2-bit quantizations #4897

ikawrakow · 2024-01-12T14:37:02Z

This PR adds quantization methods for the IQ2_XXS and IQ2_XS quants added in #4773 and #4856. It also adds a new quantization method for Q2_K quants that utilizes the importance matrix computed as per PR #4861.

I have disallowed IQ2_XXS and IQ2_XS quantization without an importance matrix as one gets just garbage without.

Be warned: IQ2_XXS and IQ2_XS quantization is very slow, at least as compared to other llama.cpp quantization types. Quantizing a 7B models takes in the range of 1.5 minutes (32-core Ryzen 5975WX) or 5 minutes (M2 Max). Larger models take proportionally longer. I advise you to go get a coffee when you start a Mixtral-8x7B or LLaMA-v2-70B quantization.

I have reported perplexities elsewhere, so will not repeat here. Results with this PR are close to what what has been reported, but do not match 100%. This is due to the fact that importance matrix based quantization is still missing for the other quants. I will add this later in a separate PR.

To use this for a model some_model, utilizing wiki.train.raw to create the importance matrix, and quantizing to IQ2_XXS:

./imatrix -m some_model -f wiki.train.raw -o imatrix_some_model.dat --chunks 100
./quantize --imatrix imatrix_some_model.dat some_model quantized_model.gguf iq2_xxs

Note: the imatrix calculation currently only works if run on the CPU. Hence, if you are using a build that uses the GPU, you need a separate, CPU-only build to run the imatrix command. Metal works. Not sure in what way the call to the data collection function is being bypassed when CUDA is enabled.

I have computed the importance matrix using wiki.train.raw for quite a few models. If there is an interest, I can post these somewhere for easy download.

…trix

kalomaze · 2024-01-12T16:15:54Z

Do you see any potential value in calculating this with some additional "high entropy" / nearly random data instead of just wikitext?
My concern with calibration datasets is that they are too narrow in scope to cover the "important" activations of the full model. But that's concerning mainly exllama quantization, and the importance matrix might not work as similarly to conventional calibration approaches, I'm guessing.

In analyzing the probabilities of the next token for nearly random data on a Mistral 7b finetune:

Token 1: 2.458356%
Token 2: 2.151364%
Token 3: 1.375462%
Token 4: 0.917227%
Token 5: 0.675354%
Token 6: 0.578321%
Token 7: 0.540716%
Token 8: 0.499979%
Token 9: 0.467090%
Token 10: 0.443426%

There is an interesting pattern; the output probabilities aren't totally even as one might expect, and fluctuate wildly from token to token. Maybe this "random" data is a good way to measure outlier activations in the model? I'm not very familiar with quantization math so feel free to correct me if this seems wrong

JianbangZ · 2024-01-12T16:20:30Z

So make sure I understand right

important matrix is mandatory for iq2
for other k quant or non k quant (q2_k, q3_k_m, q4_0), important matrix is not mandatory.

Are there general quant imporvment when not using important matrix?

ikawrakow · 2024-01-12T17:04:19Z

So make sure I understand right

important matrix is mandatory for iq2

for other k quant or non k quant (q2_k, q3_k_m, q4_0), important matrix is not mandatory.

Are there general quant imporvment when not using important matrix?

Yes, I made the usage of an importance matrix computed with the imatrix tool mandatory for IQ2_XXS, IQ2_XS and Q2_K_S.

Currently there is no utilization of an importance matrix for all other quants. I will submit another PR in the next days that adds the ability to use an importance matrix also for the other quants, but that will be optional.

The reason to make it mandatory is that you get garbage without taking into account the different importance of model weights when using low-bit quantizations.

Q2_K_S Is a new quantization type that I added in PR #4872. It is an (almost) pure 2.5625 bpw quantization. This is in contrast to the existing Q2_K, which is mostly 3-bit except for very few tensors where 2.5625 bits are used. But note that the intended mix of 2- and 3-bit quants did not work for MoE models. Hence, before #4872, Q2_K behaved as what now became Q2_K_S, with the effect that Huggingface is now full of mostly useless Q2_K quantized models for Mixtral-8x7B and derivatives.

ikawrakow · 2024-01-12T17:11:58Z

Do you see any potential value in calculating this with some additional "high entropy" / nearly random data instead of just wikitext?
My concern with calibration datasets is that they are too narrow in scope to cover the "important" activations of the full model. But that's concerning mainly exllama quantization, and the importance matrix might not work as similarly to conventional calibration approaches, I'm guessing.

I'm actually hoping that, once this PR is accepted, people will come up with all sorts of creative ideas about what kind of training data one should be using to create the importance matrix. Apart from your idea to use a high-entropy training dataset (please try and see what happens), in my mind there are many open ~~equations~~ questions. E.g., what kind of training data should we use for multi-language models or for chat/instruct tuned models? Can we use this this to better quantize non-LLM models such as whisper.cpp, multi-modal models, etc., and if so, what kind of training data is needed? Etc.

ggerganov · 2024-01-12T17:16:59Z

ggml-quants.c

+    }
+}
+
+size_t quantize_q2_K(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) {


I think it would make sense to rename quant_weights to imatrix to match the header declaration in ggml-quants.h

I was thinking about that too and decided against it: at this point in the code, the data is not really a matrix, but just an array of floats that gets used as weights in the weighted RMSE minimization for a row of model weights. If I called it a "matrix", perhaps somebody could be thinking that this is an N x N matrix for a row of N weights (which would be the case if one would be using the full <a a^T> or squared gradients. I did call it imatrix (for "importance matrix") in the quantize tool and in the llama.cpp quantization function because it has that meaning there, and the actual content of the data is mostly opaque.

JianbangZ · 2024-01-12T17:22:02Z

I think it should be "-o imatrix_some_model.dat", instead of "-ow". a typo.

ggerganov · 2024-01-12T17:28:04Z

llama.cpp

+                fprintf(stderr, "\n\n============================================================\n");
+                fprintf(stderr, "Missing importance matrix for tensor %s in a very low-bit quantization\n", tensor->name);
+                fprintf(stderr, "The result will be garbage, so bailing out\n");
+                fprintf(stderr, "============================================================\n\n");


Within the llama.cpp source file, it is better to utilize the LLAMA_LOG_... macros instead of printf and fprintf so that the users have control over the log messages (i.e. be able to disable / forward / etc.)

But do we want the user to be able to disable this message? Apart from these 4, there are 31 other uses of fprintf in the llama.cpp source file. But sure, if you think that LLAMA_LOG_... is better, I can change it.

Some projects require to either disable the logs or display the errors in some UI for example. The rest of the fprintf need to be fixed as well. You can leave it like this, I will resolve all of those after we merge

ikawrakow · 2024-01-12T17:28:34Z

I think it should be "-o imatrix_some_model.dat", instead of "-ow". a typo.

I actually used -ow which comes from the long version --output-weight. Perhaps not the best choice. But with -o I was worried that this is used or may get used in the future in the common GPT parameter parsing as -o is such a common choice. So I did not use that on purpose.

JianbangZ · 2024-01-12T17:35:46Z

I think it should be "-o imatrix_some_model.dat", instead of "-ow". a typo.

I actually used -ow which comes from the long version --output-weight. Perhaps not the best choice. But with -o I was worried that this is used or may get used in the future in the common GPT parameter parsing as -o is such a common choice. So I did not use that on purpose.

-ow gives me core dump error

./test_imatrix.sh
terminate called after throwing an instance of 'std::invalid_argument'
what(): stoi
./test_imatrix.sh: line 3: 328278 Aborted (core dumped) ./imatrix -m /data1/models/qwen/Qwen-14B-Chat/ -f /data1/datasets/wikitext-2-raw/wiki.train.raw -ow /data1/models/qwen/Qwen-14B-Chat/gguf2/imatrix_qwen14bchat_wiki_en.dat --chunks 100

without -ow, it passes. But the generated imatrix.dat is empty.

Tried mistral 7B model as well, same thing

kalomaze · 2024-01-12T17:35:59Z

I'm actually hoping that, once this PR is accepted, people will come up with all sorts of creative ideas about what kind of training data one should be using to create the importance matrix. Apart from your idea to use a high-entropy training dataset (please try and see what happens), in my mind there are many open ~~equations~~ questions. E.g., what kind of training data should we use for multi-language models or for chat/instruct tuned models? Can we use this this to better quantize non-LLM models such as whisper.cpp, multi-modal models, etc., and if so, what kind of training data is needed? Etc.

I'm going to try generating high-ish temperature generations on the original base Mistral 7b model instead of purely random text; It makes sense to me intuitively that, diverse, high entropy data generated by the model itself would be the best data to measure the importance of its activations [and that it doesn't necessarily need to be "correct" data, as long as it's diverse and matches the rough distribution of expected model outputs well]

ikawrakow · 2024-01-12T17:57:30Z

-ow gives me core dump error

Yes, sorry, I was confused. It is indeed -o.

ikawrakow · 2024-01-12T18:07:00Z

without -ow, it passes. But the generated imatrix.dat is empty.

Ah, you need a build without CUDA. I'll update the instructions. While trying to get it going, it is best to increase the verbosity. If you use

./imatrix -m some_model -f some_training_data -o some_output --chunks 100 --verbosity 2

You should see something like

collect_imatrix[0]: blk.0.attn_k.weight, 4096 x 512, 0
collect_imatrix[1]: blk.0.attn_v.weight, 4096 x 512, 0
collect_imatrix[1]: blk.0.attn_q.weight, 4096 x 512, 0
collect_imatrix[1]: blk.0.attn_output.weight, 4096 x 512, 0
collect_imatrix[1]: blk.0.ffn_gate.weight, 4096 x 512, 0
collect_imatrix[1]: blk.0.ffn_up.weight, 4096 x 512, 0
collect_imatrix[1]: blk.0.ffn_down.weight, 11008 x 512, 0
collect_imatrix[1]: blk.1.attn_k.weight, 4096 x 512, 0
collect_imatrix[1]: blk.1.attn_v.weight, 4096 x 512, 0
collect_imatrix[1]: blk.1.attn_q.weight, 4096 x 512, 0
collect_imatrix[1]: blk.1.attn_output.weight, 4096 x 512, 0
collect_imatrix[1]: blk.1.ffn_gate.weight, 4096 x 512, 0
collect_imatrix[1]: blk.1.ffn_up.weight, 4096 x 512, 0
collect_imatrix[1]: blk.1.ffn_down.weight, 11008 x 512, 0
collect_imatrix[1]: blk.2.attn_k.weight, 4096 x 512, 0
collect_imatrix[1]: blk.2.attn_v.weight, 4096 x 512, 0
collect_imatrix[1]: blk.2.attn_q.weight, 4096 x 512, 0
collect_imatrix[1]: blk.2.attn_output.weight, 4096 x 512, 0
collect_imatrix[1]: blk.2.ffn_gate.weight, 4096 x 512, 0
collect_imatrix[1]: blk.2.ffn_up.weight, 4096 x 512, 0
collect_imatrix[1]: blk.2.ffn_down.weight, 11008 x 512, 0
collect_imatrix[1]: blk.3.attn_k.weight, 4096 x 512, 0
collect_imatrix[1]: blk.3.attn_v.weight, 4096 x 512, 0
collect_imatrix[1]: blk.3.attn_q.weight, 4096 x 512, 0
collect_imatrix[1]: blk.3.attn_output.weight, 4096 x 512, 0
collect_imatrix[1]: blk.3.ffn_gate.weight, 4096 x 512, 0
collect_imatrix[1]: blk.3.ffn_up.weight, 4096 x 512, 0
collect_imatrix[1]: blk.3.ffn_down.weight, 11008 x 512, 0
collect_imatrix[1]: blk.4.attn_k.weight, 4096 x 512, 0
collect_imatrix[1]: blk.4.attn_v.weight, 4096 x 512, 0
collect_imatrix[1]: blk.4.attn_q.weight, 4096 x 512, 0
collect_imatrix[1]: blk.4.attn_output.weight, 4096 x 512, 0
collect_imatrix[1]: blk.4.ffn_gate.weight, 4096 x 512, 0
collect_imatrix[1]: blk.4.ffn_up.weight, 4096 x 512, 0
collect_imatrix[1]: blk.4.ffn_down.weight, 11008 x 512, 0
...

If you don't see these log messages with --verbosity 2, it means that the call to the data collection is being bypassed, which happens on CUDA.

JianbangZ · 2024-01-12T18:14:25Z

I'm actually hoping that, once this PR is accepted, people will come up with all sorts of creative ideas about what kind of training data one should be using to create the importance matrix. Apart from your idea to use a high-entropy training dataset (please try and see what happens), in my mind there are many open ~~equations~~ questions. E.g., what kind of training data should we use for multi-language models or for chat/instruct tuned models? Can we use this this to better quantize non-LLM models such as whisper.cpp, multi-modal models, etc., and if so, what kind of training data is needed? Etc.

I'm going to try generating high-ish temperature generations on the original base Mistral 7b model instead of purely random text; It makes sense to me intuitively that, diverse, high entropy data generated by the model itself would be the best data to measure the importance of its activations [and that it doesn't necessarily need to be "correct" data, as long as it's diverse and matches the rough distribution of expected model outputs well]

I don't think you necessarily need the model's output to calibrate itself. But I do think if the target model is a instruct model (chat model), using some sort of SFT datset, and control the calibration process that each conversation datapoint is fed as a whole should result in better results. technically.

ggerganov · 2024-01-12T18:17:57Z

Btw, don't pay attention to the failing iOS and Swift builds - these are expected to fail when changing the ggml.h header

kalomaze · 2024-01-12T18:19:00Z

I don't think you necessarily need the model's output to calibrate itself. But I do think if the target model is a instruct model (chat model), using some sort of SFT datset, and control the calibration process that each conversation datapoint is fed as a whole should result in better results. technically.

My reasoning for this is that calibration datasets for specific domains (e.g. instruction datasets for calibration) are often too narrow in domain/formatting to be good generalized measurements of weight importance, as was observed in exllama2's quantization scheme (where The Pile and less "structured" pretrain-esque data seemed to give better results).

For example, the entirety of the wikitext train file that was used in this PR's example doesn't contain the word "cybersecurity" once out of 20,000 lines, but I did see that term mentioned in my test generations.

Basically, I am looking to deliberately counteract the bias of typical calibration datasets.

JianbangZ · 2024-01-12T18:24:10Z

I don't think you necessarily need the model's output to calibrate itself. But I do think if the target model is a instruct model (chat model), using some sort of SFT datset, and control the calibration process that each conversation datapoint is fed as a whole should result in better results. technically.

My reasoning for this is that calibration datasets for specific domains (e.g. instruction datasets for calibration) are often too narrow in domain/formatting to be good generalized measurements of weight importance. For example, the entirety of the wikitext train that was used in this PR's example doesn't contain the word "cybersecurity" once, but I saw that mentioned in my test generations. Essentially, I am looking to deliberately counteract the bias of typical calibration datasets

I see now. I like your idea of letting model to generate its own data. At the end of day, the quantization should remain in the original model’s means, instead of do another round of finetuing with external data. I also worry using wiki.train will cause overfitting to have a artificial low PPL number on wiki.test

8XXD8 · 2024-01-13T12:51:12Z

I did some experiments with 3 different datasets for the importance matrix calculations.
I used Mistral 7b instruct as the base model.
F16 PPL = 6.7077 +/- 0.04371

wiki.train.dat
imatrix calculation: Final estimate: PPL = 7.0983 +/- 0.11899
iq2_xxs perplexity: Final estimate: PPL = 8.4120 +/- 0.05422

The Bible
imatrix calculation: Final estimate: PPL = 1.1913 +/- 0.00602
iq2_xxs perplexity: Final estimate: PPL = 8.7087 +/- 0.05715

1Mb of random characters, generated with tr -dc '[:alnum:] \n' </dev/urandom | head -c 1M > rng.txt
imatrix calculation: Final estimate: PPL = 200.9160 +/- 2.64542
iq2_xxs perplexity: Final estimate: PPL = 8.7203 +/- 0.05645

There is a gigantic difference between the imatrix PPL, and the measured final wiki.test.raw perplexity.
I tried chatting with the models, they all seem coherent, and I could not notice any obvious style difference, but I haven't done any in depth reasoning tests.

I think imatrix could generate its own random data when -o is not specified, since the result is usable, although not the lowest ppl.

I used master with Rocm backend for the perplexity measurements.

ggerganov · 2024-01-13T18:45:02Z

tests/test-backend-ops.cpp

@@ -56,7 +56,7 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m
        GGML_ASSERT(size % ggml_blck_size(tensor->type) == 0);
        std::vector<uint8_t> dataq(ggml_row_size(tensor->type, size));
        int64_t hist[16];
-        ggml_quantize_chunk(tensor->type, data.data(), dataq.data(), 0, size, hist);
+        ggml_quantize_chunk(tensor->type, data.data(), dataq.data(), 0, size/tensor->ne[0], tensor->ne[0], hist, nullptr);


I guess test-backend-ops could generate a dummy importance matrix (say full of 1.0f) just to be able to perform the 2-bit quantizations - even if the values would not make sense. For backend comparisons it should be enough. This way we will be able to add the IQ2 quants to the tests.

We can do this later

ikawrakow · 2024-01-14T11:34:23Z

@8XXD8

Thank you for these interesting results.

I have the ability to do 2-bit quantization without an importance matrix. For Mistral-7B-Instruct-v0.2 and IQ2_XXS I get a perplexity of 356.2850, so basically completely useless (and this is why I have disallowed quantization without an importance matrix). From this, and from your results, we can conclude

Any importance matrix can be useful when quantizing with very few bits, even random data or The Bible, to prevent the quantized model to go crazy
Using a broader text corpus that is more representative of common language use (such as wiki.train.raw) is better than random or narrow-scope text (such as The Bible)

Given the perplexity of the training run using The Bible, we can conclude that Mistral-7B-Instruct basically knows The Bible by heart, which I personally find quite funny.

sorasoras · 2024-01-14T19:17:20Z

@8XXD8

Thank you for these interesting results.

I have the ability to do 2-bit quantization without an importance matrix. For Mistral-7B-Instruct-v0.2 and IQ2_XXS I get a perplexity of 356.2850, so basically completely useless (and this is why I have disallowed quantization without an importance matrix). From this, and from your results, we can conclude

Any importance matrix can be useful when quantizing with very few bits, even random data or The Bible, to prevent the quantized model to go crazy

Using a broader text corpus that is more representative of common language use (such as wiki.train.raw) is better than random or narrow-scope text (such as The Bible)

Given the perplexity of the training run using The Bible, we can conclude that Mistral-7B-Instruct basically knows The Bible by heart, which I personally find quite funny.

encounter error when quantize for q2xs
[ 51/ 323] blk.2.ffn_down.weight - [13696, 5120, 1, 1], type = f16, quantizing to iq2_xs .. GGML_ASSERT: D:\a\llama.cpp\llama.cpp\ggml-quants.c:9003: n_per_row%QK_K == 0
GGML_ASSERT: D:\a\llama.cpp\llama.cpp\ggml-quants.c:9003: n_per_row%QK_K == 0
GGML_ASSERT: D:\a\llama.cpp\llama.cpp\ggml-quants.c:9003: n_per_row%QK_K == 0
GGML_ASSERT: D:\a\llama.cpp\llama.cpp\ggml-quants.c:9003: n_per_row%QK_K == 0

Nexesenex · 2024-01-15T22:59:00Z

Forgive me for this question, provoked by 8XXD8's tests :
Could the iMatrix of these new quants (or the calibration dataset when quantizing on Exllama) be used as "the training of the poor" accordingly to the dataset used, this in order to give a particular coloration to any given model at the stage of its quantization?

* 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>

gotzmann · 2024-02-26T15:28:02Z

Forgive me for this question, provoked by 8XXD8's tests : Could the iMatrix of these new quants (or the calibration dataset when quantizing on Exllama) be used as "the training of the poor" accordingly to the dataset used, this in order to give a particular coloration to any given model at the stage of its quantization?

@Nexesenex I think, yes. Depending on style of training materials, it might add some flavour.

* 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>

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 --------- 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 --------- 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 --------- 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> --------- 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 --------- 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…

Kawrakow added 5 commits January 12, 2024 09:41

imatrix: load

e9372e4

imatrix: WIP

8da2b25

imatrix: Add Q2_K quantization

75f4cbf

imatrix: also guard against Q2_K_S quantization without importance ma…

d5598f7

…trix

imatrix: guard even more against low-bit quantization misuse

f342143

ikawrakow mentioned this pull request Jan 12, 2024

SOTA 2-bit quants - part 2 #4856

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ggerganov reviewed Jan 12, 2024

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ggerganov approved these changes Jan 13, 2024

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ikawrakow merged commit 147b17a into master Jan 14, 2024
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ikawrakow deleted the ik/quantize-iq2 branch January 14, 2024 07:46

ikawrakow mentioned this pull request Jan 14, 2024

Add importance matrix calculation to non-CPU back-ends #4931

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ikawrakow mentioned this pull request Jan 29, 2024

SOTA 3-bit quants #5196

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2-bit quantizations #4897

2-bit quantizations #4897

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