llama : ggml-backend integration #4766
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slaren
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ggerganov
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On my machine with 3x P40 LLaMA 2 70b q6_K crashes: LLaMA 2 7b q4_0 and LLaMA 2 13b q6_K work correctly. |
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I did some performance testing on my Linux system with 3x P40:
The P40s are connected to the motherboard via PCIe 3.0 x16/x8/x8. Choosing the x16 GPU as the main GPU lets you use the full PCIe bandwidth. Choosing any of the other GPUs effectively reduces the interconnect speed to x8/x4/x4. For dual GPU use the interconnect speed is effectively limited to x8/x8. I was not able to test LLaMA 2 70b or Mixtral because they currently do not work with this PR. On my system the new scheme for distributing weights is slower. One of the reasons is probably that P40s are comparatively slow so the interconnect speed doesn't bottleneck them as much as e.g. 3x RTX 4090. Synchronization overhead may also depend on OS. Finally, because the P40s use MMQ they benefit from the performance optimizations in this PR: #3110 . For FP16 cuBLAS similar optimizations could be done by converting the hidden state to FP16 on the main device and caching the dequantized weight matrix (otherwise it is dequantized multiple times if tiling is enabled). |
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Thanks for testing. There is an issue in the graph splitting logic that is causing some operations of each layer to be run on a different GPU, and that's why it fails with 70B, it creates too many splits. |
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The graph split generation should be much better now. With 3080+3090Ti I get 3600 t/s pp 512, and 105 t/s tg 128. Compared to a single GPU, I get 4020 t/s pp, 121 t/s tg with only the 3090Ti, and 3320 t/s pp, 110 t/s tg with only the 3080. |
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Did some tests on 4x RTX 4090 - significant improvements are observed masterggml_init_cublas: GGML_CUDA_FORCE_MMQ: no
build: b3a7c20 (1768) PR(bumped ggml_init_cublas: GGML_CUDA_FORCE_MMQ: no
build: d4fca23 (1763) Any idea why F16 TG is slower with this PR? Will keep the pod active for the day - can do some more tests if needed P.S. Here are some additional results using the LLAMA_CUBLAS=1 make -j batched-bench && ./batched-bench models/codellama-34b/ggml-model-f16.gguf 18432 0 99 1 100 128 1,2,3,4,5,6,7,8,16,32,64masterPR |
Token generation is I/O bound. So FP16 is going to be comparatively slow (which reduces the interconnect bottleneck). At the same time the hidden state is very small and thus can be transferred quickly between devices. So I think that this is just a scenario where the implementation on master has favorable conditions. |
I think there are some cases where the row splitting implementation can achieve higher tg performance than with a single GPU, so maybe this is one of them, but I am just guessing. I am already aware of some inefficiencies in the current implementation, for instance the way the data is copied between GPUs is very inefficient, but there is still a lot of work to do before this can be merged, so I think it is better to leave that for another PR. |
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I have no idea what needs to be done to fix the swift build, the errors don't make sense to me. |
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The swift builds are failing because #4691 makes the swift examples pull the The builds will be resolved after upstreaming the changes from this PR to the |
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Nice job, glad to see! (And thanks, as this will close #4055 which I held dear :) For parallelism in prompt processing there is possibly another optimization (https://www.deepspeed.ai/tutorials/pipeline/) |
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Does that work for inference? The article seems to be about training, by interleaving forward and backward passes, but I don't see how to apply that during inference. |
slaren
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I'll still have to wrap my head around it and it's 7:00 AM. maybe I'm mistaken but if we process a batch of tokens through GPU1 (layers 1-10), then pass those to GPU2 (11-20), wouldn't we be able to process the next on GPU1 while GPU2 is still working ? |
What you can do is split a batch of e.g. 512 tokens into 4 batches of 128 tokens. If you then have 4 GPUs you can have the GPU with the first few layers work on the first batch first. Once the first batch has passed through the layers of the first GPU, the first GPU can start working on the second batch while the second GPU can start working on the first batch. In this particular example you could get up to 57% GPU utilization, so more than 2x more than without pipelining. For very large batches you could get up to ~100% GPU utilization. The problem with this approach is that the code in its current form very much does not like small batches in terms of performance:
So even if you do pipelining and get higher GPU utilization the overall performance could be worse. I have a prototype for int8 matrix matrix multiplication using tensor cores; I'll make a related PR later today (did not yet investigate performance in detail but could be better for small batches). |
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This PR changes perplexity in non-negligible ways. I came across this while looking into #4900. I checked out the version just before #4872 was merged (49662cb), quantized Mixtral-instruct-8x7B with This is just one anecdotal example, but |
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I'm looking into this and a difference in the PPL values with partial offloading is expected from this PR, but I cannot say yet if the large delta that you observe is within that expectation. The reason that the results change after this PR is that for the non-offloaded layers, the RoPE is now computed on the CPU, while before this PR, it was computed on the GPU - i.e. the Lines 6174 to 6178 in 584d674 The same analysis is also valid for the softmax in the attention, because the Bottomline is that for non-offloaded layers, RoPE and softmax are now running on the CPU, while before they ran on the GPU, so some change in PPL should be observed. I did the following experiment - I checked out the commit before this PR and applied this patch: diff --git a/llama.cpp b/llama.cpp
index ce413f60..d22669c2 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -6172,8 +6172,8 @@ static const std::unordered_map<const char *, llm_offload_func_e> k_offload_map
//{ "inp_embd", OFFLOAD_FUNC_NR }, // TODO: missing K-quants get_rows kernel
{ "pos_embd", OFFLOAD_FUNC_NR },
- { "inp_pos", OFFLOAD_FUNC_FRC }, // this is often used for KQ ops (e.g. rope)
- { "KQ_mask", OFFLOAD_FUNC_FRC },
+ { "inp_pos", OFFLOAD_FUNC_NR }, // this is often used for KQ ops (e.g. rope)
+ { "KQ_mask", OFFLOAD_FUNC_NR },
{ "K_shift", OFFLOAD_FUNC_FRC },
{ "K_shifted", OFFLOAD_FUNC },This will make the However, they still don't match for Mixtral - I guess there is some additional difference that I do not see, likely due to the extra operations involved in the FFN. I also checked that the PPL with full offload is the same for CUDA and Metal before and after this PR. I'll keep looking as I am not yet 100% sure that there isn't some other issue Edit: for Mixtral with These were on the CPU before, so I suspect this should explain the difference in the PPL values. Will try to confirm this |
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partially load with opencl dont work llama-b1843-bin-win-clblast-x64.zip |
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This pr broke file prompt ingestion in the main example. DetailsOther models like tinyllama and stablelm-3b-4e1t will spew out single letters repeatedly. |
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There is likely an issue with the |
you where right, it does indeed work with kv offloading. it is still funny to watch it decent into madness and mostly print escape sequences/rawbytes at the "end". |
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@ggerganov Thanks for the detailed explanation of differences introduced by this PR. Somehow I find it unlikely that running RoPE and softmax on the CPU instead of the GPU can cause such large differences in PPL. My latest example is Mixtral-8x7B quantized with |
4 tasks
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All this work and afterwards 2x3090 went from 18.9 t/s to 15 t/s and 2xP40 went from 8.8 to like 8.5, regardless of which layer split option is used. |
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Are you on Windows? I also noticed a single GPU performance regression for my AMD GPU but we were not able to pin down what the cause is. Possibly some API call that is fast on Linux with CUDA is slow for Windows and HIP. |
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I am on linux. I was worried about this so I am building an older version to double check my speeds. Around dec 27th, which is when my backup is from, I was still getting at least 16.3 t/s so there have been regressions along the way. I probably wasn't paying attention because I was using mixtral. |
There are performance regression on rdna2 on single GPU,but I haven't notice that on a rdna3 GPU like 7900xtx on Windows at least. |
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Just to add my experience: using 2x RTX A6000 with an Nvlink bridge I'm only getting around 60% of the tokens/s using the new default layer splitting vs row splitting. I'm guessing without the Nvlink it would be about the same. |
Can you do a git bisect to check which commit is to blame? |
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I have to investigate. I have snapshots from nov 29, dec 27 and fresh. Going back to earlier ones isn't helping at the moment, I also haven't rebooted in about 3 months. |
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Something weird is going on because I no longer hit 18.x back to november backup. Then again.. my outdoor temperatures are around 30f and my CPUs top out at 14c with the fans pushed to low. You'd think it works better colder but I guess not. I still have my old benchmarks saved on the same models so it's not in my head. By the current measurement I went from 14.4 to 15.4t/s. I suppose it will remain a mystery for a while longer. |
jordankanter
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* llama : ggml-backend integration * ggml-backend : add names to buffers * fix unmap after loading * batched-bench : add tensor_split param * llama : check for null tensor_split * ggml-backend : increase GGML_MAX_BACKENDS * improve graph splitting, partial fix for --no-kv-offload * cuda : add ggml-backend split buffer support * cuda : do not create buffer types for devices that don't exist (fixes usage without CUDA devices available) * ggml : fix null backend dereference (ggerganov#4807) * ggml : fix null backend dereference * ggml : also check ggml_backend_is_cpu * test-backend-ops : check buffer allocation failures * llama : add cparam (split_mode) and command line argument (--split-mode, -sm) to configure the split mode (none, layer or row) * ggml : fix mul_mat_id work size * llama : rewrite session kv load/set without graphs * minor * llama : only initialize used backends, free backends on context free * llama : abort ctx if cuda backend init fails * llama : rewrite lora with ggml-backend and compute on CPU ggml-ci * llama : only map to a backend buffer the region of the file mapping containing the tensors used in the buffer * opencl : add ggml-backend buffer type * cuda : only use batched_cublas with batched mat muls (fixes fp16 tg perf) * llama : on Metal, by default offload the full model ggml-ci * metal : page align the data ptr (ggerganov#4854) * Apply suggestions from code review Co-authored-by: Johannes Gäßler <johannesg@5d6.de> * cuda : fix split buffer free * address review comments * llama-bench : add split-mode parameter * fix whitespace * opencl : fix double initialization * server : add --split-mode parameter * use async copy and compute to improve multi-gpu performance ggml-ci * use async memcpys to copy the graph outputs to the CPU * fix opencl * use a host buffer for the cpu compute buffer for faster copies to the gpu --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
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The offending commit is: #4606 |
hodlen
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* llama : ggml-backend integration * ggml-backend : add names to buffers * fix unmap after loading * batched-bench : add tensor_split param * llama : check for null tensor_split * ggml-backend : increase GGML_MAX_BACKENDS * improve graph splitting, partial fix for --no-kv-offload * cuda : add ggml-backend split buffer support * cuda : do not create buffer types for devices that don't exist (fixes usage without CUDA devices available) * ggml : fix null backend dereference (ggerganov#4807) * ggml : fix null backend dereference * ggml : also check ggml_backend_is_cpu * test-backend-ops : check buffer allocation failures * llama : add cparam (split_mode) and command line argument (--split-mode, -sm) to configure the split mode (none, layer or row) * ggml : fix mul_mat_id work size * llama : rewrite session kv load/set without graphs * minor * llama : only initialize used backends, free backends on context free * llama : abort ctx if cuda backend init fails * llama : rewrite lora with ggml-backend and compute on CPU ggml-ci * llama : only map to a backend buffer the region of the file mapping containing the tensors used in the buffer * opencl : add ggml-backend buffer type * cuda : only use batched_cublas with batched mat muls (fixes fp16 tg perf) * llama : on Metal, by default offload the full model ggml-ci * metal : page align the data ptr (ggerganov#4854) * Apply suggestions from code review Co-authored-by: Johannes Gäßler <johannesg@5d6.de> * cuda : fix split buffer free * address review comments * llama-bench : add split-mode parameter * fix whitespace * opencl : fix double initialization * server : add --split-mode parameter * use async copy and compute to improve multi-gpu performance ggml-ci * use async memcpys to copy the graph outputs to the CPU * fix opencl * use a host buffer for the cpu compute buffer for faster copies to the gpu --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
hodlen
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readme : update hot topics
common : add `--version` option to show build info in CLI (#4433)
build : detect host compiler and cuda compiler separately (#4414)
sync : ggml (SD ops, tests, kernels) (#4444)
* sync : ggml (SD ops, tests, kernels)
ggml-ci
* cuda : restore im2col
ggml-ci
* metal : fix accuracy of dequantization kernels
ggml-ci
* cuda : restore correct im2col
ggml-ci
* metal : try to fix moe test by reducing expert size
ggml-ci
* cuda : fix bin bcast when src1 and dst have different types
ggml-ci
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Co-authored-by: slaren <slarengh@gmail.com>
server : fix handling of characters that span multiple tokens when streaming (#4446)
readme : update supported model list (#4457)
convert : support loading vocab from fast tokenizer config (#3633)
* Add HFVocab into convert.py
* Update convert.py
* Update convert.py
* add bytes_to_unicode function
* change add_meta_vocab fucntion
* remove debug code
* remove byte_encoder
* Add newline between classes
* Check tokenizer.json when tokenizer.model is not exist.
* Move transformers dependency to local code
* Add error context with 'raise from'
* Add fast tokenizer option to BpeVocab
* Update convert.py
* Add VocabLoader and remove *Vocab class
* Add transformers dependency
* remove added tokens and check newline token to decide spm or bpe
* Update convert.py
* Add special token type
* Update convert.py
* Update convert.py
* Update convert.py
* Fix typo in convert.py
* Fix when params.n_vocab < tokenizer vocab size
* update vocab class
* change funtion name
* Remove unused variable/functions, add types to class variable and methods, delete blank liens
* fix flake8 warnings
* code style cleanup
* make mypy happy
* change exception
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Co-authored-by: Jared Van Bortel <jared@nomic.ai>
ggml : fix OpenCL broadcast requirement for ggml_mul (close #4453)
ggml : add ggml_row_size() (fixes llama out of space) (#4461)
* Fixes "Not enough space in the context's memory pool" encountered on certain models, which seems to be caused by some imprecision related to the automatic casting of floating point values
* do not cast to size_t, instead just use doubles
* ggml : add ggml_row_size(), deprecate ggml_type_sizef()
* ggml : fix row size compute to avoid overflows
* tests : fix sizey -> sizez
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Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
py : add protobuf dependency (#4466)
ggml : remove n_dims from ggml_tensor (#4469)
ggml-ci
ggml : use ggml_row_size where possible (#4472)
* ggml : use ggml_row_size where possible
ggml-ci
* ggml : move ggml_nbytes_split to ggml-cuda.cu
ggml : group mul_mat_id rows by matrix (cpu only) (#4480)
* ggml : group mul_mat_id rows by matrix (cpu only)
* remove mmid parameters from mm forward
* store row groups in wdata and calculate only once in GGML_TASK_INIT
ggml-ci
server : add optional API Key Authentication example (#4441)
* Add API key authentication for enhanced server-client security
* server : to snake_case
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
llama : sanity checks for access to logits (#4274)
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
lora : add support for non-llama models (#3333)
* lora : add support for non-llama models
ggml-ci
* avoid leaking ggml_context on failure
cleanup
ggml-ci
* lora : allow 1d tensors
* lora : include embd and output layers in size calculation
* fix style
Link to cublas dynamically on Windows even with LLAMA_STATIC (#4506)
server : allow requests larger than 8K (#4500)
server : fix possible ambiguity in content type charset (#4501)
server : fix grammar being ignored (#4494)
Fix bug in identifying the grammar.
server : disable llm logs if SERVER_VERBOSE is off (#3792)
finetune : keep allocs alive until all allocations are done (#4486)
build : Check the ROCm installation location (#4485)
* build : Check the ROCm installation location
* more generic approach
* fixup! It was returning the path instead of the command output
* fixup! Trailing whitespace
gguf-py : fail fast on nonsensical special token IDs (#4489)
llama.swiftui : add bench functionality (#4483)
* llama.swiftui : add bench button
* llama.swiftui : initial bench functionality
* force to use n_gpu_layers on simulator
* add download buttons & expose llamaState.loadModel
* update project.pbxproj
* comment #Preview & fix editorconfig check
* gitignore : xcode stuff
* llama.swiftui : UX improvements
* llama.swiftui : avoid data copy via "downloadTask"
* llama.swiftui : remove model from project
* llama : remove "mostly" from model infos
* llama.swiftui : improve bench
---------
Co-authored-by: jhen <developer@jhen.me>
readme : update hot topics
decode : fix logits_valid for legacy API (#4516)
llama : fix try_override for bool_value which always return true (#4519)
llama : add phi-2 + fix NeoX rope + ggml_mul_mat_set_prec (#4490)
* phi2 implementation
* fix breaking change
* phi-2 : various fixes
* phi-2 : use layer norm eps
* py : whitespaces
* llama : fix meta KV override bug
* convert : phi don't add BOS token
* convert : revert "added_tokens_decoder" change
* phi-2 : scale Q instead of KQ for better precision
* ggml : fix NeoX rope to rotate just first n_dims
* cuda : less diff in the rope_neox kernel
* ggml : add ggml_mul_mat_set_prec
ggml-ci
* Update ggml-cuda.cu
Co-authored-by: slaren <slarengh@gmail.com>
* Update ggml-cuda.cu
Co-authored-by: slaren <slarengh@gmail.com>
* cuda : ggml_cuda_op_mul_mat_cublas support F32 precision
* cuda : remove oboslete comment
---------
Co-authored-by: Ebey Abraham <ebeyabraham@microsoft.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: slaren <slarengh@gmail.com>
llama.swiftui : add more models
llama.swiftui : add tinyllama 1.1B F16
ggml-cuda: Fix HIP build (#4528)
regression of #4490
Adds defines for two new datatypes
cublasComputeType_t, cudaDataType_t.
Currently using deprecated hipblasDatatype_t since newer ones very recent.
ggml : fixed check for _MSC_VER (#4535)
Co-authored-by: Eric Sommerlade <ersomme@microsoft.com>
CUDA: Faster Mixtral prompt processing (#4538)
* CUDA: make MoE tensors contiguous for batch size>1
* Update ggml-cuda.cu
Co-authored-by: slaren <slarengh@gmail.com>
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Co-authored-by: slaren <slarengh@gmail.com>
Fix access violation in ggml_cuda_free_data if tensor->extra is NULL (#4554)
llama : disable per-tensor info prints on model load (#4562)
cuda : replace asserts in wrong architecture checks with __trap (#4556)
* cuda : replace asserts in wrong architecture checks with __trap
* make bad_arch noreturn, remove returns
cuda : better error message for ggml_get_rows (#4561)
* Update ggml-cuda.cu
* Update ggml-cuda.cu
* Update ggml-cuda.cu
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Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
py : open merges file as 'utf-8' (#4566)
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>
```
readme : update coding guidelines
CUDA: mul_mat_id always on GPU for batches >= 32 (#4553)
common : remove incorrect --model-draft default (#4568)
ggml-cuda: Fix HIP build by adding define for __trap (#4569)
Regression of 139882392258671ffe5acdfcadc0bc08572d6eef
HIP doesn't have trap, only abort
cuda : ROCm AMD Unified Memory Architecture (UMA) handling (#4449)
* AMD ROCm: handle UMA memory VRAM expansions
This resolves #2797 by allowing ROCm AMD GPU users with a UMA to
dynamically expand the VRAM allocated to the GPU.
Without this, AMD ROCm users with shared CPU/GPU memory usually are
stuck with the BIOS-set (or fixed) framebuffer VRAM, making it
impossible to load more than 1-2 layers.
Note that the model is duplicated in RAM because it's loaded once for
the CPU and then copied into a second set of allocations that are
managed by the HIP UMA system. We can fix this later.
* clarify build process for ROCm on linux with cmake
* avoid using deprecated ROCm hipMallocHost
* keep simplifying the change required for UMA
* cmake: enable UMA-compatible allocation when LLAMA_HIP_UMA=ON
metal : fix `ggml_metal_log` vargs (#4373)
llama : allow getting n_batch from llama_context in c api (#4540)
* allowed getting n_batch from llama_context in c api
* changed to use `uint32_t` instead of `int`
* changed to use `uint32_t` instead of `int` in `llama_n_ctx`
* Update llama.h
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
llama : initial ggml-backend integration (#4520)
* llama : initial ggml-backend integration
* add ggml-metal
* cuda backend can be used though ggml-backend with LLAMA_GGML_BACKEND_CUDA_TEST
access all tensor data with ggml_backend_tensor_get/set
* add ggml_backend_buffer_clear
zero-init KV cache buffer
* add ggml_backend_buffer_is_hos, used to avoid copies if possible when accesing tensor data
* disable gpu backends with ngl 0
* more accurate mlock
* unmap offloaded part of the model
* use posix_fadvise64(.., POSIX_FADV_SEQUENTIAL) to improve performance with mmap
* update quantize and lora
* update session copy/set to use ggml-backend
ggml-ci
* use posix_fadvise instead of posix_fadvise64
* ggml_backend_alloc_ctx_tensors_from_buft : remove old print
* llama_mmap::align_offset : use pointers instead of references for out parameters
* restore progress_callback behavior
* move final progress_callback call to load_all_data
* cuda : fix fprintf format string (minor)
* do not offload scales
* llama_mmap : avoid unmapping the same fragments again in the destructor
* remove unnecessary unmap
* metal : add default log function that prints to stderr, cleanup code
ggml-ci
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
ci : add `jlumbroso/free-disk-space` to docker workflow (#4150)
* [github][workflows][docker]: removes hardcoded `ggerganov` from `ghcr` repo
* [github][workflows][docker]: adds `jlumbroso/free-disk-space`
gguf : simplify example dependencies
gguf-py : fix broken link
ggml : change ggml_scale to take a float instead of tensor (#4573)
* ggml : change ggml_scale to take a float instead of tensor
* ggml : fix CPU implementation
* tests : fix test-grad0
ggml-ci
llama : add ability to cancel model loading (#4462)
* llama : Add ability to cancel model load
Updated llama_progress_callback so that if it returns false, the model
loading is aborted.
* llama : Add test for model load cancellation
* Fix bool return in llama_model_load, remove std::ignore use
* Update llama.cpp
Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com>
* Fail test if model file is missing
* Revert "Fail test if model file is missing"
This reverts commit 32ebd525bf7e5a87ee8a3dbaab3d92ce79fbf23d.
* Add test-model-load-cancel to Makefile
* Revert "Revert "Fail test if model file is missing""
This reverts commit 2796953257ee5383fa7c8fe8fa8fc888c048fb0b.
* Simplify .gitignore for tests, clang-tidy fixes
* Label all ctest tests
* ci : ctest uses -L main
* Attempt at writing ctest_with_model
* ci : get ci/run.sh working with test-model-load-cancel
* ci : restrict .github/workflows/build.yml ctest to -L main
* update requirements.txt
* Disable test-model-load-cancel in make
* Remove venv before creation
* Restructure requirements.txt
Top-level now imports the specific additional requirements for each
python file. Using `pip install -r requirements.txt` will fail if
versions become mismatched in the per-file requirements.
* Make per-python-script requirements work alone
This doesn't break the main requirements.txt.
* Add comment
* Add convert-persimmon-to-gguf.py to new requirements.txt scheme
* Add check-requirements.sh script and GitHub workflow
* Remove shellcheck installation step from workflow
* Add nocleanup special arg
* Fix merge
see: https://github.com/ggerganov/llama.cpp/pull/4462#discussion_r1434593573
* reset to upstream/master
* Redo changes for cancelling model load
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com>
ggml : extend `enum ggml_log_level` with `GGML_LOG_LEVEL_DEBUG` (#4579)
readme : add zig bindings (#4581)
ci : tag docker image with build number (#4584)
make : add LLAMA_HIP_UMA option (#4587)
NB: LLAMA_HIP_UMA=1 (or any value) adds MK_CPPFLAG -DGGML_HIP_UMA
ggml : add comment about backward GGML_OP_DIAG_MASK_INF (#4203)
llama : fix platforms without mmap (#4578)
* llama : fix platforms without mmap
* win32 : limit prefetch size to the file size
* fix win32 error clobber, unnecessary std::string in std::runtime_error
Fix CudaMemcpy direction (#4599)
cuda : fix jetson compile error (#4560)
* fix old jetson compile error
* Update Makefile
* update jetson detect and cuda version detect
* update cuda marco define
* update makefile and cuda,fix some issue
* Update README.md
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Update Makefile
* Update README.md
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
sync : ggml (fix im2col) (#4591)
* cuda : fix im2col_f32_f16 (ggml/#658)
ggml-ci
* ggml-alloc : fix ggml_tallocr_is_own
---------
Co-authored-by: leejet <leejet714@gmail.com>
lookup : add prompt lookup decoding example (#4484)
* initial commit, going through initializations
* main loop finished, starting to debug
* BUG: generates gibberish/repeating tokens after a while
* kv_cache management
* Added colors to distinguish drafted tokens (--color). Updated README
* lookup : fix token positions in the draft batch
* lookup : use n_draft from CLI params
* lookup : final touches
---------
Co-authored-by: Leon Ericsson <leon.ericsson@icloud.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
CUDA: fixed row rounding for 0 tensor splits (#4594)
grammar : check the full vocab only if necessary (opt) (#4306)
* Check the full vocab for grammar only if necessary
* Fix missing logit restoration step (?)
Does this matter, actually?
* Fix whitespace / formatting
* Adjust comment
* Didn't mean to push test gbnf
* Split sampling into the helper function (?)
And also revert the changes made to the header
* common : fix final newline
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
server : allow to specify custom prompt for penalty calculation (#3727)
ci(docker): fix tags in "Build and push docker image (tagged)" (#4603)
fallback to CPU buffer if host buffer alloc fails (#4610)
cuda : improve cuda pool efficiency using virtual memory (#4606)
* cuda : improve cuda pool efficiency using virtual memory
* fix mixtral
* fix cmake build
* check for vmm support, disable for hip
ggml-ci
* fix hip build
* clarify granularity
* move all caps to g_device_caps
* refactor error checking
* add cuda_pool_alloc, refactor most pool allocations
ggml-ci
* fix hip build
* CUBLAS_TF32_TENSOR_OP_MATH is not a macro
* more hip crap
* llama : fix msvc warnings
* ggml : fix msvc warnings
* minor
* minor
* cuda : fallback to CPU on host buffer alloc fail
* Update ggml-cuda.cu
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* Update ggml-cuda.cu
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* ensure allocations are always aligned
* act_size -> actual_size
---------
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
llama : add PLaMo model (#3557)
* add plamo mock
* add tensor loading
* plamo convert
* update norm
* able to compile
* fix norm_rms_eps hparam
* runnable
* use inp_pos
* seems ok
* update kqv code
* remove develop code
* update README
* shuffle attn_q.weight and attn_output.weight for broadcasting
* remove plamo_llm_build_kqv and use llm_build_kqv
* fix style
* update
* llama : remove obsolete KQ_scale
* plamo : fix tensor names for correct GPU offload
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
simplify bug issue template (#4623)
Adding Emeltal reference to UI list (#4629)
Fix new CUDA10 compilation errors (#4635)
Update comment for AdamW implementation reference. (#4604)
Co-authored-by: Will Findley <findley@gmail.com>
cuda : fix vmm pool with multi GPU (#4620)
* cuda : fix vmm pool with multi GPU
* hip
* use recommended granularity instead of minimum
* better error checking
* fix mixtral
* use cudaMemcpy3DPeerAsync
* use cuda_pool_alloc in ggml_cuda_op_mul_mat
* consolidate error checking in ggml_cuda_set_device
* remove unnecessary inlines
ggml-ci
* style fixes
* only use vmm for the main device
* fix scratch buffer size, re-enable vmm pool for all devices
* remove unnecessary check id != g_main_device
Add byte token type when tokenizer.model is not exists (#4641)
* Add byte token type to hf format
* remove unused variable
ggml : fix dot product for ARM (#4630)
ggml-ci
scripts : add sync-ggml-am.sh
finetune : fix output formatting in print_params (#4653)
This commit fixes the output formatting in the print_params function
which currently looks like this:
```console
print_params: n_vocab: 32000
print_params: n_ctx: 128
print_params: n_embd: 4096
print_params: n_ff: 11008
print_params: n_head: 32
print_params: n_head_kv: 32
print_params: n_layer: 32
print_params: norm_rms_eps : 0.000010
print_params: rope_freq_base : 10000.000000
print_params: rope_freq_scale : 1.000000
```
With this comit the output will look like this:
```console
print_params: n_vocab : 32000
print_params: n_ctx : 128
print_params: n_embd : 4096
print_params: n_ff : 11008
print_params: n_head : 32
print_params: n_head_kv : 32
print_params: n_layer : 32
print_params: norm_rms_eps : 0.000010
print_params: rope_freq_base : 10000.000000
print_params: rope_freq_scale : 1.000000
```
Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
llama : add AWQ for llama, llama2, mpt, and mistral models (#4593)
* update: awq support llama-7b model
* update: change order
* update: benchmark results for llama2-7b
* update: mistral 7b v1 benchmark
* update: support 4 models
* fix: Readme
* update: ready for PR
* update: readme
* fix: readme
* update: change order import
* black
* format code
* update: work for bot mpt and awqmpt
* update: readme
* Rename to llm_build_ffn_mpt_awq
* Formatted other files
* Fixed params count
* fix: remove code
* update: more detail for mpt
* fix: readme
* fix: readme
* update: change folder architecture
* fix: common.cpp
* fix: readme
* fix: remove ggml_repeat
* update: cicd
* update: cicd
* uppdate: remove use_awq arg
* update: readme
* llama : adapt plamo to new ffn
ggml-ci
---------
Co-authored-by: Trần Đức Nam <v.namtd12@vinai.io>
Co-authored-by: Le Hoang Anh <v.anhlh33@vinai.io>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
gpt2 : Add gpt2 architecture integration (#4555)
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
scripts : do not sync commits from this repo
ggml : fix some mul mat cases + add tests for src1 F16 (ggml/669)
* fixed mul-mat error for old GPUs
* style fixes
* add mul mat src1 f16 test cases, fix more cases
ggml-ci
---------
Co-authored-by: bssrdf <bssrdf@gmail.com>
Co-authored-by: slaren <slarengh@gmail.com>
sync : ggml
ci : build with CLBlast + ggml-opencl use GGML_API (whisper/1576)
* Build with CLBlast
* Declare GGML_API
After rebasing, examples/talk-llama failed:
"D:\a\whisper.cpp\whisper.cpp\build\ALL_BUILD.vcxproj" (build target) (1) ->
"D:\a\whisper.cpp\whisper.cpp\build\examples\talk-llama\talk-llama.vcxproj" (default target) (14) ->
(Link target) ->
llama.obj : error LNK2019: unresolved external symbol ggml_cl_free_data referenced in function "public: __cdecl llama_model::~llama_model(void)" (??1llama_model@@QEAA@XZ) [D:\a\whisper.cpp\whisper.cpp\build\examples\talk-llama\talk-llama.vcxproj]
llama.obj : error LNK2019: unresolved external symbol ggml_cl_transform_tensor referenced in function "public: void __cdecl llama_model_loader::load_all_data(struct ggml_context *,void (__cdecl*)(float,void *),void *,struct llama_mlock *)" (?load_all_data@llama_model_loader@@QEAAXPEAUggml_context@@P6AXMPEAX@Z1PEAUllama_mlock@@@Z) [D:\a\whisper.cpp\whisper.cpp\build\examples\talk-llama\talk-llama.vcxproj]
D:\a\whisper.cpp\whisper.cpp\build\bin\Release\talk-llama.exe : fatal error LNK1120: 2 unresolved externals [D:\a\whisper.cpp\whisper.cpp\build\examples\talk-llama\talk-llama.vcxproj]
scripts : print list of sync commits
llama.swiftui : fix infinite loop, ouput timings, buff UI (#4674)
* fix infinite loop
* slight UI simplification, clearer UX
* clearer UI text, add timings to completion log
main-cmake-pkg : fix build issue (#4665)
* Fix main-cmake-pkg compilation
* Use glob to load common files
* cmake : fix trailing whitespace
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
server : allow to generate multimodal embeddings (#4681)
server : fix OpenAI server sampling w.r.t. penalty. (#4675)
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.
See mozilla-Ocho/llamafile@711344b
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.
See Mozilla-Ocho/llamafile@1cd334f
cmake : fix ld warning duplicate libraries libllama.a (#4671)
* fix "ld: warning: ignoring duplicate libraries: '../libllama.a'"
* fix warning in example.
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
...for simplicity
* *.nix: nixfmt
nix shell github:piegamesde/nixfmt/rfc101-style --command \
nixfmt flake.nix .devops/nix/*.nix
* flake.nix: add maintainers
* nix: move meta down to follow Nixpkgs style more closely
* nix: add missing meta attributes
nix: clarify the interpretation of meta.maintainers
nix: clarify the meaning of "broken" and "badPlatforms"
nix: passthru: expose the use* flags for inspection
E.g.:
```
❯ nix eval .#cuda.useCuda
true
```
* flake.nix: avoid re-evaluating nixpkgs too many times
* flake.nix: use flake-parts
* nix: migrate to pname+version
* flake.nix: overlay: expose both the namespace and the default attribute
* ci: add the (Nix) flakestry workflow
* nix: cmakeFlags: explicit OFF bools
* nix: cuda: reduce runtime closure
* nix: fewer rebuilds
* nix: respect config.cudaCapabilities
* nix: add the impure driver's location to the DT_RUNPATHs
* nix: clean sources more thoroughly
...this way outPaths change less frequently,
and so there are fewer rebuilds
* nix: explicit mpi support
* nix: explicit jetson support
* flake.nix: darwin: only expose the default
---------
Co-authored-by: Someone Serge <sergei.kozlukov@aalto.fi>
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
* improve check-requirements.sh
* small syntax change
* don't remove venvs if nocleanup is passed
* See if this fixes docker workflow
* Move check-requirements.sh into ./scripts/
---------
Co-authored-by: Jared Van Bortel <jared@nomic.ai>
cuda: fix vmm oom issue on NVIDIA AGX Orin (#4687)
Signed-off-by: hydai <hydai@secondstate.io>
clip : enable gpu backend (#4205)
* clip: enable CUDA backend
* add missing kernels
* add enough padding for alignment
* remove ggml_repeat of clip.cpp
* add metal backend
* llava : fixes
- avoid ggml_repeat
- use GGML_USE_ instead of CLIP_USE_ macros
- remove unused vars
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
clip : use ggml_backend_buffer_is_host (#4205)
CUDA: fix tensor core logic for Pascal and HIP (#4682)
ggml : add ggml_cpu_has_avx_vnni() (#4589)
* feat: add avx_vnni based on intel documents
* ggml: add avx vnni based on intel document
* llama: add avx vnni information display
* docs: add more details about using oneMKL and oneAPI for intel processors
* docs: add more details about using oneMKL and oneAPI for intel processors
* docs: add more details about using oneMKL and oneAPI for intel processors
* docs: add more details about using oneMKL and oneAPI for intel processors
* docs: add more details about using oneMKL and oneAPI for intel processors
* Update ggml.c
Fix indentation upgate
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
CUDA: fixed tensor cores not being used on RDNA3 (#4697)
clip : refactor + bug fixes (#4696)
* clip : refactor + bug fixes
ggml-ci
* server : add log message
ggml : add ggml_vdotq_s32 alias (#4715)
ggml-ci
flake.nix: expose full scope in legacyPackages
flake.nix: rocm not yet supported on aarch64, so hide the output
flake.nix: expose checks
workflows: nix-ci: init; build flake outputs
workflows: nix-ci: add a job for eval
workflows: weekly `nix flake update`
workflows: nix-flakestry: drop tag filters
...and add a job for flakehub.com
workflows: nix-ci: add a qemu job for jetsons
flake.nix: suggest the binary caches
flake.lock: update
to a commit recently cached by nixpkgs-cuda-ci
metal : enable shader debugging (cmake option) (#4705)
* ggml : disable fast-math for Metal (cmake build only)
ggml-ci
* metal : fix Metal API debug warnings
* cmake : add -fno-inline for Metal build (#4545)
* metal : fix API debug warnings
* metal : fix compile warnings
* metal : use uint64_t for strides
* cmake : rename option to LLAMA_METAL_SHADER_DEBUG
* metal : fix mat-vec Q8_0 kernel for BS > 1
* metal : normalize mat-vec kernel signatures
* cmake : respect LLAMA_QKK_64 option
* metal : fix mat-vec Q4_K kernel for QK_K == 64
ggml-ci
finetune: fix typo in README.md (#4733)
Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
py : re-enable mmap in convert hf (#4732)
* update: awq support llama-7b model
* update: change order
* update: benchmark results for llama2-7b
* update: mistral 7b v1 benchmark
* update: support 4 models
* fix: Readme
* update: ready for PR
* update: readme
* fix: readme
* update: change order import
* black
* format code
* update: work for bot mpt and awqmpt
* update: readme
* Rename to llm_build_ffn_mpt_awq
* Formatted other files
* Fixed params count
* fix: remove code
* update: more detail for mpt
* fix: readme
* fix: readme
* update: change folder architecture
* fix: common.cpp
* fix: readme
* fix: remove ggml_repeat
* update: cicd
* update: cicd
* uppdate: remove use_awq arg
* update: readme
* llama : adapt plamo to new ffn
ggml-ci
* fix: update torch version
---------
Co-authored-by: Trần Đức Nam <v.namtd12@vinai.io>
Co-authored-by: Le Hoang Anh <v.anhlh33@vinai.io>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
server : add --override-kv parameter (#4710)
* Changes to server to allow metadata override
* documentation
* flake.nix: expose full scope in legacyPackages
* flake.nix: rocm not yet supported on aarch64, so hide the output
* flake.nix: expose checks
* workflows: nix-ci: init; build flake outputs
* workflows: nix-ci: add a job for eval
* workflows: weekly `nix flake update`
* workflows: nix-flakestry: drop tag filters
...and add a job for flakehub.com
* workflows: nix-ci: add a qemu job for jetsons
* flake.nix: suggest the binary caches
* flake.lock: update
to a commit recently cached by nixpkgs-cuda-ci
---------
Co-authored-by: John <john@jLap.lan>
Co-authored-by: Someone Serge <sergei.kozlukov@aalto.fi>
editorconfig : fix whitespace and indentation #4710
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 <ggerganov@gmail.com>
* Fix llm_load_tensors: the asserts were not backcompat
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
llama : replace all API facing `int`'s with `int32_t` (#4577)
* replaced all API facing `int`'s with `int32_t`
* formatting and missed `int` in `llama_token_to_piece`
llama : llama_model_desc print number of experts
server : add token counts to html footer (#4738)
* server: add token counts to stats
* server: generate hpp
---------
Co-authored-by: phiharri <ph@got-root.co.uk>
metal : optimize ggml_mul_mat_id (faster Mixtral PP) (#4725)
* ggml : disable fast-math for Metal (cmake build only)
ggml-ci
* metal : fix Metal API debug warnings
* cmake : add -fno-inline for Metal build (#4545)
* metal : fix API debug warnings
* metal : fix compile warnings
* metal : use uint64_t for strides
* cmake : rename option to LLAMA_METAL_SHADER_DEBUG
* metal : fix mat-vec Q8_0 kernel for BS > 1
* metal : normalize mat-vec kernel signatures
* cmake : respect LLAMA_QKK_64 option
* metal : fix mat-vec Q4_K kernel for QK_K == 64
* metal : optimizing ggml_mul_mat_id (wip)
* metal : minor fix
* metal : opt mul_mm_id
server : throw an error when `slot unavailable` (#4741)
ggml : extend ggml_get_rows, ggml_repeat, ggml_concat (ggml/639)
* add more int ops
* ggml_compute_forward_dup_bytes
* add tests
* PR comments
* tests : minor indentations
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
scripts : fix sync order + metal sed
metal : add kernel_get_rows_i32
ggml-ci
sync : ggml
ggml-ci
cuda : mark I16 and I32 ops as unsupported
ggml-ci
cuda : simplify expression
Co-authored-by: slaren <slarengh@gmail.com>
swift : update Package.swift to use ggml as dependency (#4691)
* updates the package.swift to use ggml as dependency
* changes the ggml package url src to ggerganov
train : fix typo in overlapping-samples help msg (#4758)
This commit fixes a typo in the help message for the
--overlapping-samples option.
Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
llama.swiftui : fix build of ggml.metallib (#4754)
* metal: fix metal backend init failure in swiftui
* metal: build ggml.metallib instead of copy src
* llama.swift : remove debug flags from metallib build
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
ggml : include stdlib.h before intrin.h (#4736)
server : fix options in README.md (#4765)
* fix examples/server/README.md
* minor : fix whitespace
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
llama.swiftui : support loading custom model from file picker (#4767)
* swiftui: support load model from file picker
* swiftui: remove trailing whitespace
Print backend name on test-backend-ops failure (#4751)
server : send token probs for "stream == false" (#4714)
finetune : remove unused includes (#4756)
This commit removes unused includes from finetune.cpp.
Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
examples : add few-shot translation example (#4783)
ggml : do not sched_yield when calling BLAS (#4761)
* ggml : do not sched_yield when calling BLAS
ggml-ci
* ggml : fix do_yield logic
ggml-ci
* ggml : simplify do_yield logic
ggml-ci
ggml : add error handling to graph_compute (whisper/1714)
ggml : fix q2_k bpw in comments (ggml/680)
metal : switch back to default.metallib (ggml/681)
ggml-ci
flake.nix : fix typo (#4700)
betwen -> between
cmake : check for openblas64 (#4134)
openblas v0.3.22 64-bit pkg-config file is named openblas64.pc
https://github.com/OpenMathLib/OpenBLAS/issues/3790
examples : improve base-translate.sh script (#4783)
llama.swiftui : use correct pointer for llama_token_eos (#4797)
server : fix n_predict check (#4798)
ggml : use __builtin_amdgcn_sudot4 in __dp4a for gfx11 (#4787)
llama.swiftui : add visionOS target (#4805)
llama : print tensor meta for debugging
llama.swiftui : use llama.cpp as SPM package (#4804)
llama : remove redundant GQA check (#4796)
llama : remove unused vars (#4796)
CUDA: fixed redundant value dequantization (#4809)
llama-bench : add no-kv-offload parameter (#4812)
readme : add lgrammel/modelfusion JS/TS client for llama.cpp (#4814)
examples : add passkey test (#3856)
* examples : add passkey test
* passkey : better prints
* passkey : select pass key pos from CLI
* passkey : simplify n_past logic
* make : add passkey target
* passkey : add "self-extend"-like context extension (#4810)
* llama : "self-extend"-like context extension
* passkey : add comment
* passkey : add readme
main : add self-extend support (#4815)
* examples : add passkey test
* passkey : better prints
* passkey : select pass key pos from CLI
* passkey : simplify n_past logic
* llama : "self-extend"-like context extension
* passkey : add comment
* main : add Self-Extend support
* llama : add comment about llama_kv_cache_seq_div
llama.swiftui : update readme
swift : exclude ggml-metal.metal from the package (#4822)
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!
* Fixing tests
* PR suggestion
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
readme : add link to SOTA models
common : fix the short form of `--grp-attn-w`, not `-gat` (#4825)
See https://github.com/ggerganov/llama.cpp/blob/master/common/common.cpp#L230C53-L230C57
CUDA: faster softmax via shared memory + fp16 math (#4742)
ggml : fix vld1q_s8_x4 32-bit compat (#4828)
* ggml : fix vld1q_s8_x4 32-bit compat
ggml-ci
* ggml : fix 32-bit ARM compat (cont)
ggml-ci
server : add api-key flag to documentation (#4832)
Document the api-key flag added to server in https://github.com/ggerganov/llama.cpp/pull/4441
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.
* minor : fix trailing whitespace
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
scripts : script to get Paul Graham essays in txt format (#4838)
readme : add 3rd party collama reference to UI list (#4840)
Add a VSCode extension for llama.cpp reference to UI list
scripts : improve get-pg.sh (#4838)
metal : improve dequantize precision to match CPU (#4836)
ggml-ci
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.
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 <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>
Python script to compare commits with llama-bench (#4844)
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.
llama : recognize 1B phi models (#4847)
This update categorizes models with 24 layers as MODEL_1B, ensuring compatibility with different Phi model variants without impacting existing Phi-2 model functionality.
llama : add additional suffixes for model params (#4834)
* llm_load_print_meta: Add additional suffixs for model params
* Update llama.cpp model param log
remove unneeded comments and convert from > to >=
server : add a `/health` endpoint (#4860)
* added /health endpoint to the server
* added comments on the additional /health endpoint
* Better handling of server state
When the model is being loaded, the server state is `LOADING_MODEL`. If model-loading fails, the server state becomes `ERROR`, otherwise it becomes `READY`. The `/health` endpoint provides more granular messages now according to the server_state value.
* initialized server_state
* fixed a typo
* starting http server before initializing the model
* Update server.cpp
* Update server.cpp
* fixes
* fixes
* fixes
* made ServerState atomic and turned two-line spaces into one-line
server : fix build + rename enums (#4870)
server : update readme to document the new `/health` endpoint (#4866)
* added /health endpoint to the server
* added comments on the additional /health endpoint
* Better handling of server state
When the model is being loaded, the server state is `LOADING_MODEL`. If model-loading fails, the server state becomes `ERROR`, otherwise it becomes `READY`. The `/health` endpoint provides more granular messages now according to the server_state value.
* initialized server_state
* fixed a typo
* starting http server before initializing the model
* Update server.cpp
* Update server.cpp
* fixes
* fixes
* fixes
* made ServerState atomic and turned two-line spaces into one-line
* updated `server` readme to document the `/health` endpoint too
fix : cuda order of synchronization when setting a buffer (ggml/679)
* fix : cuda order of synchronization when setting a buffer
* also sync before memcpy
---------
Co-authored-by: slaren <slarengh@gmail.com>
Fix execlp call (ggml/689)
NULL can be an integer constant expression with the value zero, in this case the behavior would be undefined because of an incorrect type being passed to the variable arguments.
ggml : change GGML_MAX_NAME at compile time (ggml/682)
* change GGML_MAX_NAME to 128
* allow controlling the value of GGML_MAX_NAME through external macro definitions
metal : wrap each operation in debug group (ggml/690)
ggml : remove ggml_cpy_inplace and ggml_cont_inplace (ggml/693)
metal : fix deprecation warning (ggml/690)
sync : ggml
metal : put encoder debug group behind a define (#4873)
server : fix typo in model name (#4876)
main : print total token count and tokens consumed so far (#4874)
* Token count changes
* Add show token count
* Updating before PR
* Two requested changes
* Move param def posn
ci: nix-flake-update: new token with pr permissions (#4879)
* ci: nix-flake-update: new token with pr permissions
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
server : add `LOG_INFO` when model is successfully loaded (#4881)
* added /health endpoint to the server
* added comments on the additional /health endpoint
* Better handling of server state
When the model is being loaded, the server state is `LOADING_MODEL`. If model-loading fails, the server state becomes `ERROR`, otherwise it becomes `READY`. The `/health` endpoint provides more granular messages now according to the server_state value.
* initialized server_state
* fixed a typo
* starting http server before initializing the model
* Update server.cpp
* Update server.cpp
* fixes
* fixes
* fixes
* made ServerState atomic and turned two-line spaces into one-line
* updated `server` readme to document the `/health` endpoint too
* used LOG_INFO after successful model loading
server : support for multiple api keys (#4864)
* server: added support for multiple api keys, added loading api keys from file
* minor: fix whitespace
* added file error handling to --api-key-file, changed code to better
reflect current style
* server: update README.md for --api-key-file
---------
Co-authored-by: Michael Coppola <info@michaeljcoppola.com>
server : implement credentialed CORS (#4514)
* Implement credentialed CORS according to MDN
* Fix syntax error
* Move validate_api_key up so it is defined before its first usage
swift : pin ggml commit + remove ggml.h from spm-headers (#4878)
ggml-ci
ggml : SOTA 2-bit quants (add IQ2_XS) (#4856)
* iq2_xs: basics
* iq2_xs: this should have been in the basics
* iq2_xs: CUDA and scalar CPU works
* iq2_xs: WIP Metal
* iq2_xs: Metal now works
* iq2_xs: working, but dog slow, ARM_NEON dot product
* iq2_xs: better ARM_NEON dot product
We are now at 19.5 t/s for TG-128 and 61 t/s for PP-512 when
running on the CPU.
* iq2_xs: AVX2 dot product - 19.5 t/s
* iq2_xs: faster AVX2 dit product
21.4 t/s for TG-128, 59.2 t/s for PP-512.
The latter is 2x compared to the previous version.
* iq2_xs: had forgotten to delete iq2-data.h
* Add llama enum for IQ2_XS
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
llama : restore intended k-quants mixes for MoE models (#4872)
* Restore intended k-quants quantization mixes for MoE models
* Update Q2_K_S values in the quantize tool
Still using LLaMA-v1 PPL values in the quant description
today does not make much sense. But let's leave this update
for another PR.
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
swift : track ggml release branch (#4867)
main : disable token count by default (#4874)
main : better name for variable n_print (#4874)
server : fix infill when prompt is empty (#4833)
Importance Matrix calculation (#4861)
* imatrix: 1st version
* imatrix: WIP
* Cleanup
* Update examples/imatrix/imatrix.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
llama : fix llm_build_k_shift to use correct n_rot (#4889)
* llama : fix llm_build_k_shift to use correct n_rot
ggml-ci
* llama : always use hparams.n_rot for ggml_rope_custom
ggml-ci
* convert : fix persimmon conversion to write correct n_rot
py : fix lint (#4889)
common : streamline the formatting of help (#4890)
* common : streamline the formatting of help
- Separate alternative parameters by a comma
- Do not indent `--version` differently
* Update common/common.cpp
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
llama : fix typo "imp_embd" -> "inp_embd"
CUDA: fix softmax compile for old CUDA versions (#4862)
gitignore : imatrix
llama.swiftui : update models layout (#4826)
* Updated Models Layout
- Added a models drawer
- Added downloading directly from Hugging Face
- Load custom models from local folder
- Delete models by swiping left
* trimmed trailing white space
* Updated Models Layout
export-lora : use LLAMA_FILE_MAGIC_GGLA (#4894)
This commit replaces the magic number used in export-lora.cpp with
the one defined in llama.h, which is indirectly included via common.h.
Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
llama : remove redundant assert for StableLM (#4901)
llama : ggml-backend integration (#4766)
* llama : ggml-backend integration
* ggml-backend : add names to buffers
* fix unmap after loading
* batched-bench : add tensor_split param
* llama : check for null tensor_split
* ggml-backend : increase GGML_MAX_BACKENDS
* improve graph splitting, partial fix for --no-kv-offload
* cuda : add ggml-backend split buffer support
* cuda : do not create buffer types for devices that don't exist (fixes usage without CUDA devices available)
* ggml : fix null backend dereference (#4807)
* ggml : fix null backend dereference
* ggml : also check ggml_backend_is_cpu
* test-backend-ops : check buffer allocation failures
* llama : add cparam (split_mode) and command line argument (--split-mode, -sm) to configure the split mode (none, layer or row)
* ggml : fix mul_mat_id work size
* llama : rewrite session kv load/set without graphs
* minor
* llama : only initialize used backends, free backends on context free
* llama : abort ctx if cuda backend init fails
* llama : rewrite lora with ggml-backend and compute on CPU
ggml-ci
* llama : only map to a backend buffer the region of the file mapping containing the tensors used in the buffer
* opencl : add ggml-backend buffer type
* cuda : only use batched_cublas with batched mat muls (fixes fp16 tg perf)
* llama : on Metal, by default offload the full model
ggml-ci
* metal : page align the data ptr (#4854)
* Apply suggestions from code review
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* cuda : fix split buffer free
* address review comments
* llama-bench : add split-mode parameter
* fix whitespace
* opencl : fix double initialization
* server : add --split-mode parameter
* use async copy and compute to improve multi-gpu performance
ggml-ci
* use async memcpys to copy the graph outputs to the CPU
* fix opencl
* use a host buffer for the cpu compute buffer for faster copies to the gpu
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
CUDA: faster q8_0 -> f16 dequantization (#4895)
examples : add pydantic models to GBNF grammar generator (#4883)
* Create pydantic-models-to-grammar.py
* Added some comments for usage
* Refactored Grammar Generator
Added example and usage instruction.
* Update pydantic_models_to_grammar.py
* Update pydantic-models-to-grammar-examples.py
* Renamed module and imported it.
* Update pydantic-models-to-grammar.py
* Renamed file and fixed grammar generator issue.
backend_sched : fix assignments
ggml-ci
ggml : fix 32-bit ARM compat for IQ2_XS (whisper/1758)
* ggml : fix 32-bit ARM compat
* ggml : fix fix
* ggml : fix fix fix
sync : ggml
convert : update phi-2 to latest HF repo (#4903)
* convert : update phi-2 to latest HF repo
ggml-ci
* py : try to fix flake stuff
server : fix crash with multimodal models without BOS token (#4904)
server : fix deadlock that occurs in multi-prompt scenarios (#4905)
* * fix deadlock
* * dont ruint all whitespace
compare-llama-bench: tweak output format (#4910)
metal : refactor kernel loading code (#4794)
* metal : detect more GPU families
* metal : refactor kernel loading
* metal : set kernel family requirements
* metal : fix kernel init + fix compile options
* metal : take into account simdgroup reduction support
* metal : print only skipped kernels
* metal : fix check for simdgroup reduction support
* metal : check for Metal 3
* metal : free allocations
* metal : normalize encoder:setComputePipelineStatus calls
ggml-ci
* metal : fix Metal3 family check
ggml-ci
* metal : check for simdgroup matrix mul. feature
ggml-ci
gguf : fix potential infinite for-loop (#4600)
Co-authored-by: Bernhard Gstrein <gstrein@informatik.uni-freiburg.de>
main : add parameter --no-display-prompt (#4541)
* add the parameter : --no-display-prompt , combine with --log-disable it will display only the generated tokens
* remove empty line
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
workflows: unbreak nix-build-aarch64, and split it out (#4915)
The fix should be just the `sudo apt-get update`
llama : minimize size used for state save/load (#4820)
* examples : save-load-state: save only required state
* llama : only reserve n_vocab * n_batch at most for logits
llama_decode asserts that only n_batch tokens are passed each call, and
n_ctx is expected to be bigger than n_batch.
* llama : always reserve n_vocab * n_batch for logits
llama_context de-serialization breaks if the contexts have differing
capacity for logits and llama_decode will at maximum resize to
n_vocab * n_batch.
* llama : only save and restore used logits
for batch sizes of 512 this reduces save state in the best case by
around 62 MB, which can be a lot if planning to save on each message
to allow regenerating messages.
* llama : use ostringstream and istringstream for save and load
* llama : serialize rng into minimum amount of space required
* llama : break session version due to serialization changes
metal : disable log for loaded kernels (#4794)
llama : fix detokenization of non-special added-tokens (#4916)
Co-authored-by: goerch <jhr.walter@t-online.de>
server : fix prompt caching with system prompt (#4914)
metal : remove old API (#4919)
ggml-ci
ggml: cache sin/cos for RoPE (#4908)
sync : ggml
Make Q3_K_S be the same as olf Q3_K_L for Mixtral-8x7B (#4906)
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2-bit quantizations (#4897)
* imatrix: load
* imatrix: WIP
* imatrix: Add Q2_K quantization
* imatrix: also guard against Q2_K_S quantization without importance matrix
* imatrix: guard even more against low-bit quantization misuse
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
llama : support WinXP build with MinGW 8.1.0 (#3419)
metal : correctly set SIMD support flags on iOS (#4923)
* Correctly set support_simdgroup_reduction and support_simdgroup_mm on iPhone/iPad
* log a little bit more info on iOS
Fix ffn_down quantization mix for MoE models (#4927)
* Fix ffn_down quantization mix for MoE models
In #4872 I did not consider the part where every third
tensor is quantized with more bits. Fir MoE this leads to tensors
of the same layer being quantized with different number of bits,
which is not considered as a possibility in the inference implementation
(it is assumed all experts use the same quantization).
* Fix the fix
* Review suggestion
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
llama : use LLAMA_LOG_ macros for logging
scripts : sync-ggml-am.sh option to skip commits
llama : check LLAMA_TRACE env for extra logging (#4929)
* llama : minor fix indent
* llama : check LLAMA_TRACE env for extra logging
ggml-ci
Add ability to use importance matrix for all k-quants (#4930)
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
llama : fix missing quotes (#4937)
CUDA: faster dequantize kernels for Q4_0 and Q4_1 (#4938)
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
llama : check for 256 divisibility for IQ2_XS, IQ2_XXS (#4950)
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
cuda : fix dequantize kernel names (#4938)
awq-py : fix typo in awq-py/README.md (#4947)
llama : apply classifier-free guidance to logits directly (#4951)
pass cpu-architecture arguments only to host code (C;C++) (#4943)
speculative : threading options (#4959)
* speculative: expose draft threading
* fix usage format
* accept -td and -tbd args
* speculative: revert default behavior when -td is unspecified
* fix trailing whitespace
finetune : use LLAMA_FILE_MAGIC_GGLA (#4961)
This commit replaces the magic number LLAMA_FILE_MAGIC_LORA used in
finetune.cpp with LLAMA_FILE_MAGIC_GGLA defined in llama.h.
Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
ggml : introduce GGML_CALL function annotation (#4850)
This change makes it possible to build ggml-cuda.cu and ggml-metal.m as
independent dynamic shared objects, that may be conditionally linked at
runtime in a multiplatform binary. It introduces a GGML_CALL annotation
that documents which functions have a cyclic call relationship, between
the application code and GPU modules.
This change does nothing, unless the build defines -DGGML_MULTIPLATFORM
which causes back-references and function pointers to conform to MS ABI
which is supported by NVCC, ROCm, XCode, GCC and Clang across platforms
examples : fix and improv docs for the grammar generator (#4909)
* Create pydantic-models-to-grammar.py
* Added some comments for usage
* Refactored Grammar Generator
Added example and usage instruction.
* Update pydantic_models_to_grammar.py
* Update pydantic-models-to-grammar-examples.py
* Renamed module and imported it.
* Update pydantic-models-to-grammar.py
* Renamed file and fixed grammar generator issue.
* Fixed some issues and bugs of the grammar generator. Imporved Documentation
* Update pydantic_models_to_grammar.py
metal : log `recommendedMaxWorkingSetSize` on iOS 16+ (#4936)
* metal: Log `recommendedMaxWorkingSetSize` on iOS 16+
* Only log on iOS and macOS, ignoring tvOS and other platforms
* Check for Xcode version before using recommendedMaxWorkingSetSize
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
metal : replace loop of dispatch_async with dispatch_apply (#4934)
* Replace loop of dispatch_async with dispatch_apply
* Update ggml-metal.m
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
android : introduce starter project example (#4926)
* Introduce starter project for Android
Based on examples/llama.swiftui.
* Add github workflow
* Set NDK version
* Only build arm64-v8a in CI
* Sync bench code
* Rename CI prop to skip-armeabi-v7a
* Remove unused tests
metal : localized logic in `ggml_metal_graph_compute` (#4924)
* Metal: Localized logic in `ggml_metal_graph_compute`, minor performance improvement
* Whitespace
* Collecting command buffer completions on single t…
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This PR completes the integration of llama.cpp with ggml-backend. The main change is that partial offloading is handled through
ggml_backend_sched, and it is supported with all the backends. This also implements multi-GPU support by offloading different layers to different GPUs, instead of splitting matrices by rows. While this does not achieve the same level of parallelism as row-level splitting, it requires less synchronization and it may be more efficient in some cases.There is still some work to do, but layer-level multi-GPU with CUDA should already work. For me with WSL, this is significantly faster than the current row-level splitting, so I am opening this now in case anyone wants to give it a try.
-tscan be used as usual to configure the fraction of layers to offload to each GPU.TODO
ggml_backend_schedFixes #4055