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* Fixed CUDA RoPE
* ggml_cuda_mul_mat_vec_p021
* ggml_cuda_scale
* ggml_cuda_diag_mask_inf
* ggml_is_permuted
* ggml_cuda_cpy
* flatten rows for ggml_cuda_op
* Added a --low-vram option
* Fixed Windows performance
* Fixed LLAMA_CUDA_DMMV_Y > 1 for WizardLM
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* add python wrapper
https://gist.github.com/abetlen/2b90e5f153f6efd00931d098de5c73ce
* fix decoding error. adds errors=ignore parameter
* add python bindings for functions to get and set the whole llama state
(rng, logits, embedding and kv_cache)
* update python bindings
* add text generating baby-llama from scratch example
* fix race condition bug in ggml_compute_forward_diag_mask_f32
* implement ggml_soft_max_back for more performant backward pass of soft_max
avoids creating big intermediate matrices of size n_embd x n_embd for llama layers and n_vocab x n_vocab for cross entropy loss
* improve softmax backward pass
go from quadratic runtime to linear runtime by simplifying the formulas
* fix race condition bug in non-inplace ggml_compute_forward_diag_mask_f32
memcpy needs to be synchronized across threads to avoid race conditions.
=> do it in INIT phase
* fix bug in ggml_compute_forward_soft_max_back_f32 on DEBUG build
* improve performance of mul_mat backward pass
avoid transpose by using mul_mat with swapped arguments
* avoid printing too much newlines in baby-llama-text
* activate threading in baby-llama-text
* add ggml_out_prod and use it for mul_mat backward pass for improved performance
performance stats report improvement from 37 seconds to 16 seconds runtime during my training tests
* better weight initialization improves training convergence at start
* better weight initialization improves training convergence at start
* improve ggml_out_prod performance
- change iteration order (>15s -> 10s runtime)
- parallelize over one more dimension: over dst matrix rows (10s -> <5s runtime)
* add llama sampler, shuffle samples and constrain sampling to tokens occurring in train data
* fix get_samples call, add model tensor names, increase model size, start training samples after newline
* save train trained model to checkpoint and load model to be trained from checkpoint
* use inplace functions where possible
* initialize rng with srand
* use different arguments for input and output checkpoint
* ggml fixes to support backward pass on inplace operations
* remove duplicate include
* fix cross entropy loss
- add target probabilities for each sample which is then used in cross entropy loss
* print used memory before and after optimization
* sample with non-greedy sampling parameters at the end of training
* add cmake target for baby-llama-text
* add ggml_add1_inplace to header
* enable gradient propagation for inplace add1 and scale operations
those functions backward passes don't need the original src0, so they also work when forward is inplace
* implement AdamW in ggml_opt_adam by adding weight decay parameter (default 0.001f)
also add a schedule parameter (default 1.0f) that can be used to scale alpha and decay according to learning schedule.
setting the decay parameter to zero disables AdamW resulting in normal Adam optimizer.
since the difference between Adam and AdamW is minimal it is not implemented as another optimizer, but integrated into the existing Adam optimizer.
* use inplace operations in cross_entropy_loss
* fix random weight initialization scale
* add missing default parameters for adam optimizer
* add ggml_opt_context, so that we can properly resume training
otherwise the optimizer states, tracking statistics about the error function and its derivates,
will reset to zero each time ggml_opt is called, hindering convergence on resumed training.
now the optimizer context and all its memory is stored in a separate struct.
* fix bug in llama_sample_token_mirostat_v2
when all candidates are filtered out through mu threshold, the following soft_max operation will fail.
so keep at least one.
* add forward function without using cache, for more performant training
during training on whole samples no cache is required.
removing the cache and simplifying the remaining code results in performance and memory usage improvement.
* print suppressed newline tokens as string "\n"
printing too much actual newlines is suppressed to avoid flooding the console.
* store optimizer state in training checkpoint and add learning schedule
persistent optimizer state allows to resume training without resetting the optimizer
learning schedule consists of linear warmup ramp followed by cosine decay with restarts
* remove unused functions
* fix bug in get_samples which corrupted training targets
* save checkpoint only when it was trained
* simplify code
* remove trailing whitespace
* simplify backward pass for SQRT
* replace inefficient repeat backward pass with dedicated repeat_back operation
* add ggml_cross_entropy_loss with backward pass for faster training
cross entropy loss can also be implemented using softmax and log, but as dedicated operation it is faster and especially avoids unnecessary memory overhead.
* add tests for cross_entropy_loss backward pass
finite differences regularly results in estimated gradient of zero, despite the backward pass giving non zero gradient.
_probably_ the finite differences fails due to numerical issues
* use ggml_cross_entropy_loss in text training example
* remove trailing whitespace
* slightly improve how cross entropy loss is compute
btw: directly implemented cross entropy loss seems to have way lower magnitudes than when implemented with softmax and log.
probably the input to log gets closer to zero due to float numerics.
maybe the multiplication by (1.0-eps)/sum is more accurate..
* add llama_get_vocab to get the vocabulary as output parameters
* set default model.type for unknown models with few layers
* add export of training checkpoint to llama compatible model file
* get vocabulary for exporting training checkpoint to llama compatible model file
* implement backward pass of flash attention
* bugfixes for backward pass of flash attention
* test flash attention backward pass
need to set loose error bounds to pass.
the finitie differences are close to numeric limits and often return quite different values than the backward pass.
reducing eps further lets the gradients vanish completely.
likewise setting eps to big results in wronger values.
the softmax in the middle of the function is probably the most responsible for the numeric issues using finite differences.
* add option to train with flash attention and move options to the top of the main function
training from scratch also works with flash attention
training convergence and generation results after fix number of iterations are worse than when not using flash attention.
maybe there still lingers a bug in the flash attention backward pass?
but training works, just with slower convergence.
flash attention is still worth to use, because it requires way less memory and is faster with high n_ctx
* add train_params and command line option parser
* remove unnecessary comments
* add train params to specify memory size
* remove python bindings
* rename baby-llama-text to train-text-from-scratch
* replace auto parameters in lambda function
* add #include <climits>
* add explicit cast to fix compile error
"error: non-constant-expression cannot be narrowed from type 'int64_t' (aka 'long long') to 'uint32_t' (aka 'unsigned int') in initializer list [-Wc++11-narrowing]"
* remove trailing whitespace
* add ggml_opt_resume_g which accepts forward and backward cgraphs
* fix formulas in comments
* bug fix for ggml_compute_forward_get_rows_back_f32
the result should be set to zero, not to whatever data is in opt0
* improve training memory usage with scratch buffers
instead of relying on the automatic backward pass, we manually create the graph for the backward pass.
it turns out that all backward pass operations need only temporary memory which can be reused after each layer.
will compute backward pass for ALL model parameters
* add option to use scratch buffers in training or not
make it configurable because currently training with scratch buffers implies flash attention and optimization over all parameters.
* ci : disable temporary
* store view offset and permute axes in opt[0] instead of storing it in padding
use memcpy to store offset, because offset is of type size_t.
when storing it as int32_t offset would have to be smaller than 2^31 which is not necessarily true.
* minor : fix compile warnings + minor style changes
* fix bug in threaded indices calculation of ggml_compute_forward_flash_attn_back_f32
* store view offset like in master branch
* bug fix in forward_batch_wo_cache_flash_attn_train
* scratch buffer bug fixes in forward_batch_wo_cache_flash_attn_train
data of permute and reshape is the same as their input.
if we want to preserve the output of permute/reshape, we also need to preserve their inputs.
replace reshape(src0, src1) with reshape_nd calls so that we don't need src1.
replace (temporary) t03 with ggml_repeat(ctx0, layer.attention_norm, t02).
in the future we could also use the new broadcasting ggml_mul to avoid these repeat calls.
for this we need backward pass of broadcasting ggml_mul.
* remove unnecessary scratch buffer 0
buf 0 is persistent memory, so we can just disable scratch for this by using buf -1
* avoid creating unnecessary grad tensors
previously we need to create grads for model parameters, so that expand(..) correctly populates cgraph->leafs & cgraph->grads
this wasted memory, because unnecessary grad for each op were automatically created:
the automatically generated grad was unnecessary because we later manually set the grad (e.g. t35->grad = expand(gb, ...) ).
this discarded the automatically generated grad resulting in wasted memory.
improved this by changing expand(..) to not use ggml_build_forward_expand.
expand set cgraph->nodes but not the leafs.
cgraph->leafs & cgraph->grads are set in another pass after the last expand call.
* print used training seed
* zero initialize gfbuf and gbbuf
* ci : re-enable workflows + add README for training
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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* Allow "quantizing" to f16 and f32
Fix an issue where quantizing didn't respect LLAMA_NO_K_QUANTS
Add brief help to the list of quantization types in the quantize tool
Ignore case for quantization type arguments in the quantize tool
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* metal : improve q4_K
28.3 -> 26.0 ms/token by avoiding a branch in the
calculation of the scales.
* metal : small improvement for Q4_K
* metal : still optimizing Q4_K
This commit pushes it down to 25.3 ms / token.
The crazy idea of using 6 bits for the scales is really costly on
Metal: if I remove the bit fiddling necessary to make the block
scales, time goes almost to the Q4_0 23 ms/token.
Before pushing the k-quants upstream I had a Q4_K variant that
had used 8-bit scales. It wasn't more accurate, used 0.125 bits more per weight,
was running slightly slower on the CPU (due to the larger model size
and being memory bound there), and the difference was entirely
negligible under CUDA. So, I decided to publish the version with 6-bit
scales. Perhaps I should re-consider and change to 8-bit scales?
* metal : some more optimizations
Q2_K: 25.4 ms/token
Q6_K: 27.3 ms/token
Q4_0: 22.8 ms/token
Q4_1: 23.1 ms/token
* metal : Q3_K support
Something is not quite right yet.
* metal : Q5_K support
Initial version achieves 31.2 ms/token, 210 GB/s
* metal : still not able to figure out why q3_K does not work
* Minor
* metal : yet another failed attempt to make q3_K work
* metal : optimize Q5_K
31.2 ms -> 27.8 ms.
250 GB/s.
* metal : q3_K still not working
Adding a heavily commented q3_K metal kernel to explain
my obviously faulty logic. Perhaps someone could spot the issue?
* metal : q3_K finally working
Not optimized at all.
What was the issue? The scales are not 4-bytes aligned,
and I was accessing them with a uint32_t pointer.
When I tried that on CUDA, I got an error (illegal memory access)
and added a memcpy to a local array of 3 uint32_t's.
But on Metal it told me there is no memcpy, so I tried
accessing directly. There is no error, just garbage results.
At some point I did try accessing the scales with an uint16_t
pointer (the scales are for sure 2-byte aligned), but was
still getting garbage. I guess, there must have been another bug.
No access to scales is via a uint16_t pointer and, after starting
from scratch from the C dequantize function, it finally works.
* metal : Q3_K 1st optimization pass
* metal : Q3_K second optimization pass - 29.6 ms/token
* metal : Q3_K cleanup
* metal : fixed accidentally broken Q2_K
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* Rebase to latest
* Show progress
* Add assert to make sure we only allocate temp buffer for non-CPU backend tensor
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
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Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
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from 16/32bit (#1691)
* Add support for quantizing already quantized models
* Threaded dequantizing and f16 to f32 conversion
* Clean up thread blocks with spares calculation a bit
* Use std::runtime_error exceptions.
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* Add opencl release memory
* Rename function name
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* CUDA multi GPU + scratch
ggml_cuda_compute_forward
Tensor parallelism
ggml_cuda_add
ggml_cuda_rms_norm
ggml_cuda_silu
CUDA scratch buffer
--main-gpu CLI option
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Co-authored-by: Spencer Sutton <Spencer.Sutton@precisely.com>
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std::exception (#1599)
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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* Use MTLDevice.newBufferWithBytesNoCopy to share buffers between CPU and GPU
* Page-align buffers used by Metal
* Remove trailing whitespace
* Only import unistd.h for Metal builds
* metal : remove unnecessary copies
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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* Starting to add k-quantization to ggml
I think it is better to have quantization separate from
ggml. For now just adding the k-quants there, but it would be
better to also factor out the existing ggml quantizations.
* Adding Q3_K and Q8_K (de)-quantization
* Q3_K now working on CUDA and AVX2/scalar
CUDA is not ideal - ~50% slower than Q4_0 for
single token prediction, about the same in batch
mode (perplexity). CPU single token is ~55 ms
(on Ryzen 7950X).
* Some improvement for Q3_K on CUDA
It is now ~22.5 ms/token on my GPU, so ~30% slower than Q4_0.
* Some more CUDA optimizations for Q3_K
Single token is now 20.5 ms/token (~20% slower than Q4_0).
Perplexity is on par with Q4_0.
* Adding Q4_K - scalar, AVX2, CUDA
Performance is the same or perhaps very slightly better than Q4_0 on the CPU.
On the GPU, single token prediction is ~10% better than Q4_0,
batch mode (perplexity is about the same).
* Adding Q6_K - scalar, AVX2, CUDA
Performance is ~40% lower compared to Q4_K on the CPU.
This is to be expected, considering that we are memory bound
on the CPU and the 6-bit model is ~44% larger than the 4-bit.
On the GPU, single token prediction is ~6% lower than Q4_0,
batch mode (perplexity) is even closer (but still slower).
* Adding Q5_K - scalar, AVX2, CUDA
Performance is ~20% lower compared to Q4_K on the CPU.
This is to be expected, considering that we are memory bound
on the CPU and the 5-bit model is ~22% larger than the 4-bit.
On the GPU, single token prediction is about the same as Q4_0
for both, single token and batch prediction.
* Per convention, all QX_K quantizations use Q5_K for output.weight
* Adding quantization mixes
* Quantization mixes: didn't quite get what I wanted in the last commit
* Q4_K dot product for ARM_NEON
* Q6_K dot product for ARM_NEON
* Q5_K dot product for ARM_NEON
* Adding Q3_K dot for ARM_NEON
It is 22% slower than Q4_K, despite the smaller model size.
On x86_64, where we are memory bound, the Q3_K model is
quite a bit faster than Q4_K.
* A very slightly faster ARM_NEON Q3_K dot
* Adding Q2_K - just CUDA for now
Token prediction is pretty good - about 15.5 ms on a RTX 4080.
Perplexity is about the same as Q4_K.
* Adding scalar and AVX2 Q2_K dot
* Adding ARM_NEON Q2_K dot
About the same performance as Q4_K.
* A slightly faster ARM_NEON Q2_K dot
Single token prediction is now ~36 ms on M2 Max.
The code is much simpler too.
* Fixed bug in Q2_K CUDA dot product kernel
Stranegly enough, for the few prompts I tried with the 7B model
the responses looked perfectly reasonable. Only realized something
is not quite right when I tried the larger models and started getting
nonse back.
In any case, Q2_K single token evaluation time on an RTX 4080 in a Ryzen7950X
box iusing CUDA and model fully loaded on the GPU are
~15.5 ms for 7B, ~25.4 ms for 13B, and ~55.8 ms for 30B.
The max number of layers that fit in VRAM for The 65B is 32.
With that, we get ~330 ms per token, which is not that much faster
than just running on the CPU (~470 ms per token).
* Don't print zeros/NaNs when no count histogram has been collected
* A 10% faster CUDA vector dot kernel for Q3_K
Q3_K is now running at ~18.5 ms / token on CUDA,
so the gap to Q4_0 is only 10%.
It seems memory acccess pattern is more important for
performance than the amount of computation the kernel
does.
* A slightly daster Q4_K AVX2 dot product
For perplexity, where we are less memory bound, time per
pass drops by ~5%. Barely measurable difference for single
token prediction.
* A slightly faster ARM_NEON A4_K dot product
* Minor
* Fix quantization error test
We cannot possibly be expecting rmse < 0.002 for 2- and 3-bit
quantization variants.
* Fix docker build
I have been sloppy with vector reinterpret casts on ARM_NEON.
It seems clang is very forgiving in that regard.
* Added forgotten ggml.o dependence on k_quants.h to the Makefile
* Had unintentionally committed the Makefile with -Ofast enabled
* ggml : rename k_quants -> ggml-quants-k, use lowercase in code
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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The 128 MB was too optimistic.
Too bad it is not dynamically computed.
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* mtl : export the LLaMA computation graph
* ci : disable temporary
* mtl : adapt the MNIST example as starter
* mtl : no need for mtl-export tool, add cli arg for main instead
* mtl : export just a small part of the graph for now to make it easier
* mtl : move MSL code into separate file for easy editing
* mtl : initial get_rows_q4_0 kernel
* mtl : confirmed get_rows_q4_0 is working correctly
* mtl : add rms_norm kernel + confirm working
* mtl : add mul kernel + confirm working
* mtl : initial mul_mat Q4 kernel (wrong results)
* mtl : mul_mat fixes (still wrong)
* mtl : another mul_mat Q4 (still does not work)
* mtl : working mul_mat q4
* ggml : fix handling of "view" ops in ggml_graph_import()
* mtl : add rope kernel
* mtl : add reshape and transpose handling
* ggml : store offset as opt arg for ggml_view_xd() operators
* mtl : add cpy kernel + handle view ops
* mtl : confirm f16 x f32 attention mul mat
* mtl : add scale kernel
* mtl : add diag_mask_inf kernel
* mtl : fix soft_max kernel
* ggml : update ggml_nbytes() to handle non-contiguous tensors
* mtl : verify V tensor contents
* mtl : add f32 -> f32 cpy kernel
* mtl : add silu kernel
* mtl : add non-broadcast mul kernel
* mtl : full GPU inference of the computation graph
* mtl : optimize rms_norm and soft_max kernels
* mtl : add f16 mat x f32 vec multiplication kernel
* mtl : fix bug in f16 x f32 mul mat + speed-up computation
* mtl : faster mul_mat_q4_0_f32 kernel
* mtl : fix kernel signature + roll inner loop
* mtl : more threads for rms_norm + better timing
* mtl : remove printfs from inner loop
* mtl : simplify implementation
* mtl : add save/load vocab to ggml file
* mtl : plug Metal inference into llama.cpp (very quick-n-dirty)
* mtl : make it work with main example
Lots of hacks but at least now it generates text
* mtl : preparing for merge
* mtl : clean-up ggml mtl interface + suport scratch / inplace
* mtl : remove temp / debug code
* metal : final refactoring and simplification
* Revert "ci : disable temporary"
This reverts commit 98c267fc77fe811082f672538fc91bcfc9072d63.
* metal : add comments
* metal : clean-up stuff, fix typos
* readme : add Metal instructions
* readme : add example for main
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* Use events instead of clFinish, where possible
* OpenCL: Don't load gpu layers into RAM, add mul_f32 kernel
* Reduce queueing overhead for contiguous tensors by using single mul kernel call
* Adapt to #1612 cl_mem malloc changes
* Reduce code duplication between cuda and opencl branches
* Improve implementation
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This adds support to llama.cpp to load the model.
Currently missing are changes that are required from convert.py to convert the model correctly. It needs some changes to start reading the JSON configuration for HF models instead of deriving the values by guessing.
Co-authored-by: FNsi <125447286+FNsi@users.noreply.github.com>
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* Move back to C++ for OpenCL
* Refactor OpenCL code to work more like the CUDA code, add missing functions
* Deduplicate dequant kernels
* Add OpenCL compile options
* Use compile args for preprocessing constants
* Restore default platform + device selection by id behavior
---------
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
Co-authored-by: Henri Vasserman <henv@hot.ee>
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The underlying representation of multibyte character literals is
implementation-defined. This could, at least in principle, cause
cross-build data export/import issues independent of endianness.
Define magic numbers as integer literals to be on the safe side.
Signed-off-by: Juuso Alasuutari <juuso.alasuutari@gmail.com>
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broadcasting for ggml_mul (#1483)
* Broadcasting for ggml_mul
* CUDA kernel for ggml_mul, norms in VRAM
* GPU weights not in RAM, direct loading with cuFile
* fixup! GPU weights not in RAM, direct loading with cuFile
* fixup! GPU weights not in RAM, direct loading with cuFile
* define default model path once, sync path with readme (#1366)
* ~7% faster Q5_1 AVX2 code (#1477)
* convert.py: Support models which are stored in a single pytorch_model.bin (#1469)
* Support models in a single pytorch_model.bin
* Remove spurious line with typo
* benchmark-matmul: Print the average of the test results (#1490)
* Remove unused n_parts parameter (#1509)
* Fixes #1511 lambda issue for w64devkit (mingw) (#1513)
* Fix for w64devkit and mingw
* make kv_f16 the default for api users (#1517)
* minor : fix compile warnings
* readme : adds WizardLM to the list of supported models (#1485)
* main : make reverse prompt option act as a stop token in non-interactive mode (#1032)
* Make reverse prompt option act as a stop token in non-interactive scenarios
* Making requested review changes
* Update gpt_params_parse and fix a merge error
* Revert "Update gpt_params_parse and fix a merge error"
This reverts commit 2bb2ff1748513591ad45b175a75ed1d8089d84c8.
* Update gpt_params_parse and fix a merge error take 2
* examples : add persistent chat (#1495)
* examples : add persistent chat
* examples : fix whitespace
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* tests : add missing header
* ggml : use F16 instead of F32 in Q4_0, Q4_1, Q8_0 (#1508)
* ggml : use F16 instead of F32 in Q4_0, Q4_1 and Q8_0
* llama : bump LLAMA_FILE_VERSION to 3
* cuda : update Q4 and Q8 dequantize kernels
* ggml : fix AVX dot products
* readme : update performance table + hot topics
* ggml : fix scalar implementation of Q4_1 dot
* llama : fix compile warnings in llama_set_state_data()
* llama : fix name shadowing and C4146 (#1526)
* Fix name shadowing and C4146
* Fix if macros not using defined when required
* Update llama-util.h
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
* Update llama-util.h
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
* Code style
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Fix for mingw (#1462)
* llama : add llama_init_backend() API (close #1527)
* feature : add blis and other BLAS implementation support (#1502)
* feature: add blis support
* feature: allow all BLA_VENDOR to be assigned in cmake arguments. align with whisper.cpp pr 927
* fix: version detection for BLA_SIZEOF_INTEGER, recover min version of cmake
* Fix typo in INTEGER
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Revert "feature : add blis and other BLAS implementation support (#1502)"
This reverts commit 07e9ace0f9da424d82e75df969642522880feb92.
* GPU weights not in RAM, direct loading with cuFile
* llama : code style fixes + progress print fix
* ggml : ggml_mul better broadcast support
* cmake : workarounds for cufile when CMake version < 3.25
* gg rebase fixup
* Loop in llama.cpp, fixed progress callback
* Attempt clang-tidy fix
* llama : fix vram size computation
* Add forgotten fclose()
---------
Co-authored-by: András Salamon <ott2@users.noreply.github.com>
Co-authored-by: Ilya Kurdyukov <59548320+ilyakurdyukov@users.noreply.github.com>
Co-authored-by: Tom Jobbins <784313+TheBloke@users.noreply.github.com>
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Co-authored-by: DannyDaemonic <DannyDaemonic@gmail.com>
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Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: David Kennedy <dakennedyd@gmail.com>
Co-authored-by: Jason McCartney <jmac@theroot.org>
Co-authored-by: Evan Jones <evan.q.jones@gmail.com>
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Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: Zenix <zenixls2@gmail.com>
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* Fix name shadowing and C4146
* Fix if macros not using defined when required
* Update llama-util.h
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
* Update llama-util.h
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
* Code style
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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* ggml : use F16 instead of F32 in Q4_0, Q4_1 and Q8_0
* llama : bump LLAMA_FILE_VERSION to 3
* cuda : update Q4 and Q8 dequantize kernels
* ggml : fix AVX dot products
* readme : update performance table + hot topics
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* CUDA kernel for q4_0 dequant. + mat. vec. mult.
* Added q4_1 via template
* Added missing __syncthreads();
* --gpu_layers -> --gpu-layers
* Shorter dequantize_mul_mat_vec line
* q5_0 dequantize_mul_mat kernel
* More readable dequantize_mul_mat_vec logic
* dequantize_mul_mat_vec kernels for q5_1, q8_0, f16
* llama : offload "output" tensor to GPU too + coding style fixes
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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example (#1360)
* implement 8 of 14 missing backward pass operations used by llama
- GGML_OP_ADD_AT
- GGML_OP_CPY
- GGML_OP_MUL_MAT (src0.grad)
- GGML_OP_PERMUTE
- GGML_OP_RESHAPE
- GGML_OP_SCALE
- GGML_OP_TRANSPOSE
- GGML_OP_VIEW
implement additional ggml operation GGML_OP_ADD_AT, which is necessary for backward pass of GGML_OP_VIEW.
this operation adds src1 to src0 with data offset, i.e. to view(src0, ..., offset).
the values are return in a tensor size of src0. values outside of [data+offset:data+offset+nbytes(src1)] are just the original values from src0.
still missing backward passes for llama:
- GGML_OP_DIAG_MASK_INF
- GGML_OP_GET_ROWS
- GGML_OP_RMS_NORM
- GGML_OP_ROPE
- GGML_OP_SILU
- GGML_OP_SOFT_MAX
* implement 5 of 6 missing backward pass operations used by llama
- GGML_OP_DIAG_MASK_INF
- GGML_OP_GET_ROWS
- GGML_OP_RMS_NORM
- GGML_OP_SILU
- GGML_OP_SOFT_MAX
add necessary ggml operations GGML_OP_ADD1, GGML_OP_SILU_BACK, GGML_OP_RMS_NORM_BACK, GGML_OP_DIAG_MASK_ZERO, and GGML_OP_ROPE_BACK
GGML_OP_ADD1 is necessary to add a scalar value in the backward pass of GGML_OP_SOFT_MAX
GGML_OP_ADD1 could also be replaced by using GGML_OP_ADD and GGML_OP_REPEAT, but the performance would be worse. additionally GGML_OP_REPEAT will return unexpected value when the the input to GGML_OP_SOFT_MAX contains only a single scalar. in this case GGML_OP_REPEAT will not return the value that should be repeated (src1) but the value which shape the result should take (src0). So in this case it can not replace GGML_OP_ADD1.
GGML_OP_SILU_BACK, GGML_OP_RMS_NORM_BACK and GGML_OP_ROPE_BACK are necessary for backward pass of GGML_OP_SILU, GGML_OP_RMS_NORM and GGML_OP_ROPE. The backward pass for these functions cannot be easily composed of existing operations. Since the backward pass builds a computation graph we need operations forward pass implementations of the the required backward passes. Sounds a bit confusing at first, I know...
GGML_OP_DIAG_MASK_ZERO is necessary for backward pass of GGML_OP_DIAG_MASK_INF.
Some operations where previously inplace-only. for backward pass there needs to be non-inplace variants.
staying consistent with other operations that have non-inplace and inplace variants, the operations are changed to non-inplace and
functions with "_inplace" are added which are inplace.
in llama we need to call the inplace variants so that it is implemented as before.
for llama backward pass we need to use the non-inplace variants.
still not completely implemented backward passes for llama:
- GGML_OP_ROPE: needs forward pass for GGML_OP_ROPE_BACK
- GGML_OP_GET_ROWS: only necessary for tokenizer
* norm & rms_norm can not be threaded:
after investigation rms norm for quite some time I come to the conclusion that neither norm, nor rms_norm can be threaded, because we need mean over all items, not just of the slices each thread sees.
* remove already resolved TODO
* implement backward pass of ggml_rope and ggml_rope_back
* implement backward pass for ggml_get_rows and for new operation ggml_get_rows_back
* add test-grad0.c
* use GGML_PRINT_DEBUG for debug messages which will otherwise flood the console
* test both gradients of mul_mat
* disable graph dot export as it floods console
* bug fixes for silu_back
* successfully test silu backward
* bug fix for scale backward pass
use sum instead of mean for gradient of scalar scale parameter
* successfully test scale backward
* improve performance of sum backward pass
use add1(x,y) instead of add(x,repeat(y,x))
* improve performance of sqr backward pass
use scale(x,y) instead of mul(x,repeat(y,x))
* successfully test rope backward
* bug fix for cpy backward pass
* successfully test cpy backward
* bug fix for reshape backward pass
* successfully test reshape backward
* add test-opt.c
this uses ggml_opt to train a,b for minimal e=sum(sqr(c - a*b)) for random initial a,b,c
* correctly implement softmax backward pass using new operation ggml_diag
ggml_diag constructs diagonal matrices with entries.
ggml_diag(shape[a,1,c,d]) -> shape[a,a,c,d]
* successfully test soft_max backward
* align shape annotations
* add shape annotations for llama
* de-duplicate ggml_forward_dup code taking care of contiguous tensors of same type.
with this we can duplicate tensor of any typ as long as they are contiguous.
* fix ggml_compute_forward_dup_same_cont for when nelements < nthreads
when more threads are used than elements exist ie1 was less than ie0, resulting in invalid negative byte count argument in memcpy
* bug fix for add_at forward
required for view backward pass
src0 values must be copied to dst, because during addition we don't touch all dst elements in contrast to the normal add function.
* successfully test view backward
* minor code format improvement
* fix ggml_forward_add functions to work correctly with transposed tensors
uses the same logic as in ggml_compute_forward_add_q_f32, but make it consistent across all ggml_compute_forward_add_... functions.
this also slightly changes the mem access pattern of the different threads to works as in ggml_compute_forward_add_q_f32.
* fix ggml_forward_add1 functions to work correctly with transposed tensors
uses the same logic as in ggml_compute_forward_add1_q_f32, but make it consistent across all ggml_compute_forward_add1_... functions.
this also slightly changes the mem access pattern of the different threads to works as in ggml_compute_forward_add1_q_f32.
* test-grad0.c : add print_elements to help with debugging
* successfully test permute backward
* some minor test-grad0 fixes
* fix sub, mul and div functions to work correctly with transposed tensors
uses the same logic as in add
* implement ggml_cont backward pass
* successfully test transpose backward and permute for all permutations
also test sub, mul and div up to max n_dims
* test-grad0.c add TODO for view_2d and view_3d
add_at (required for view backward pass) is a bit tricky for n_dims > 1.
* fix comments
* successfully test diag_mask_inf and diag_mask_zero backward
* test-grad0 : fix test for div
nargs and ndims was swapped, corrupting the stack
* fix diag_mask to work with non-inplace input
* move dup call into the actual add_at functions
* fix get rows backward pass
* successfully test get_rows backward
* fix view backward pass
add nb parameters to add_at like in view.
together with offset they define how to view dst and src0 during the add_at operation.
* successfully test backward pass of view_1d, view_2d and view_3d
* fix backward pass for rms_norm
I would have used formulas from other frameworks, but they differed so I could not decide which is correct.
Instead it was derived here in comment using manual forward-backward automatic differention of rms_norm and simplification.
* successfully test backward pass of rms_norm
some tests may fail when gradients are large.
could not find a satisfying configuration to check for abs error and relative error that passes all tests while still actually testing the results with tight enough error bounds.
when looking at the values the "failed" tests look actually ok. for example:
rms_norm: ndims=2, i=0, k=2, x0=0.000153, xm=0.000053, xp=0.000253, f0=0.278594, f1=0.086213, g0=961.905457, g1=966.064941, eps=0.000100, error_abs=4.159485, error_rel=0.004324
it is due to the test logic in check_gradients that they fail.
* add todos for llama backward pass
- implementation for ADD1 backward pass should probably use sum instead of mean (but this backward pass is not required)
- repeat is not yet tested and looks like it only works for single element src0 inputs.
* add operation ggml_sum_rows
ggml_sum_rows(shape[a,b,c,d]) -> shape[1,b,c,d]
* add missing GGML_OP_SUM_ROWS
* fix backward pass for repeat
requires ggml_sum_rows
* successfully test backward pass of repeat
* update quantization types in switch-case of add_at and add1
* add baby-llama example training a very small llama model from scratch to output a sinusoidal wave.
had to increase maximum number of optimization parameters to train from scratch.
* fix softmax in baby-llama example
* switching from training with adam to lbfgs produces much better results in the baby-llama example
* train with two examples, creating new tensors each time..
* fix bug when using ggml_opt to optimize params in one context and use a renewable context for eval and opt
when not keeping gradients of model parameters they are overwritten by tensors created by opt, which may be invalid after opt context is renewed.
so we need to keep the original gradients and make dups for opt
* train on multiple examples, generate & print tokens with trained model afterwards
ctx0 for evaluation and optimization is renewed for each sample
* add ggml_reshape_1d, ggml_reshape_4d and ggml_view_4d
* fix soft_max backward pass for input->ne[1] != 1
* add ggml_log operation necessary for cross entropy loss
* add test for ggml_log gradients
* implement backward pass for ggml_sum_rows, necessary for cross entropy loss
* implement ggml_repeat support for rank > 2 tensors
* add test for ggml_sum_rows gradients
* fix training get_example_targets
predict the next token, not the current token!
* add square_error_loss and cross_entropy_loss functions
* optimize loss over multiple samples
this increases computation graph, need parallel batched forward for more efficiency.
* fix backward pass for add_at and change arguments to have same order as in view
* add ggml_set(ctx, a, b) to set b in view of a and return modified a
necessary to set values into kv_self cache and properly propagate the gradients
* fix kv_self gradients for training
use ggml_set instead of ggml_cpy to set kv_self cache with properly propagating gradients
* replace inplace operations for training with copying operations to allow gradient propagation
* add GGML_ASSERT to catch ggml_rope and back value errors
* add trainable lora-only model with all big matrices C split into A,B with A*B=C
this is not a lora-finetune, but the whole model changed to have only low-rank "lora" matrices.
training this instead of the normal model resulted in much worse results though...
* vastly improve training results
instead of logit targets 0 and 1 use -1 and +1.
* shorten code using a variable
* change name of GGML_OP_ADD_AT to GGML_OP_ACC
* smaller default values for baby llama model parameters
* update static assert of GGML_OP_COUNT
* remove shape annotations in llama_eval_internal
* revert disabling of threading for rms_norm and norm
* rename print functions in baby-llama example
* fix call to ggml_set_name
* add missing include for strcmp, etc
* remove trailing whitespace
* reduce number of test-grad0 iterations
avoid exceeding timeout of automated tests
* remove busy loop that was used as sleep for slower sinus wave generation
* disable slow tests grad0 and opt to avoid exceeding timeouts
* c++ in baby-llama example
use c++ includes instead of c includes
use std::min, std::max instead of MIN, MAX macros
* c++ in baby-llama example
use c++ includes instead of c includes
use std::min, std::max instead of MIN, MAX macros
* ggml : fix compiler warnings + cosmetic changes
* ggml : fix nullptr derefs in GGML_OP_CONT and GGML_OP_RESHAPE back
* swap arguments to vDSP_vdiv call
documentation for vDSP_vdiv states: "Note that B comes before A!"
* swap arguments to vDSP_vdiv call
documentation for vDSP_vdiv states: "Note that B comes before A!"
* ggml : swap vDSP_vsub args as per documentation
* add parallel batched forward function for baby-llama training
* cleanup code for batched training
* remove trailing whitespace
* minor : fix compiler warnings + indentation style
* ggml : fix null ptr deref in backward pass
* ggml : remove Q4_2 remnants
* ggml : fix clang-tidy warnings
* baby-llama : couple of clang-tidy warnings
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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* ggml : remove Q4_0 bit shufling (ARM NEON)
* ggml : remove Q4_1 bit shuffling (ARM NEON + reference)
* ggml : nibbles_from_floats() + bytes_from_nibbles() (ARM NEON)
* ggml : remove Q4_2 bit shuffling (WIP, BROKEN)
* ggml : remove Q5_0 bit shuffling (ARM NEON)
* ggml : 2x faster scalar implementations
* ggml : remove Q5_1 bit shuffling (ARM NEON + scalar)
* ggml : simplify scalar dot
* ggml : remove WASM SIMD bit shuffling + remove vzip for ARM 32-bit
* ggml : fix Q4_1 quantization
* ggml : update cuBLAS + normalize variable names
* ggml : remove Q4_2 mode
* ggml : minor formatting
* ggml : fix Q5_0 quantization
* scripts : add script for measuring the time per token
* AVX implementations (#1370)
* ggml : uniform 5th bit extraction
* llama : produce error upon loading old model files
* llama : fix model magic/version write
* ggml : speed-up Q5_0 + Q5_1 at 4 threads
* ggml : preserve old Q4 and Q5 formats
* ggml : simplify Q8_1 - no need for low / high sums anymore
* ggml : fix Q8_0 and Q8_1 rounding
* Revert "AVX implementations (#1370)"
This reverts commit 948d124837f9d287d8490f41338e0e4cceb0814f.
* ggml : fix AVX2 implementation
* sha : update hashes for 7B and 13B
* readme : update timings + remove warning banner
* llama : update v2 PR number to 1405
* ggml : fix WASM comments
* ggml : back to original bit order
* readme : add note that Q4 and Q5 have been changed
* llama : fix return for unknown version
---------
Co-authored-by: Stephan Walter <stephan@walter.name>
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fixes #1363
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* llama : require first token to be BOS
* scripts : add ppl-run-all.sh
* perplexity : add BOS for each chunk
* readme : update perplexity values after BOS fix
* perplexity : add clarifying comments
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* llama : only copy used KV cache in get / set state
* switch to ggml for copying k, v
* avoid designated initializers
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* ggml: add names to tensors
* minor improvements to dot file formatting
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* cuBLAS: fall back to pageable memory if pinned alloc fails
* cuBLAS: do not use pinned memory if env variable GGML_CUDA_NO_PINNED is set
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* llama : minor - remove explicity int64_t cast
* ggml : reduce memory buffer for F16 mul_mat when not using cuBLAS
* ggml : add asserts to guard for incorrect wsize
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