* Speedup tokenization
On current master it takes ~3.2 seconds to tokenize
Wikitext. With this change it becomes ~525 ms.
* Fixit: it was missing the piece after the last found occurence
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Make ggml-cuda.cu build with QK_K = 64
Using LLAMA_CUDA_FORCE_DMMV = ON and -nommq it runs and produces
a meaningful result.
* k_quants tuning for Falcon-7b
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* tests : write a Python tokenizer test (wip)
* llama : prefix input text for tokenization with whitespace
* llama : distinguish pieces from decoded text + fix detokenization
* common : add comments
* examples : no longer manually add leading space when tokenizing
* tests : use Python to generate tokenizer tests for C++
* tests : add option to tokenize text files
ggml-ci
* tests : add test-tokenizer-1.py
* llama.cpp : fix LF token
* hellaswag : move the concat space for clarity
* tests : add falcon tests (py + cpp, currently do not pass Unicode)
ggml-ci
* common : temporary separate llama_detokenize calls for SPM and BPE
---------
Co-authored-by: klosax <131523366+klosax@users.noreply.github.com>
The use of special characters within source files can break compiling on some computers with different region and language settings. Using Unicode escape sequences should allow for the code to be compiled on all setups without needing to change your computers settings or switch regions.
* llama.cpp : fix spm whitespace escaping + clean up
* main.cpp : spm - add whitespace in front of prompt
* test-tokenizer-0.cpp : spm - add whitespace in front of prompt
* Add llama_beam_search().
* Add '// Beam search' heading to llama.{h,cpp} after llama_grammar_accept_token().
* Add space around * pointers and & references.
* Add spaces around comparison and assignment operators.
* Prefer west const.
* Use llama_ prefix for structs in global namespace.
* Delete obsolete comment from an earlier revision.
* Change eos to eob in llama_beam and llama_beam_view structs.
* use hipblas based on cublas
* Update Makefile for the Cuda kernels
* Expand arch list and make it overrideable
* Fix multi GPU on multiple amd architectures with rocblas_initialize() (#5)
* add hipBLAS to README
* new build arg LLAMA_CUDA_MMQ_Y
* fix half2 decomposition
* Add intrinsics polyfills for AMD
* AMD assembly optimized __dp4a
* Allow overriding CC_TURING
* use "ROCm" instead of "CUDA"
* ignore all build dirs
* Add Dockerfiles
* fix llama-bench
* fix -nommq help for non CUDA/HIP
---------
Co-authored-by: YellowRoseCx <80486540+YellowRoseCx@users.noreply.github.com>
Co-authored-by: ardfork <134447697+ardfork@users.noreply.github.com>
Co-authored-by: funnbot <22226942+funnbot@users.noreply.github.com>
Co-authored-by: Engininja2 <139037756+Engininja2@users.noreply.github.com>
Co-authored-by: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com>
Co-authored-by: jammm <2500920+jammm@users.noreply.github.com>
Co-authored-by: jdecourval <7315817+jdecourval@users.noreply.github.com>
* metal: better memory alloc w/ concurrency dispatch
The ggml-alloc should only free tensors at memory barriers.
* ggml-alloc: avoid return silently
In certain cases, the allocate_node() function may silently return
without performing any memory allocation.
* Improve UNK, BOS, EOS token handling when converting without metadata.
* Allow importing as a module.
* Remove some obsolete code and minor cleanups.
* Set default UNK token mapping from -1 to 0 in llama.cpp
* Try to handle overflow due to buggy Windows Python with a better error message
* Improve LLaMA-2 2-, 3- and 4-bit quantization
* Q3_K_S: use Q5_K for 1st 2 layers of attention.wv and feed_forward.w2
* Q4_K_S: use Q6_K for 1st 2 layers of attention.wv and feed_forward.w2
* Q2_K and Q3_K_M: use Q5_K instead of Q4_K for 1st 2 layers of
attention.wv and feed_forward.w2
This leads to a slight model sized increase as follows:
Q2_K : 2.684G vs 2.670G
Q3_K_S: 2.775G vs 2.745G
Q3_K_M: 3.071G vs 3.057G
Q4_K_S: 3.592G vs 3.563G
LLaMA-2 PPL for context 512 changes as follows:
Q2_K : 6.6691 vs 6.8201
Q3_K_S: 6.2129 vs 6.2584
Q3_K_M: 6.0387 vs 6.1371
Q4_K_S: 5.9138 vs 6.0041
There are improvements for LLaMA-1 as well, but they are
way smaller than the above.
* Minor 4-bit quantization improvement
For the same model size as previus commit, we get
PPL = 5.9069 vs 5.9138.
* Some more fine tuning
* Adding make_qkx2_quants
With it, we get PPL = 5.8828 for L2-7B Q4_K_S.
* Another minor improvement
* Q2_K improvement
Smaller model, lower perplexity.
7B: file size = 2.632G, PPL = 6.3772 vs original 2.670G PPL = 6.8201
12B: file size = 5.056G, PPL = 5.4577 vs original 5.130G PPL = 5.7178
It is mostly Q3_K except for tok_embeddings, attention.wq, attention.wk,
which are Q2_K
* Iterating
* Revert Q5_K back to make_qkx1_quants
* Better Q6_K
* make_qkx2_quants is better for Q5_K after all
* Fix after rebasing on master
* Fix for changed tensor names
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
use a different function for no_alloc to avoid breaking backwards compat, fixes lora
remove 512 n_batch limit
fixed 2048 batch size
cleanup
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* llama : add benchmark example
* add to examples CMakeLists.txt
* fix msvc build
* add missing include
* add Bessel's correction to stdev calculation
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* improve markdown formatting
* add missing include
* print warning is NDEBUG is not defined
* remove n_prompt and n_gen from the matrix, use each value separately instead
* better checks for non-optimized builds
* llama.cpp : fix MEM_REQ_SCRATCH0 reusing the value of n_ctx of the first call
* fix json formatting
* add sql output
* add basic cpu and gpu info (linx/cuda only)
* markdown: also show values that differ from the default
* markdown: add build id
* cleanup
* improve formatting
* formatting
---------
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* metal: matrix-matrix multiplication kernel
This commit removes MPS and uses custom matrix-matrix multiplication
kernels for all quantization types. This commit also adds grouped-query
attention to support llama2 70B.
* metal: fix performance degradation from gqa
Integers are slow on the GPU, and 64-bit divides are extremely slow.
In the context of GQA, we introduce a 64-bit divide that cannot be
optimized out by the compiler, which results in a decrease of ~8% in
inference performance. This commit fixes that issue by calculating a
part of the offset with a 32-bit divide. Naturally, this limits the
size of a single matrix to ~4GB. However, this limitation should
suffice for the near future.
* metal: fix bugs for GQA and perplexity test.
I mixed up ne02 and nb02 in previous commit.
* add log_callback to llama_context_params for custom logging.
* Fix macro expansion on gcc
* Add struct llama_state for global variables and move log_callback there
* Turn log level into enum and some minor changes.
* Remove model_for_logging parameter (not needed anymore)
* Convert remaining fprintf(stderr, ...) calls to use new macros.
* Fix enum and initialize g_state
* Fix log calls after merge
* Fix missing static
* Add back all the new lines in the logging strings
* Add comment for llama_log_callback and replace remaining printf calls
---------
Co-authored-by: grahameth <->
Co-authored-by: Helmut <helmut.buhler@inf.h-brs.de>
* added stream saving context data to file to avoid allocating unnecessary amounts of memory
* generalised copying state data to file or buffer
* added comments explaining how copy_state_data works
* fixed trailing whitespaces
* fixed save load state example
* updated save load state to use public function in llama.cpp
* - restored breakage of the llama_copy_state_data API
- moved new logic for copying llama state data to internal function
* fixed function declaration order
* restored save load state example
* fixed whitepace
* removed unused llama-util.h include
* Apply suggestions from code review
Co-authored-by: slaren <slarengh@gmail.com>
* Apply code review suggestions
Co-authored-by: slaren <slarengh@gmail.com>
---------
Co-authored-by: slaren <slarengh@gmail.com>
* ggml : add graph tensor allocator
* ggml : don't calculate data pointer of unallocated tensors when creating a view with an offset
* ggml : refactor ggml_view_Nd into ggml_view_tensor_offset
* ggml : graph allocation in contexts
* allocate work buffer as a ggml_object in ggml_graph_compute_with_ctx
* llama.cpp : allocate graph in the context
* add GGML_PAD
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* metal: concurrently dispatch commands
Function `ggml_metal_graph_find_concurrency` will run and write
commands that can be issued concurrently to metal context `concur_list`
array, when `ggml_metal_graph_compute` is called for the first time.
* metal: don't call find_concurrency automatically.
* metal : code style changes
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* make rms_norm_eps a parameter
* add rms_norm_eps to command line
* fix baby llama, test-grad0
* use scientific notation for eps param in the help
ggml-ci
* llama, main : constrain sampling to grammar
* allow loading grammar from file
* fix whitespace errors
* handle & print parser errors
* add comments to grammar syntax and allow newlines where unambiguous
* add missing include
* support alternates in root rule
* fix bugs with empty token and EOS
* adjust JSON grammar
* remove swp file
* rewrite ternary expressions
Co-authored-by: Henri Vasserman <henv@hot.ee>
* use struct for grammar elements and add Unicode support
* add unicode escapes
* add inverse char ranges
* only sample full tokens (no peeking or truncation)
* llama : minor style changes
blindly applied in online editor - hopefully I didn't break something
* update help text
* add warning message if EOS is disabled
---------
Co-authored-by: Henri Vasserman <henv@hot.ee>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* ci : run ctest
ggml-ci
* ci : add open llama 3B-v2 tests
ggml-ci
* ci : disable wget progress output
ggml-ci
* ci : add open llama 3B-v2 tg tests for q4 and q5 quantizations
ggml-ci
* tests : try to fix tail free sampling test
ggml-ci
* ci : add K-quants
ggml-ci
* ci : add short perplexity tests
ggml-ci
* ci : add README.md
* ppl : add --chunks argument to limit max number of chunks
ggml-ci
* ci : update README
* Implement customizable RoPE
The original RoPE has pre-defined parameters
theta_i = 10000^(−2(i−1)/d), for i in [1, 2, ..., d/2]
Our customizable RoPE, ggml_rope_custom_inplace, uses
theta_i = scale * base^(−2(i−1)/d), for i in [1, 2, ..., d/2]
with the default matches the original
scale = 1.0
base = 10000
The new command line arguments
--rope-freq-base
--rope-freq-scale
set the two new RoPE parameter.
Recent researches show changing these two parameters extends the context limit with minimal loss.
1. Extending Context to 8K
kaiokendev
https://kaiokendev.github.io/til#extending-context-to-8k
2. Extending Context Window of Large Language Models via Positional Interpolation
Shouyuan Chen, Sherman Wong, Liangjian Chen, Yuandong Tian
https://arxiv.org/abs/2306.15595
3. NTK-Aware Scaled RoPE allows LLaMA models to have extended (8k+) context size without any fine-tuning and minimal perplexity degradation.
https://www.reddit.com/user/bloc97https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
For the bold, try adding the following command line parameters to your favorite model:
-c 16384 --rope-freq-base 80000 --rope-freq-scale 0.5
* ggml-metal: fix custom rope
* common: fix argument names in help
* llama: increase MEM_REQ_EVAL for MODEL_3B
It avoids crashing for quantized weights on CPU.
Better ways to calculate the required buffer size would be better.
* llama: make MEM_REQ_EVAL depend on n_ctx
* server: use proper Content-Type in curl examples
Without the header Content-Type: application/json, curl will POST with
Content-Type: application/x-www-form-urlencoded
Though our simple server doesn't care, the httplib.h used has a limit
with CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH 8192
With Content-Type: application/json, we can send large json data.
* style : minor fixes, mostly indentations
* ggml : fix asserts
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Initial implementation
* Remove debug print
* Restore signature of llama_init_from_gpt_params
* Free guidance context
* Make freeing of guidance_ctx conditional
* Make Classifier-Free Guidance a sampling function
* Correct typo. CFG already means context-free grammar.
* Record sampling time in llama_sample_classifier_free_guidance
* Shift all values by the max value before applying logsoftmax
* Fix styling based on review
* This allows LLAMA models that were previously incompatible with K quants to function mostly as normal. This happens when a model has a vocab != 32000, e.g 32001 which means it's not divisible by 256 or 64. Since the problematic dimensions only apply for `tok_embeddings.weight` and `output.weight` (dimentions 4096 x n_vocab), we can simply quantize these layers to Q8_0 whereas the majority of the hidden layers are still K-quanted since they have compatible dimensions.
* Fix indentation
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* As an alternative, to avoid failing on Metal due to lack of Q8_0 support, instead quantize tok_embeddings.weight to Q4_0 and retain output.weight as F16. This results in a net gain of about 55mb for a 7B model compared to previous approach, but should minimize adverse impact to model quality.
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* MPI support, first cut
* fix warnings, update README
* fixes
* wrap includes
* PR comments
* Update CMakeLists.txt
* Add GH workflow, fix test
* Add info to README
* mpi : trying to move more MPI stuff into ggml-mpi (WIP) (#2099)
* mpi : add names for layer inputs + prep ggml_mpi_graph_compute()
* mpi : move all MPI logic into ggml-mpi
Not tested yet
* mpi : various fixes - communication now works but results are wrong
* mpi : fix output tensor after MPI compute (still not working)
* mpi : fix inference
* mpi : minor
* Add OpenMPI to GH action
* [mpi] continue-on-error: true
* mpi : fix after master merge
* [mpi] Link MPI C++ libraries to fix OpenMPI
* tests : fix new llama_backend API
* [mpi] use MPI_INT32_T
* mpi : factor out recv / send in functions and reuse
* mpi : extend API to allow usage with outer backends (e.g. Metal)
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* ggml_graph_compute: deprecate using ggml_context, try resolve issue #287
* rewrite: no longer consider backward compitability; plan and make_plan
* minor: rename ctx as plan; const
* remove ggml_graph_compute from tests/test-grad0.c, but current change breaks backward
* add static ggml_graph_compute_sugar()
* minor: update comments
* reusable buffers
* ggml : more consistent naming + metal fixes
* ggml : fix docs
* tests : disable grad / opt + minor naming changes
* ggml : add ggml_graph_compute_with_ctx()
- backwards compatible API
- deduplicates a lot of copy-paste
* ci : enable test-grad0
* examples : factor out plan allocation into a helper function
* llama : factor out plan stuff into a helper function
* ci : fix env
* llama : fix duplicate symbols + refactor example benchmark
* ggml : remove obsolete assert + refactor n_tasks section
* ggml : fix indentation in switch
* llama : avoid unnecessary bool
* ggml : remove comments from source file and match order in header
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* use javascript generators as much cleaner API
Also add ways to access completion as promise and EventSource
* export llama_timings as struct and expose them in server
* update readme, update baked includes
* llama : uniform variable names + struct init
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Generalize quantize_fns for simpler FP16 handling
* Remove call to ggml_cuda_mul_mat_get_wsize
* ci : disable FMA for mac os actions
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* convert checks in llama_load_session_file to throw and handle them
* make llama_load_session_file_internal static
* address feedbacks to avoid using exceptions
* Remove multiple shards
* Remove multiple file loaders
* Remove llama_load_tensor_shard class
* Simplify load logic
* Remove dead code guess_n_parts function
* Remove vocab_only from constructor of llama_model_loader
* Remove alignment_prevents_mmap which is not more needed.
* Remove useless check
* add interface for float input
* fixed inpL shape and type
* add examples of input floats
* add test example for embd input
* fixed sampling
* add free for context
* fixed add end condition for generating
* add examples for llava.py
* add READMD for llava.py
* add READMD for llava.py
* add example of PandaGPT
* refactor the interface and fixed the styles
* add cmake build for embd-input
* add cmake build for embd-input
* Add MiniGPT-4 example
* change the order of the args of llama_eval_internal
* fix ci error
* detect NUMA systems and pin work threads to nodes (linux)
* disable mmap prefetch/readahead for NUMA systems
* avoid sending finalize op to thread pool if it does nothing
* silence robot
* fix args
* make --numa a param
* recommendation that n_nodes evenly divide n_threads did not warrant such aggressive enforcement
* lower synchronization overhead
* statically allocate
* move numa state to g_state
* add description for --numa
* ggml : minor style changes
* ggml : minor style + try fix sanitizer build
* llama : allow to initialize backend with NUMA support
* llama : avoid ggml include in llama-util.h
* ggml : style / formatting
* ggml : fix handling of ops with n_threads > n_tasks > 1
* server : utilize numa parameter
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* k_quants: WIP super-blocks with 64 weights
* k_quants: WIP super-blocks with 64 weights
Q6_K scalar and AVX2 works
* k_quants: WIP super-blocks with 64 weights
Q4_K scalar and AVX2 works
* k_quants: WIP super-blocks with 64 weights
Q2_K scalar and AVX2 works. Q2_K is way too slow (it is actually slower
than the scalar implementation)
* k_quants: WIP super-blocks with 64 weights
Q3_K scalar and AVX2 works.
* k_quants: WIP super-blocks with 64 weights
Q5_K scalar and AVX2 works, and with that all
k_quants are done on AVX2 and scalar
* k_quants: WIP super-blocks with 64 weights
Q6_K working on CUDA. Cannot make it run quite as gast as
with super-blocks with 256 weigths: 8% slower on 4080,
20% slower on the 1660 (but there we fit 1 less layer on the
GPU because pf the larger model size), so some fraction of
these 20% is due to that,
* k_quants: WIP super-blocks with 64 weights
Q4_K working on CUDA. ~10% slower on GTX-1660,
16% slower on 4080.
* k_quants: WIP super-blocks with 64 weights
Q2_K working on CUDA. ~3% slower on GTX-1660,
10% slower on 4080.
* k_quants: WIP super-blocks with 64 weights
Q3_K working on CUDA.
* k_quants: WIP super-blocks with 64 weights
Q5_K working on CUDA, and with this CUDA is done.
* k_quants: WIP super-blocks with 64 weights
Q6_K working on ARM_NEON
* k_quants: WIP super-blocks with 64 weights
Q4_K working on ARM_NEON, but quite a bit slower than 256 weights
* k_quants: WIP super-blocks with 64 weights
Q2_K working on ARM_NEON, but quite a bit slower than 256 weights
* k_quants: WIP super-blocks with 64 weights
Q3_K working on ARM_NEON, but quite a bit slower than 256 weights.
* k_quants: WIP super-blocks with 64 weights
Q5_K working on ARM_NEON, but quite a bit slower than 256 weights.
With that, we have full support for ARM_NEON, although
performance is not quite there.
* k_quants: WIP super-blocks with 64 weights
Slightly more efficient Q3_K and Q5_K
* k_quants: WIP super-blocks with 64 weights
Another small improvement for Q3_K and Q5_K on ARM_NEON
* k_quants: WIP super-blocks with 64 weights
Yet another speedup for Q5_K on ARM_NEON.
We are now within 10% of the QK_K = 256 version.
* k_quants: WIP super-blocks with 64 weights
* We are able to pass preprocessor macros to the Metal
compiler
* Q6_K works and is actually slightly more efficient than
the QK_K = 256 version (25.2 ms vs 25.8 ms)
* k_quants: WIP super-blocks with 64 weights
Q4_K works on Metal and is actually slightly faster
than QK_K = 256 (21.95 ms vs 24.0 ms).
* k_quants: WIP super-blocks with 64 weights
Q2_K works on Metal and is very slightly faster
than QK_K = 256 (23.8 ms vs 24.2 ms).
* k_quants: WIP super-blocks with 64 weights
Q3_K works on Metal and is slightly faster
than QK_K = 256 (26.6 ms vs 28.3 ms).
* k_quants: WIP super-blocks with 64 weights
Q5_K works on Metal and is slightly faster
than QK_K = 256 (23.7 ms vs 26.3 ms).
* k_quants: call them _K, not _k, also on Metal
* k_quants: correctly define QK_K in llama.cpp
* Fixed bug in q4_K quantization added with the 64-block addition
* Simplify via lambda
* k_quants: swicth Q3_K to 4-bit scales when QK_K = 64
Otherwise there isn't much benefit from this
quantization type. There is some very slight loss
in accuracy, but we reduce size by ~7%.
E.g., for OpenLLaMA-3B, Q3_K_S perplexity is
8.6131 with 8-bit scales and 8.6352 with 4-bit,
while file size decreases from 1.53G to 1.44G.
* k_quants: switch Q4_K to 4-bit scales when QK_K = 64
Here the loss in accuracy is greater than for Q3_K,
but the Q4_K points still move further to the left on
the perplexity vs size curve.
* k_quants: forgot to add the Metal changes in last commit
* k_quants: change Q5_K to be type 0 when QK_K = 64
Still needs AVX2 implementation
* k_quants: AVX2 implementation for new 64-weight Q5_K
* k_quants: 10% faster ARM_NEON Q5_K dot product
* k_quants: fixed issue caused by merging with master
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Fix top-p sampling to match the standard definition (smallest set that has probability mass at least p, not largest set with probability mass less than p)
* top-p: correct gt to gte
* add test for correct top-p behavior
* llama : make model stateless and context stateful
* llama : minor cleanup
* llama : update internal API declaration
* Apply suggestions from code review
fix style
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Missing model memory release
* Fix style
* Add deprecated warning for public API function llama_init_from_file
* Update public API use cases: move away from deprecated llama_init_from_file
* Deprecate public API function llama_apply_lora_from_file
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* fixed issue: memory is not guaranteed to be aligned properly during ggml_init call from loading saved sessions
* - removed commented out old code from fix
- updated another instance of same issue below original
* Only use Q6_K for output weights if tensor size is multiple of 256
* Fixed copy/paste mistake
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Convert vector to f16 for dmmv
* compile option
* Added compilation option description to README
* Changed cmake CUDA_ARCHITECTURES from "OFF" to "native"
* Fix examples/metal
* k-quants: prevent usage when tensor size is not divisible by 256
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* metal : handle buffers larger than device's maxBufferLength
* metal : print more verbose device info + handle errors
* metal : fix prints for overlapping views
* metal : minimize view overlap to try to utilize device memory better
Georgi Gerganov
Kawrakow
Przemysław Pawełczyk
Tim Miller
Cebtenzzre
klosax
Matt Pulver
slaren
Henri Vasserman
Shouzheng Liu
Evan Jones
Kerfuffle
goerch
Jhen-Jie Hong
grahameth
Johannes Gäßler
l3utterfly
eric8607242
Rand Xie
Christian Demsar
Guillaume "Vermeille" Sanchez
Rinne
Alex Klinkhamer
Xiao-Yong Jin
Bach Le
LostRuins
Evan Miller
oobabooga
Qingyou Meng
Tobias Lütke
Stephan Walter
Howard Su
Aaron Miller
m3ndax
ningshanwutuobang
zrm
Alex Renda
Didzis Gosko
Ettore Di Giacinto
Borislav Stanimirov