llama : add new API functions to support encoder-decoder models: llama_encode(), llama_model_has_encoder(), llama_model_decoder_start_token()
common, llama-cli : use new API functions to support encoder-decoder models
convert-hf : handle shared token embeddings tensors in T5Model
convert-hf : handle SentencePiece BPE tokenizer in T5Model (for Pile-T5 models)
convert-hf : add MT5ForConditionalGeneration and UMT5ForConditionalGeneration to architectures supported by T5Model
* support splits in convert.py
* Support split by size and dry run to write estimated shards/filesizes
* Move split functionality to new GGUFManager class
* fix improper function signature
* tentative push of convert-hf-to-gguf support
* resolve merge + SplitArguments for easier parsing
* Fix eager tensor memory leak and remove convert.py changes
Removed a memory leak caused by unexpected reference retention to eager tensors.
Also removed GGUFManager functionality in convert.py in favor of specializing for convert-hf-to-gguf.py.
* refactor SplitStrategy to be a deque
Instead of having SplitStrategy have a `data` field that is a deque, just have SplitStrategy be a subclass of deque itself.
* fix Q8 quantization
* remove unnecessary imports in gguf_manager
* fix final? merge issue
* fix gguf_writer placement and remove comments
* oops, actually fix gguf_writer placement
* reduce duplicated code from gguf_writer
* further simplify GGUFManager
* simplify even further and standardize with GGUFWriter
* reduce diffs with master
* form shards while adding tensors, SHA256 sums agree with master
* re-add type hint
Co-authored-by: compilade <git@compilade.net>
* GGUFWriter compatibility fix
Co-authored-by: compilade <git@compilade.net>
* Shard dataclass and un-negative dont_add_architecture
* type consistency in format_n_bytes_to_str
* move kv keys to constants.py
* make pathlib explicit
* base-1024 bytes to base-1000
* rename GGUFManager to GGUFWriterSplit
* Update gguf-py/gguf/constants.py
Co-authored-by: compilade <git@compilade.net>
* fix convert-hf-to-gguf.py permissions
* fix line endings
* Update gguf-py/gguf/gguf_writer_split.py
Co-authored-by: compilade <git@compilade.net>
* convert-hf : restore executable file permission
* examples/convert-legacy-llama.py: restore executable file permission
* reinstate original gguf package import and fix type annotation
* attempt to appease the linter
* attempt 2 to appease the linter
* attempt 3 to appease the linter
* comma consistency
* Update convert-hf-to-gguf.py
Co-authored-by: compilade <git@compilade.net>
* edit cmd line args
* use simplification from #7827
* kv/ti data are still wrong
* try to refactor kv data (still fails)
* fix ti data messiness
* tidy up
* fix linting
* actually make the linter happy
* cleanup round 1
* remove SplitStrategy, SplitArguments
* appease linter
* fix typing and clean up
* fix linting
* Update gguf-py/gguf/gguf_writer.py
Co-authored-by: compilade <git@compilade.net>
* progress bar, fix split logic
* Update gguf-py/gguf/gguf_writer.py
Co-authored-by: compilade <git@compilade.net>
* catch oversights
* Update gguf-py/gguf/gguf_writer.py
Co-authored-by: compilade <git@compilade.net>
* Update gguf-py/gguf/gguf_writer.py
Co-authored-by: compilade <git@compilade.net>
* Update gguf-py/gguf/gguf_writer.py
Co-authored-by: compilade <git@compilade.net>
* Update gguf-py/gguf/gguf_writer.py
Co-authored-by: compilade <git@compilade.net>
* Update gguf-py/gguf/gguf_writer.py
Co-authored-by: compilade <git@compilade.net>
* swap bar orders
* Update gguf-py/gguf/gguf_writer.py
Co-authored-by: compilade <git@compilade.net>
* Update gguf-py/gguf/gguf_writer.py
Co-authored-by: compilade <git@compilade.net>
* compatibility fix
* Update gguf-py/gguf/gguf_writer.py
Co-authored-by: compilade <git@compilade.net>
* Update convert-hf-to-gguf.py
Co-authored-by: compilade <git@compilade.net>
---------
Co-authored-by: Brian <mofosyne@gmail.com>
Co-authored-by: compilade <git@compilade.net>
* gguf-py : add T5 model architecture
* gguf-py : add separate tensors for encoder and decoder
* gguf-py : add new model header parameters: decoder_start_token_id, attention.relative_buckets_count, tokenizer.ggml.remove_extra_whitespaces, tokenizer.ggml.precompiled_charsmap
* convert-hf : add model conversion support for T5ForConditionalGeneration and T5WithLMHeadModel
---------
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* 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
* support for Poro chat pre-tokenizer
* add support for Poro pre-tokenizer
* Update convert-hf-to-gguf-update.py
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Change Poro-34B-chat to poro-chat
* Change Poro-34B-chat to poro-chat
* Update convert-hf-to-gguf-update.py
* Update llama.cpp
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
In #7075, to fix the conversion of (some) models using model-00001-of-00001.safetensors instead of model.safetensors for a single model part we simply used the same logic as the part count to get the part names.
But this doesn't always work correctly, like when unusual additional model files like consolidated.safetensors in https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3 are present.
This commit matching both the prefix and the suffix of the model part names should fix this problem without breaking any previously-supported upstream models. But according to report by @teleprint-me there is still some
persistent problem, but shall do in the meantime.
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
* 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
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Add optional MLP bias for Granite models
Add optional MLP bias for ARCH_LLAMA to support Granite models.
Partially addresses ggerganov/llama.cpp/issues/7116
Still needs some more changes to properly support Granite.
* llama: honor add_space_prefix from the model configuration
propagate the add_space_prefix configuration from the HF model
configuration to the gguf file and honor it with the gpt2 tokenizer.
Signed-off-by: Giuseppe Scrivano <gscrivan@redhat.com>
* llama: add support for small granite models
it works only for the small models 3b and 8b.
The convert-hf-to-gguf.py script uses the vocabulary size of the
granite models to detect granite and set the correct configuration.
Signed-off-by: Giuseppe Scrivano <gscrivan@redhat.com>
---------
Signed-off-by: Giuseppe Scrivano <gscrivan@redhat.com>
Co-authored-by: Steffen Roecker <sroecker@redhat.com>
* 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
---------
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* 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
---------
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* convert-hf : add conversion of bloom-style qkv tensor to gpt-style qkv (code borrowed from BloomModel)
* llama : add inference support for LLM_ARCH_GPTNEOX
* llama : add model types for every Pythia variant and GPT-NeoX
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* 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>
* Update brute force test: add_special
* Update brute force test: default values for add_bos_token and add_eos_token
* Enable rtrim when pre-inserting BOS
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Revert "server : fix test regexes"
* Update brute force test: special tokens
* Fix added tokens
- Try to read 'added_tokens.json'.
- Try to read 'tokenizer_config.json'.
- Try to read 'tokenizer.json'.
* Fix special tokens rtrim
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* server : fix test regexes
* convert-hf-to-gguf-update: automate updating
* convert-hf-to-gguf-update: improve download
* share requests session for performance
* create directories only when needed, don't skip downloads when empty directory encountered
* be more graceful about errors
* 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.
* 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
* 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>
* 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
* 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
* Support Llama 3 conversion
The tokenizer is BPE.
* style
* Accept suggestion
Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com>
* llama : add llama_token_is_eog()
ggml-ci
* llama : auto-detect more EOT tokens when missing in KV data
* convert : replacing EOS token is a hack
* llama : fix codegemma EOT token + add TODOs
* llama : fix model type string for 8B model
---------
Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
