* batched embedding: pool outputs by sequence id. updated embedding example
* bring back non-causal attention
* embd : minor improvements
* llama : minor
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* BERT model graph construction (build_bert)
* WordPiece tokenizer (llm_tokenize_wpm)
* Add flag for non-causal attention models
* Allow for models that only output embeddings
* Support conversion of BERT models to GGUF
* Based on prior work by @xyzhang626 and @skeskinen
---------
Co-authored-by: Jared Van Bortel <jared@nomic.ai>
Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* support minicpm arch.
* fix tab/space typo.
* convert minicpm model via convert-hf-gguf.py
* try to make tokenizer work
* fix bug for quantize minicpm
* fix for flake8 lint
* remove convert-minicpm.py
* fix for editorconfig
* correct minicpm model type (size)
* constants expanded for minicpm
* Minor change of the constant names for minicpm
* Add n_key_dim and n_value_dim
Some models use values that are not derived from `n_embd`.
Also remove `n_embd_head` and `n_embd_gqa` because it is not clear
which "head" is referred to (key or value).
Fix issue #4648.
* Fix `llm_build_kqv` to use `n_value_gqa`
* Rebase
* Rename variables
* Fix llm_build_kqv to be more generic wrt n_embd_head_k
* Update default values for n_embd_head_k and n_embd_head_v
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Fix llm_load_tensors: the asserts were not backcompat
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Otherwise, on Windows converting bling-phi-2-v0 (<https://huggingface.co/llmware/bling-phi-2-v0>) via convert-hf-to-gguf.py will fail with the following error:
```
Traceback (most recent call last):
File "C:\Users\User\git\gguf\convert-hf-to-gguf.py", line 1061, in <module>
model_instance.set_vocab()
File "C:\Users\User\git\gguf\convert-hf-to-gguf.py", line 52, in set_vocab
self._set_vocab_gpt2()
File "C:\Users\User\git\gguf\convert-hf-to-gguf.py", line 264, in _set_vocab_gpt2
special_vocab = gguf.SpecialVocab(dir_model, load_merges=True)
File "C:\Users\User\git\gguf\gguf\vocab.py", line 33, in __init__
self._load(Path(path))
File "C:\Users\User\git\gguf\gguf\vocab.py", line 81, in _load
self._try_load_merges_txt(path)
File "C:\Users\User\git\gguf\gguf\vocab.py", line 95, in _try_load_merges_txt
for line in fp:
File "C:\Users\User\miniconda3\envs\gguf\lib\encodings\cp1252.py", line 23, in decode
return codecs.charmap_decode(input,self.errors,decoding_table)[0]
UnicodeDecodeError: 'charmap' codec can't decode byte 0x81 in position 1415: character maps to <undefined>
```
* convert : support Mixtral as LLAMA arch
* convert : fix n_ff typo
* llama : model loading
* ggml : sync latest ggml_mul_mat_id
* llama : update graph to support MoE
* llama : fix cur -> cur_expert
* llama : first working version
* llama : fix expert weighting in the FFN
* ggml : ggml_get_rows support 2D indexing [n_tokens, n_experts] (cpu only)
* ggml : add n_as argument to ggml_mul_mat_id
* ggml : fix ggml_get_rows to take into account ne02 / ne11
* metal : add more general support for ggml_get_rows + tests
* llama : add basic support for offloading moe with CUDA
* metal : add/mul/div use general kernel when src1 not cont
* metal : reduce the kernel launches for ggml_mul_mat_id
* ggml : get_rows : support non-contiguos tensors with gaps, generalize up to 3D
* ggml : update get_rows f16 and q
* cuda : support non-contiguous src1 in get_rows
* llama : offload missing ffn_moe_silu
* metal : fix ggml_get_rows to work with non-cont src1
* metal : add indirect mat-vec kernels for all quantization types
* llama : do not quantize expert gating tensors
* llama : add n_expert and n_expert_used to hparams + change quants
* test-backend-ops : add moe test
* cuda : fix get_rows when ncols is odd
* convert : determine n_ctx correctly
* metal : fix ggml_mul_mat_id for F32
* test-backend-ops : make experts more evenly probable (test_moe)
* test-backend-ops : cleanup, add moe test for batches
* test-backend-ops : add cpy from f32 -> all types test
* test-backend-ops : fix dequantize block offset
* llama : fix hard-coded number of experts
* test-backend-ops : simplify and disable slow tests to avoid CI timeout
* test-backend-ops : disable MOE test with thread sanitizer
* cuda : fix mul_mat_id with multi gpu
* convert : use 1e6 rope_freq_base for mixtral
* convert : fix style
* convert : support safetensors format
* gguf-py : bump version
* metal : add cpy f16 -> f32 kernel
* metal : fix binary ops for ne10 % 4 != 0
* test-backend-ops : add one more sum_rows test
* ggml : do not use BLAS with ggml_mul_mat_id
* convert-hf : support for mixtral-instruct (#4428)
* convert : typo fix, add additional hyperparameters, use LLaMA arch for Mixtral-instruct
* convert : use sentencepiece tokenizer for Mixtral-instruct
* convert : make flake8 happy
* metal : fix soft_max kernels
ref: 1914017863
* metal : limit kernels to not use more than the allowed threads
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Radek Pilar <github@mrkva.eu>
Disabled rules:
* E203 Whitespace before ':' - disabled because we often use 'C' Style where values are aligned
* E211 Whitespace before '(' (E211) - disabled because we often use 'C' Style where values are aligned
* E221 Multiple spaces before operator - disabled because we often use 'C' Style where values are aligned
* E225 Missing whitespace around operator - disabled because it's broken so often it seems like a standard
* E231 Missing whitespace after ',', ';', or ':' - disabled because we often use 'C' Style where values are aligned
* E241 Multiple spaces after ',' - disabled because we often use 'C' Style where values are aligned
* E251 Unexpected spaces around keyword / parameter equals - disabled because it's broken so often it seems like a standard
* E261 At least two spaces before inline comment - disabled because it's broken so often it seems like a standard
* E266 Too many leading '#' for block comment - sometimes used as "section" separator
* E501 Line too long - disabled because it's broken so often it seems like a standard
* E701 Multiple statements on one line (colon) - broken only in convert.py when defining abstract methods (we can use# noqa instead)
* E704 Multiple statements on one line - broken only in convert.py when defining abstract methods (we can use# noqa instead)
* gguf-py: gguf-dump: Respect --no-tensor flag in JSON mode.
* Respect add_bos_token GGUF metadata value
* gguf-py: Try to fix SpecialVocab giving up too easily for the Nth time
* gguf-py: Refactor and add file reading support
* Replay changes from #3871
Credit to @cebtenzzre for that pull
* Various type annotation fixes.
* sort imports with isort (again)
* Fix missing return statement in add_tensor
* style cleanup with flake8
* fix NamedTuple and Enum usage
* Fix an issue with state init in GGUFReader
Move examples to an examples/ directory
Clean up examples
Add an example of modifying keys in a GGUF file
Update documentation with info on examples
Try to support people importing gguf/gguf.py directly
* Damagage is not a word.
* Clean up gguf-py/examples/modify_gguf.py whitespace
Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com>
* Update gguf-py/examples/modify_gguf.py formatting
Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com>
* Update gguf-py/gguf/gguf_reader.py type hint
Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com>
* Make examples executable, formatting changes
* Add more information to GGUFReader and examples comments
* Include a gguf Python package version bump
* Add convert-gguf-endian.py script
* cleanup
* gguf-py : bump minor version
* Reorganize scripts
* Make GGUFReader endian detection less arbitrary
* Add JSON dumping support to gguf-dump.py
Which I kind of regret now
* A few for gguf-dump.py cleanups
* Murder accidental tuple in gguf-py/scripts/gguf-dump.py
Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com>
* cleanup
* constants : remove unneeded type annotations
* fix python 3.8 compat
* Set up gguf- scripts in pyproject.toml
* And include scripts/__init__.py, derp
* convert.py: We can't currently support Q8_0 on big endian.
* gguf-py: SpecialVocab: Always try available sources for special token ids
gguf-py: SpecialVocab: Try to load merges from merges.txt if not in tokenizer.json
gguf-py: SpecialVocab: Add 'add_bos_token' type bools to GGUF metadata
u
* cleanup
* Promote add_X_token to GGUF metadata for BOS and EOS
---------
Co-authored-by: Jared Van Bortel <jared@nomic.ai>
Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com>
* Add validation for special token ids to llama.cpp
Small optimization for llama_byte_to_token SPM mode
* Fix BPE newline check, only I could break something so simple
* Killll meeeeee
* Account for GGUF_KEY_KEY only setting when the key exists
* Minor code cleanups.
* Fix convert.py error msg when added tokens are out of range
* Make gguf SpecialVocab vocab size-aware
Update conversion scripts accordingly
* Avoid a string copy
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* check whether platform is 390x if yes->do not import immintrin.h
* support s390x big endian
* support --bigendian option for s390x
1. verified with baichuan7b-chat with float 16 on s390x
2. verified with baichuan7b-chat
3. verified with chinese-alpaca-2-13b-f16
* update format based on editor-config checker result
* Update convert-baichuan-hf-to-gguf.py
* 1. check in ggml.c if endianess is not match
2. update GGUF version
3. change get_pack_prefix to property
4. update information log
* always use "GGUF" as beginng of GGUF file
* Compare "GGUF" with file header char by char
1. Set GGUF_MAGIC to "GGUF" string instead of int value
2. Compare "GGUF" char by char to ensure its byte order
3. Move bytes swap code from convert.py to gguf.py write_tensor_data
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* add placeholder of starcoder in gguf / llama.cpp
* support convert starcoder weights to gguf
* convert MQA to MHA
* fix ffn_down name
* add LLM_ARCH_STARCODER to llama.cpp
* set head_count_kv = 1
* load starcoder weight
* add max_position_embeddings
* set n_positions to max_positioin_embeddings
* properly load all starcoder params
* fix head count kv
* fix comments
* fix vram calculation for starcoder
* store mqa directly
* add input embeddings handling
* add TBD
* working in cpu, metal buggy
* cleanup useless code
* metal : fix out-of-bounds access in soft_max kernels
* llama : make starcoder graph build more consistent with others
* refactor: cleanup comments a bit
* add other starcoder models: 3B, 7B, 15B
* support-mqa-directly
* fix: remove max_position_embeddings, use n_train_ctx
* Update llama.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Update llama.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Apply suggestions from code review
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* fix: switch to space from tab
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* convert : fix python 3.8 support
* convert : sort imports
* convert : fix required parameters in convert-llama-ggmlv3-to-gguf
* convert : fix mypy errors in convert-llama-ggmlv3-to-gguf
* convert : use PEP 585 generics and PEP 604 unions
Now that we have `from __future__ import annotations`, we can use this
modern syntax in Python 3.7 instead of restricting support to Python 3.9
or 3.10 respectively.
* gguf.py : a tuple is already a tuple
* add mypy.ini
* convert : add necessary `type: ignore` comments
* gguf-py: bump version
* convert: Fix permute calls and method/func definitions
* Cleanups for gguf-py
* Minor types cleanups.
* Initial implementation of handling merges and special tokens
* convert: Handle special tokens and merges in vocab only mode
convert: Vocab only mode no longer requires loading model tensors
* gguf: Refactor tensor name mapping
* convert: Fix type hint for special_token_types in SpecialVocab
* Use common special vocab handling in various conversion scripts
* First pass at implementing suggested changes
* Second pass
* gguf: SpecialVocab: Fix issue with special token content not in a dict
gguf: SpecialVocab: Allow skipping handling of merges
* convert-falcon-hf-to-gguf: Support --vocab-only option, bail out if no tokenizer.json
* convert-gptneox-hf-to-gguf and convert: Only handle merges for BPE tokenizer
* gguf: SpecialVocab: Actually set load_merges in object
* Uniform args parsing and vocab only mode for convert examples
* convert.py: Set gpt2 as tokenizer model when using BPE
* Squish last type warning in gguf.py - yay!
