> GGUF
* StableLM12 tensormapping and constants
* StableLM-2-12b model support
* fix
* Added 12B support
* Removed autoformatting; resolved bug where model_arch was not selecting StableLM2
* Formatting
* Do QK norm stacking in model conversion step
* Converge StableLM and StableLM2 code to simplify graph construction
* Fix accidental removal
* Removed warnings
* Revert formatter
* Move QK norm stack to private function so it's easier to read
* refactor stablelm graph builder to support 1.6, 3b and 12b more efficiently
* Proper check for None type for new_name to avoid crash; formatting; revert change to base class `write_tensors()`
* Format
* Formatting
* format
Co-authored-by: compilade <git@compilade.net>
* Fix incorrect check for K norm
* space after commas; Keep indentation multiple of 4 spaces
* Flake8 format
* Removed unnecessary conditional branches
* Removed unused comment
* Fixed incorrect tensor passing
* Format
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Co-authored-by: compilade <git@compilade.net>
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* support qwen2moe
* fix-review
* metal : support unary ops for nelements % 4 != 0
* metal : require contiguousness for float4 unary kernels
* metal : require contiguousness for float4 unary kernels (cont)
* fix-review
* names : for brevity "SHARED_EXP" -> "SHEXP"
* llama : reuse build_moe_ffn()
* llama : add model type name
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Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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This commit adds special token metadata for Fill-In-the-Middle
(FIM)/Infill to the GGUF model.
The motivation for this is that currently there is support for CodeLlama
but other models exist now like CodeGemma, but the different models use
different token ids for the special tokens and this commit allows for
supporting multiple models.
Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
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* model: dbrx convert to gguf
#6344
* llama: support dbrx
#6344
* doc: dbrx: add the model as supported
* scripts: get-wikitext-2 add unzip
* llama: increase maximum experts allowed
* llama: factorize moe graph implementation between grok, mixtral and dbrx
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Co-authored-by: Megha Agarwal <16129366+megha95@users.noreply.github.com>
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* Add Command R Plus GGUF
* Add Command R Plus GGUF
* Loading works up to LayerNorm2D
* Export new tensors in 1D so they are not quantized.
* Fix embedding layer based on Noeda's example
* Whitespace
* Add line
* Fix unexpected tokens on MPS. Re-add F16 fix. ((Noeda)
* dranger003: Fix block index overflow in CUDA dequantizing.
* Reverted blocked multiplication code as it still has issues and could affect other Llama arches
* export norms as f32
* fix overflow issues during quant and other cleanup
* Type convention
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* dranger003: Fix more int overflow during quant.
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Co-authored-by: S <seast@Ss-Mac-Studio.local>
Co-authored-by: S <s@example.com>
Co-authored-by: slaren <slarengh@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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* initial commit for sealion support
* add sealion support
* minor fix
* q/k ln and pos_embd only if required
* Apply suggestions from code review
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* minor : clear whitespaces
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Co-authored-by: bryan <bryansiow@aisingapore.org>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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* ggml : update mul_mat_id to use the same tensor for all the experts
* update cuda
* minor
* update metal
* update test-backend-ops
* fix cuda
* Update ggml-metal.m
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* update convert.py
* update convert-hf-to-gguf.py
* update convert.py for mixtral hf models
* Update convert-hf-to-gguf.py
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* cuda : support non-pow-2 number of experts
* allow quantize to work for split and merged experts models in the same way
* cleanup + disable mmap automatically with split tensors models
* update imatrix
* test-backend-ops : test qwen argsort
* update grok model loading
* llama : add merged experts tensors to the grok tensor map
* minor
* gguf : bump version
* fix quantizing of merged experts
* convert-hf-to-gguf.py : update grok (untested)
* make linter happy
* cuda/argsort : use shared memory instead of pool memory
* convert : fix grok tensor names
* metal : add support for non-pow-2 argsort
* llama : more loader cleanup, better error checking
* cuda : fix warning
* llama : still use mmap for loading old models, but copy the data to a host buffer
* add review note
* llama : remove ffn tensor counting + add sanity check
ggml-ci
* convert : fix handling of n_experts == None
ggml-ci
* imatrix : fix ncall counters
* llama : produce error if imatrix size does not match
* quantize : terminate on errors + trace logs
ggml-ci
* metal : pad shared memory to 16 bytes
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Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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* Support xverse model convert to gguf format.
* 1. Convert xverse models to gguf;
2. Add LLM_ARCH_XVERSE inference in llama.cpp;
3. Add xverse item in Supported models in README.md;
* * gguf-py: remove redundant logs
* llama: remove the init_mapping_prefetch custom parameter
* llama.cpp: Include the changes from #6122 to exclude the unused outputs of the last layers.
* - Fix format issues
- Remove duplicate set kqv_out to llm_build_kv
* Update llama.cpp
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Co-authored-by: willhe <willhe@xverse.cn>
Co-authored-by: willhe <hexin@xverse.cn>
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* iq1_m: basics
* iq1_m: basics-2
* iq1_m: CUDA dequantize works
Very 1st shot I get PPL = 9.76 for LLaMA-v2-7B.
* iq1_m: separate shifts for each group of 8 in a block
We get
PPL(LLaMA-v2-7B ) = 9.2810
PPL(LLaMA-v2-13B) = 6.8105
Not bad, but slightly higher than
sqrt(PPL(IQ1_S) * PPL(IQ2_XXS))
which is the expected outcome given that IQ1_M is
halfway between IQ1_S and IQ2_XXS in terms of bpw.
From this, we would expect
PPL = 9.14 for LLaMA-v2-7B
PPL = 6.63 for LLaMA-v2-13B
* iq1_m: go to 3-bit scales
There is slight increase in PPL, but the 0.0625 bpw reduction
in size is totally worth it.
We now have
PPL(LLaMA-v2-7B ) = 9.4469 at 1.96 bpw
PPL(LLaMA-v2-13B) = 6.8717 at 1.93 bpw
PPL(LLaMA-v2-70B) = 4.8568 at 1.85 bpw
* iq1_m: scalar dot product
* iq1_m: AVX2 dot product
* iq1_m: very slightly faster AVX2 dot product
* iq1_m: ARM_NEON dot product
Works, but very slow (10.5 t/s)
* iq1_m: Metal - dequantize works, dot product does not
* iq1_m: Metal now works
About the same performance as iq1_s.
* iq1_m: minor
* iq1_m: checking pure iq1_m quantization
It is pretty bad: PPL(LLaMA-v2-7B) = 34 if we quantize output.weight
with Q4_K.
* iiq1_m: slightly faster ARM_NEON dot product
10.5 t/s -> 11.65 t/s
* iq1_m: faster ARM_NEON dot product
11.65 t/s -> 14.9 t/s
* iq1_m: another minor ARM_NEON dot product improvement
14.9 -> 15.0 t/s
* iq1_m: small PPL improvement via super-block scale adjustment
After quantizing block scales redo the super-block scale fit.
PPL(LLaMA-v2-7B ) = 9.3346
PPL(LLaMA-v2-13B) = 6.8419
PPL(LLaMA-v2-70B) = 4.8294
PPL(Mistral-7B ) = 8.1624
* iq1_m: adapt to CUDA refactoring
* iq1_m: remove unused variable
We have progressed to warnings being errors.
* iq1_m: add to backend-ops tests
* iq1_m: fix Windows ARM
* iq1_m: use common definition of iq1m_scale_t
* cuda: assert -> NO_DEVICE_CODE
* iq1_M: PR comments
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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