John [Wed, 10 Jan 2024 13:37:09 +0000 (14:37 +0100)]
clip : support more quantization types (#4846)
Uses ggml functions instead of hardcoded names and adds support to quantize into the modern Q-K variants.
This is just the bare minimum to get k-types working - a more refined choice of types would be needed to get best quality on low quantizations.
I ran a few tests, it doesn't break anything I could notice and a Q6_K ViT works almost as well as Q8_0 but 3 times the inference speed.
Austin [Tue, 9 Jan 2024 18:46:46 +0000 (13:46 -0500)]
convert.py : fix vanilla LLaMA model conversion (#4818)
* 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 <redacted>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <redacted>
* 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 <redacted> Co-authored-by: Georgi Gerganov <redacted>
Justine Tunney [Tue, 9 Jan 2024 17:59:14 +0000 (09:59 -0800)]
llava-cli : don't crash if --image flag is invalid (#4835)
This change fixes an issue where supplying `--image missing-file` would
result in a segfault due to a null pointer being dereferenced. This can
result in distracting info being printed if robust crash analysis tools
are being used.
Behnam M [Tue, 9 Jan 2024 10:02:05 +0000 (05:02 -0500)]
server : update readme about token probs (#4777)
* updated server readme to reflect the gg/server-token-probs-4088 commit
added explanation for the API's completion result which now includes `completion_probabilities`. Also added a JSON schema that shows the type/structure of `completion_probabilities`.
* simplified the `completion_probabilities` JSON schema
It's now easier to understand what the structure of `completion_probabilities` looks like.
Kawrakow [Mon, 8 Jan 2024 15:02:32 +0000 (16:02 +0100)]
SOTA 2-bit quants (#4773)
* iq2_xxs: basics
* iq2_xxs: scalar and AVX2 dot products
Needed to change Q8_K to have quants in the -127...127 range,
else the IQ2_XXS AVX implementation becomes very awkward.
The alternative would have been to use Q8_0 instead. Perhaps
I'll change later, for now this is what we have.
* iq2_xxs: ARM_NEON dot product
Somehow strangely slow (112 ms/token).
* iq2_xxs: WIP Metal
Dequantize works, something is still wrong with the
dot product.
* iq2_xxs: Metal dot product now works
We have
PP-512 = 475 t/s
TG-128 = 47.3 t/s
Not the greatest performance, but not complete garbage either.
* iq2_xxs: slighty faster dot product
TG-128 is now 48.4 t/s
* iq2_xxs: slighty faster dot product
TG-128 is now 50.9 t/s
* iq2_xxs: even faster Metal dot product
TG-128 is now 54.1 t/s.
Strangely enough, putting the signs lookup table
into shared memory has a bigger impact than the
grid values being in shared memory.
* iq2_xxs: dequantize CUDA kernel - fix conflict with master
* iq2_xxs: quantized CUDA dot product (MMVQ)
We get TG-128 = 153.1 t/s
* iq2_xxs: slightly faster CUDA dot product
TG-128 is now at 155.1 t/s.
* iq2_xxs: add to llama ftype enum
* iq2_xxs: fix MoE on Metal
* Fix missing MMQ ops when on hipBLAS
I had put the ggml_supports_mmq call at the wrong place.
* Fix bug in qequantize_row_iq2_xxs
The 0.25f factor was missing.
Great detective work by @ggerganov!
postmasters [Tue, 2 Jan 2024 11:51:28 +0000 (03:51 -0800)]
llama : differentiate the KV dims in the attention (#4657)
* 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 <redacted>
* Fix llm_load_tensors: the asserts were not backcompat
crasm [Fri, 29 Dec 2023 14:50:29 +0000 (09:50 -0500)]
python : add check-requirements.sh and GitHub workflow (#4585)
* python: add check-requirements.sh and GitHub workflow
This script and workflow forces package versions to remain compatible
across all convert*.py scripts, while allowing secondary convert scripts
to import dependencies not wanted in convert.py.
* Move requirements into ./requirements
* Fail on "==" being used for package requirements (but can be suppressed)
* Enforce "compatible release" syntax instead of ==
* Update workflow
* Add upper version bound for transformers and protobuf
Philip Taron [Fri, 29 Dec 2023 14:42:26 +0000 (06:42 -0800)]
flake.nix : rewrite (#4605)
* flake.lock: update to hotfix CUDA::cuda_driver
Required to support https://github.com/ggerganov/llama.cpp/pull/4606
* flake.nix: rewrite
1. Split into separate files per output.
2. Added overlays, so that this flake can be integrated into others.
The names in the overlay are `llama-cpp`, `llama-cpp-opencl`,
`llama-cpp-cuda`, and `llama-cpp-rocm` so that they fit into the
broader set of Nix packages from [nixpkgs](https://github.com/nixos/nixpkgs).
3. Use [callPackage](https://summer.nixos.org/blog/callpackage-a-tool-for-the-lazy/)
rather than `with pkgs;` so that there's dependency injection rather
than dependency lookup.
4. Add a description and meta information for each package.
The description includes a bit about what's trying to accelerate each one.
5. Use specific CUDA packages instead of cudatoolkit on the advice of SomeoneSerge.
6. Format with `serokell/nixfmt` for a consistent style.
7. Update `flake.lock` with the latest goods.
* flake.nix: use finalPackage instead of passing it manually
* nix: unclutter darwin support
* nix: pass most darwin frameworks unconditionally
Justine Tunney [Fri, 29 Dec 2023 14:38:38 +0000 (06:38 -0800)]
llava-cli : refactor to use sampling library (#4669)
This change makes it possible to use flags like `--grammar` when using
the `llava-cli` program. The rest is just code cleanup deleting a long
standing TODO comment.
This change also ensures that logging information is emitted to stderr
which helps the `llava-cli` command be more friendly to shell scripts.
Justine Tunney [Fri, 29 Dec 2023 14:24:12 +0000 (06:24 -0800)]
server : replace sleep with condition variables (#4673)
The server currently schedules tasks using a sleep(5ms) busy loop. This
adds unnecessary latency since most sleep implementations do a round up
to the system scheduling quantum (usually 10ms). Other libc sleep impls
spin for smaller time intervals which results in the server's busy loop
consuming all available cpu. Having the explicit notify() / wait() code
also helps aid in the readability of the server code.
Justine Tunney [Thu, 28 Dec 2023 19:20:00 +0000 (11:20 -0800)]
Fix OpenAI server sampling w.r.t. temp and seed (#4668)
The default values for tfs_z and typical_p were being set to zero, which
caused the token candidates array to get shrunk down to one element thus
preventing any sampling. Note this only applies to OpenAI API compatible
HTTP server requests.
The solution is to use the default values that OpenAI documents, as well
as ensuring we use the llama.cpp defaults for the rest. I've tested this
change still ensures deterministic output by default. If a "temperature"
greater than 0 is explicitly passed, then output is unique each time. If
"seed" is specified in addition to "temperature" then the output becomes
deterministic once more.
See mozilla-Ocho/llamafile#117
See mozilla-Ocho/llamafile@9e4bf29
overview / pkg / ggml / sources / llama.cpp / log