* restore ci/run.sh, rename struct defination, fix bug in ggml_sycl_op_mul_mat_sycl
* fix format issue
* llama : fix segfault from unknown model arch name (llama/5820)
* llama : fix segfault from unknown model arch name
* llama : make all LLM maps const
This also requires using `std::map::at` instead of its `operator[]`
which does not exist for const maps.
* llama : name LLM_ARCH_UNKNOWN to "(unknown)"
This avoids errors from `std::map::at` when
getting the general name of the model architecture.
Using "(unknown)" instead of an empty string as per suggestion
https://github.com/ggerganov/llama.cpp/pull/5820#issuecomment-1973735284
* llama : remove redundant inner const for LLM_TENSOR_NAMES
The extra const won't do anything here as const maps
return const references to values.
Co-authored-by: Jared Van Bortel <redacted>
* llama : remove redundant nullptr check in llm_arch_from_string
Since LLM_ARCH_NAMES is a const map, no spurious elements
with a NULL name are inserted anymore, so this check is dead code.
---------
Co-authored-by: Jared Van Bortel <redacted>
* llama : refactor internal quantization functions (llama/5830)
* scripts : add pod-llama.sh
* ggml : IQ3_S improvements (llama/5829)
* iq3_s: somewhat faster AVX2 dot product
On Ryzen a 7950X TG-128 increases to 16 t/s from 15.5 t/s using
16 threads. For 8 threads it is 13.85 t/s vs 11.75 t/s.
PP-512 increases to 28.5 t/s from 23.8 t/s.
* iq3_s: somewhat faster ARM_NEON dot product
Still dog slow - 10.7 t/s up from 9.9 t/s.
* iq3_s: another small ARM_NEON improvement
10.7 -> 11.0 t/s. Using vmulq_s8 is faster than the xor - sub trick
that works best on AVX2.
* iq3_s: minor improvement on Metal
49.4 t/s -> 50.3 t/s
* iq3_s: PPL improvement
E.g., for a context of 4096 LLaMA-v2-7B goes to 5.1340 from 5.1653.
* iq3_s: use new grid everywhere
* Fix ARM_NEON
---------
Co-authored-by: Iwan Kawrakow <redacted>
* convert-hf : make model class definitions self-contained (llama/5825)
* convert : automatically fall back to HfVocab if tokenizer.model doesn't exist (llama/5821)
Co-authored-by: github-actions[bot] <redacted>
* server : init http requests thread pool with --parallel if set (llama/5836)
* ci : schedule slow server tests only on Release or on demand (llama/5839)
* llama : fix llama_copy_state_data with fragmented KV cache (llama/5840)
The row size of the saved states was based on kv_self.head while
it should be based on llama_kv_cache_cell_max.
Existing session files should still work.
* llama : fix llama_kv_cache_cell_max inability to return 1
I've also changed its return type to uint32_t,
because this function is always used to set the value of uint32_t variables,
and because the index already has this type.
* llama : fix state size calculation
Some bytes in the state were unaccounted for in llama_get_state_size.
Since the logits reserve so much space, it did not cause problems.
* gguf-dump : support i-quants (llama/5841)
Co-authored-by: Black_Fox <redacted>
* llama : allow for user specified embedding pooling type (llama/5849)
* allow for user specified pooling type
* llama : use enum types over int
---------
Co-authored-by: Georgi Gerganov <redacted>
* readme : add API changes section
* cuda : fix data race in soft max (llama/5853)
* main : support special tokens as reverse/anti prompt (llama/5847)
* Support special tokens as reverse/anti prompt.
* Tokenize antiprompts only once.
* main : minor
---------
Co-authored-by: Georgi Gerganov <redacted>
* common : use LLAMA_DEFAULT_SEED (llama/5855)
* add some new ops, fix some operators and add batch operations to certain operators. (ggml/747)
* cuda: fix group_norm
* cuda: add batch inference support for ggml_pad/ggml_upscale
Kawrakow [Sat, 2 Mar 2024 15:00:51 +0000 (17:00 +0200)]
ggml : IQ3_S improvements (llama/5829)
* iq3_s: somewhat faster AVX2 dot product
On Ryzen a 7950X TG-128 increases to 16 t/s from 15.5 t/s using
16 threads. For 8 threads it is 13.85 t/s vs 11.75 t/s.
PP-512 increases to 28.5 t/s from 23.8 t/s.
* iq3_s: somewhat faster ARM_NEON dot product
Still dog slow - 10.7 t/s up from 9.9 t/s.
* iq3_s: another small ARM_NEON improvement
10.7 -> 11.0 t/s. Using vmulq_s8 is faster than the xor - sub trick
that works best on AVX2.
* iq3_s: minor improvement on Metal
49.4 t/s -> 50.3 t/s
* iq3_s: PPL improvement
E.g., for a context of 4096 LLaMA-v2-7B goes to 5.1340 from 5.1653.
Kawrakow [Tue, 27 Feb 2024 14:34:24 +0000 (16:34 +0200)]
IQ4_XS: a 4.25 bpw quantization (llama/5747)
* Try IQ4_NL with blocks of 64 - does not look good
* iq4_xs: go to super-blocks of 256 and 6-bit scales for blocks of 32
* iq4_xs: CUDA works - 133.2 t/s
* iq4_xs: AVX2 dot product
* iq4_xs: ARM_NEON dot product
* iq4_nl: Metal implementation
As usual, Metal / Apple Silicon don't like my quants.
* iq3_xs: minor fix
* iq4_xs: shrink by using IQ3_S for attn_k and attn_q
* iq4_xs: revert using IQ3_S for attn_k and attn_v
PPL vs size is good, but CPU performance suffers: on M2 Max
TG-128 drops to 21.7 t/s from 28.8, and on a Ryzen-7950X
to 14.5 t/s from 15.8 t/s. On CUDA we have 135 t/s when
using IQ3_S vs 133 t/s with pure IQ4_XS.
* Fix CI
* iq4_xs: Added forgotten check for 256 divisibility
Kawrakow [Sat, 24 Feb 2024 14:23:52 +0000 (16:23 +0200)]
IQ3_S: a much better alternative to Q3_K (llama/5676)
* iq4_nl: squash commits for easier rebase
* Basics (quantize, dequantize)
* CUDA dequantize and dot product
* Slightly faster CUDA dot product (120 t/s)
* Switch to 6-bit scales
* Scalar dot product
* AVX2 dot product
* ARM_NEON dot product
* Works on metal, but still slow
* Slightly better Metal dot product
* Another small Metal improvement
* Metal dot product is getting there
* Faster CUDA dot product
* Add 1/8 ffn_down layers as Q5_K when no imatrix has been provided
* Report the actual bpw
* Add _xs mix that is 4.05 bpw for non-MoE models
* Remove IQ4_XS for now, slightly adjust kvalues_iq4nl
* AVX2 dot product uses Q8_0 instead of Q8_K
* Add to test-backend-ops
* Minor fix
* Also use use Q5_K for attn_output in MoE models
* Fixes after merging latest master
* Switching to blocks of 32
* AVX2 for blocks of 32
* Scaler dot product for blocks of 32
* ARM_NEON dot product for blocks of 32
* Metal kernels for blocks of 32
* Slightly faster Metal kernels
* Resurrecting iq3_xs
After all the experimentation, nothing was better than this.
* Minor PPL improvement via a block scale fudge factor
* Minor improvement via 3 neighbours
* iq3_xs: working scalar and AVX2 dot products
* iq3_xs: ARM_NEON dot product - works but extremely slow (10 t/s)
* iq3_xs: working Metal implementation
* Adding IQ3_M - IQ3_XS mix with mostly Q4_K
* iiq3_xs: a 3.4375 bpw variant
* iq3_xs: make CUDA work for new version
* iq3_xs: make scalar and AVX2 work for new version
* iq3_s: make ARM_NEON work with new version
* iq3_xs: make new version work on metal
Performance is very similar to Q3_K_S
* iq3_xs: tiny Metal speed improvement
* iq3_xs: tiny Metal speed improvement
* Fix stupid warning
* Q3_K_XS now uses a mix of IQ3_XS and IQ3_XXS
* iq3_xs: rename to iq3_s
* iq3_s: make tests pass
* Move Q3_K_XS mix to 3.25 bpw
* Attempt to fix failing tests
* Another attempt to fix the Windows builds
* Attempt to fix ROCm
* ROCm again
* iq3_s: partial fix for QK_K = 64
* iq3_s: make it work on metal for QK_K = 64
Pleasent surprise: the coding was super-block size independent,
so all it took was to delete some QK_K == 256 guards.
options:
-q, --quick skip checking the required library
action:
TEXTFILE read the text file (default: stdin)
-l, --list show the list of voices and exit
-h, --help show this help and exit
voice selection:
-n NAME, --name NAME get a voice object by name (default: Arnold)
-v NUMBER, --voice NUMBER
get a voice object by number (see --list)
-f KEY=VAL, --filter KEY=VAL
filter voices by labels (default: "use case=narration")
this option can be used multiple times
filtering will be disabled if the first -f has no "=" (e.g. -f "any")
output:
-s FILE, --save FILE save the TTS to a file (default: audio.mp3)
-p, --play play the TTS with ffplay
```
Fix issue: Conversion from Whisper to OpenVino failed #1870
convert-whisper-to-openvino.py stopped working with OpenVINO version 2023.0.0-10926-b4452d56304-releases/2023/0 .
Error was: TypeError: load(): incompatible function arguments. The following argument types are supported:
1. (self: openvino._pyopenvino.FrontEnd, path: object) -> ov::frontend::InputModel
Davidson Francis [Thu, 22 Feb 2024 13:01:08 +0000 (10:01 -0300)]
main : fix file existence check in main.cpp (#1889)
In commit dda4b0e of PR #1872, I've introduced a check for the
existence of files before loading the model. However, I haven't
considered the case where whisper.cpp might read from stdin as well,
and in such cases, the checks should ignore the "-" argument as it
does not represent a regular file.
Additionally, this commit removes the usage of 'stat()' in favor of
the recently introduced function 'is_file_exist()' in common.cpp from
PR #1871.
Apologies for the bug introduced in the previous PR and any
inconvenience it may have caused.
Co-authored-by: Jared Van Bortel <redacted>
* Update READMEs with info about numa flags, change INTERLEAVE strategy name to DISTRIBUTE everywhere, implement the improved distribution strategy from @rankaiyx, fix a spelling mistake and un-merge some bad merges
* split numa init out from llama_backend_init and created llama_numa_init. Updated all code paths and samples
* Fix up some boolean vs enum comparisons
* Added #ifdefs for non-Linux OS that don't have cpu_set_t datatype
* Update ggml.h
Align enum values
Co-authored-by: Georgi Gerganov <redacted>
* Update ggml.c
Remove whitespace
Co-authored-by: Georgi Gerganov <redacted>
* Update ggml.c
align paremeters
Co-authored-by: Georgi Gerganov <redacted>
* Update examples/server/server.cpp
remove whitespace and align brace
Co-authored-by: Georgi Gerganov <redacted>
* Update common/common.cpp
Remove whitespace and align brace
Co-authored-by: Georgi Gerganov <redacted>
* unified ggml_numa_strategy enum and fixed text alignment in server.cpp example
* Update ggml.c
simplified return for platforms without NUMA support
Co-authored-by: Jared Van Bortel <redacted>
* removed redundant else from cli argument processing of --numa
* whitespace
---------
Co-authored-by: root <redacted> Co-authored-by: Jared Van Bortel <redacted> Co-authored-by: Georgi Gerganov <redacted> Co-authored-by: Jared Van Bortel <redacted>
Davidson Francis [Mon, 19 Feb 2024 08:51:26 +0000 (05:51 -0300)]
main : check if input files exist before proceeding (#1872)
Until the most recent commit (3d42463), the main.cpp sample file does
not check whether the input files exist or not. Consequently, the
model is loaded first before reporting whether there was a failure or
not when processing a file. In environments with HDD, this can take
about 50 seconds or more, depending on the loaded model.
This commit addresses this issue by checking in advance whether the
input files exist or not.
overview / pkg / ggml / sources / whisper.cpp / log