* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* 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)
* add safetensors to convert.py help message
* Check for single-file safetensors model
* Update convert.py "model" option help message
* revert convert.py help message change
* 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>
* Work on the BPE tokenizer
Tokenizer tests work for Falcon-7B
* Try to fix build problem
* Fix debug assertion failure
* Fix MSVC Unicode BOM problem
* Cleanup and an improvement
* Fix compiler warning
* Cleanup
* Test doesn't work over the full range of Unicodes
* Update .gitignore and Makefile
* Another Makefile rule
* Testing Aquila
* Moving byte decoding back to `token_to_piece` ...
... because everyone is using it.
* Guarding some unusable code pathes
* Streamlining code and adding some more assertions
Important change: I'm classifying added tokens as control tokens now for BPE.
* Adding a comment
* Adding another assertion
* Fixed vocabulary guarding assertions
* Fix PR for recent change
* Fix PR for recent change
* Fix for compiler warning
* Fix PR for recent change
* Fix PR for recent change
* Fix PR for recent change
* Fix for compiler warning
* Fixes for more compiler warnings
* Remove unused code
* Fix initialization of static maps
* Add scores and token types back, adapt gptneox
* Update llama.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Update unicode.h
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Update unicode.h
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Ported Starcoder and added some assertions
* Fix coding style
* Apply @jploski 's fix for missing tokens
---------
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!
* Allow convert.py to convert to q8_0
Fix issue with bounded_parallel_map and greedy consuming iterator
Display elapsed time during conversion
* Add --concurrency option
Minor improvements to help text
Clean up bounded_parallel_map function a bit
* Massive speed improvement thanks to Cebtenzzre
* Refactor types
