Commit Graph

470 Commits

Author SHA1 Message Date
Cebtenzzre
bc39553c90
build : enable more non-default compiler warnings (#3200) 2023-09-28 17:41:44 -04:00
slaren
16bc66d947
llama.cpp : split llama_context_params into model and context params (#3301)
* llama.cpp : split llama_context_params into model and context params

ggml-ci

* fix metal build

* fix freq_base/scale default to model value

* llama-bench : keep the same model between tests when possible

* move n_threads to llama_context_params, add n_threads_batch

* fix mpi build

* remove kv_size(), cuda scratch fixes

* remove low-vram option

* add n_threads_batch to system info, refactor to get_system_info()

* add documentation about --threads-batch to the READMEs

* llama-bench fix

* main : fix rope freq/scale warning

* llama.cpp : add llama_get_model
common : add llama_tokenize from model

* remove duplicated ctx/model functions

ggml-ci

* cuda : print total VRAM used
2023-09-28 22:42:38 +03:00
xaedes
0e76a8992c
train : finetune LORA (#2632)
* fix track_max_mem in forward_batch_wo_cache_flash_attn_train

* remove unnecessary Adam(W) optimizer tensors.

reduces optimizer memory overhead from 7*modelsize to 2*modelsize.

additionally allows to optimize models with more than 2^31 parameters by replacing int with int64_t.

bumps training checkpoint file version, but old checkpoints can still be read.
new version with less tensors is saved.

* add gradient clipping to AdamW

* Fix reset of unused g->nodes and g->grads to NULL

* implement gradient checkpointing for training

reduces memory overhead from O(n_layer) to O(sqrt(n_layer))

as explained in readme of https://github.com/cybertronai/gradient-checkpointing

* remove unused compute buffer 3

* add and use function ggml_build_backward_expand to avoid stack overflows with large maximum number of nodes

GGML_API void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool keep);

* change AdamW decay parameter to work like the torch AdamW decay parameter

It is now relative to Adam learning rate `alpha*sched`.
Before that it was relative to `sched` only.

`alpha` being the maximum learning rate and `sched` being a scaling parameter in [0..1]

* change default AdamW weight decay parameter used in training to 0.1 as used in nanoGPT

* change default AdamW weight decay parameter defined in ggml to 0.0, making Adam default instead of AdamW

btw: the default weight decay parameter for torch.optim.AdamW is 0.01

* bug fixes for cross entropy loss

ggml_cross_entropy_loss: sums where not correctly added in workload of each thread
ggml_cross_entropy_loss_back: simplify backward process, reducing numerical issues

guard usage of exp f16 lookup in cross entropy by #define GGML_CROSS_ENTROPY_EXP_FP16

cross entropy loss is only used once during training, but it is quite sensitive to numerical errors introduced by exp-f16-lookup.
so exp-f16-lookup for cross entropy loss is disabled by default, trading better gradients for very slightly worse runtime performance.

* fix test-grad0 for cross_entropy_loss

the second argument to cross_entropy_loss must sum up to 1 for each row

* fix test-grad0 for soft_max

dont use only sum as aggregation, because sum of softmax is always 1 -> finite differences should not work
instead use sum(log(soft_max()*(1-eps)+eps)); use eps to avoid log(0)

* improve finite differences of test-grad0 by using double instead of float

* change cross_entropy_loss to output average over all rows

this helps keeping the loss and gradients in a sane range

* improve gradient checkpointing

sqrt(n_layers) is only the best checkpoint step when mem size of checkpoints and mem size of layers are equal.
since layers require more memory than the single-tensor-checkpoint we use, the optimal values are compute different:

```
  given: n, u, v
  objective: minimize(a*u+b*v) where a*b=n, a>0, b>0
  b=n/a
  minimize(a*u+v*n/a)
  diff(a*u+v*n/a, a) = u - (v*n/a)/a
  diff(a*u+v*n/a, a) == 0
  u - (v*n/a)/a == 0
  u == v*n/(a*a)
  u*a*a = v*n
  a*a = v*n/u
  a = sqrt(n*v/u)
```

this change results in more checkpoints, requiring less layers to store between checkpoints, overall improving memory usage.

* disable gradient checkpointing debug output

* llama : fix rope usage in train-text-from-scratch after ChatGLM change

* add more training parameters:

--enable-restart N         Only for Adam optimizer. Enable restarts of cos-decay
--disable-restart N        Only for Adam optimizer. Disable restarts of cos-decay
--opt-past N               Number of optimization iterations to track for delta convergence test. Disabled when zero.
--opt-delta N              Maximum delta for delta convergence test. Disabled when <= zero.
--opt-max-no-improvement N Maximum number of optimization iterations with no improvement. Disabled when <= zero.
--adam-epsf N              AdamW epsilon for convergence test. Disabled when <= zero.
--adam-min-alpha N         Adam minimum learning rate alpha, usually 0.1 * alpha

* replace memcpy with reshape operation so that the graph is not cut at the input

this makes it possible to store other values into the input tensor and then simply recompute the graph without rebuilding it

* remove unused function argument from get_example_targets_batch

* measure and print total training time

* add optimization callback to ggml_opt_resume_g

this callback is called before each iteration with custom data and pointer to learning schedule parameter (only used in Adam(W)).

can be used for dynamic learning schedule and setting input data for batches before each iteration

* use optimization callback in training

allows dynamic learning schedule and different batch data for each iteration without relying on low n_iter and high n_examples parameters

reduces runtime by avoiding restart of optimization function and improves training convergence by providing a different batch for each iteration

* add minimum number of tensor dimensions to apply weight decay (default 2)

this allows to not apply weight decay to bias parameters

* rename training parameter cos-decay-alpha to cos-decay-min and clarify that adam-min-alpha also applies to warmup

* fix increase of model.train_samples and model.train_tokens

now that each optimizer iteration gets its own batch we need to multiply by number of opt iterations

* change sampling parameters for prediction after training to defaults of common.h

and clarify what is context for prediction and what are generated tokens

* tighten abs error bounds for cross_entropy_loss in test-grad0

* add conditional compilation of using F16 exp in flash attention

uncomment `// #define GGML_FLASH_ATTN_EXP_FP16` to enable usage of f16 exp in flash attention

* tighten abs error bounds for flash_attn in test-grad0

* tighten abs error bounds for sqrt in test-grad0

* remove out-commented vectorized code of opt_adam

the vectorized code might be bit faster for low number of parameters, but it had a big memory usage overhead

* ggml : update ggml_rms_norm_back with configurable eps

* llama training : fix ggml_rms_norm_back calls to pass configurable eps

* remove trailing whitespace

* add train function using automatic gradient checkpointing backward pass and allocator

* in train function replace add_inplace by regular add

because using add_inplace seems to result in different gradients

* don't use allocate hash_map on context

because the context has no_alloc=True when using memory allocator resulting in NULL data pointers

* correctly clone reshape and permute operations by also cloning tensor->nb values

* fix variable name and add missing type cast

* terminate recursive tensor cloning when reaching tensor without src tensors

* correctly clone view tensors by setting data pointers

without this the checkpointing would only work when being used together with memory allocator

* fix variable names

* swap arguments to commutative ops to be the same as in `forward_batch_wo_cache_flash_attn`

* add input tensors as checkpoints

so that recursive tensor cloning of gradient checkpointing terminates on input tensors

* fix variable name and add missing boolean negation

* make sure some tensors are not reallocated by inserting new temporary nodes depending on them:

output and parameter gradient tensors need to be available at the end of the graph execution

parameter gradient tensors also need to be available before the graph execution because they are set to zero before each optimizer iteration

checkpoint tensors are allocated all together to reduce memory allocator fragmentation

afterwards, in addition to the temporary nodes, we also need to reset the temporary leafs

* fix ASSERT to work with zero layers

* add training options whether to use allocator and/or unified training function

* integrate unified training function which may use memory allocator

the unified training function also supports arguments whether to use flash attention and/or gradient checkpointing

* format name of cloned tensors with " (clone)" suffix

* set names for tensors in unified train function for easier debugging

* allocate graph on context using ggml_new_graph

* remove handwritten training functions

* remove unused training parameters "use_scratch" and "use_unified"

* remove trailing whitespace

* remove unused train params: mem_compute1_gb & mem_compute2_gb

mem_compute_gb is used for compute when automatic memory allocator is not enabled, otherwise it can be very small to only hold the tensor definitions
mem_compute0_gb is used for automatic memory allocator (as long as measurement of max required size is not implemented)

* remove unused forward_batch function

* add debug asserts in ggml_allocr_alloc to some common pitfalls when using this function directly

* only use ggml_allocr_alloc when tensor has NULL data and is no view

* fix test when to create temporary backward graph

temporary backward graph is only necessary when using checkpointing

* fix memory "leak" in optimizers

each iteration a new cplan with new memory for work data was allocated.
now cplan creation only happens at the start of optimization, with each iteration reusing the cplan and its work data.

* reverse order of for loop in ggml_build_backward_expand to save memory when using gradient checkpointing and allocator

with this loop order gradient checkpointing with allocator on 16 layer model saves 13% memory; 2 layer memory it saves 2% memory.

the computation results are the same

* add API functions to access llama model tensors

* add stub example for finetuning, based on train-text-from-scratch

* move and remove code

* add API functions to access remaining model parameters:

mult, head and rot

* first draft for LORA finetune training

* remove const model and layer arguments in API functions for accessing model tensors

* bug fixes to make finetune compile

automatic allocator does not work yet

* add debug prints for training memory improvements

* fix names of lora tensors

* avoid stack overflow resulting from big ggml_cgraph

replace stack allocation and ggml_build_forward by ggml_new_graph in combination with ggml_build_forward_expand

* replace llama API functions to get model tensors by one function to get model tensor by name

LLAMA_API struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name);

* remove unused call to not existing llama_get_layer_from_model

* implement ggml_compute_forward_out_prod_q_f32

* remove trailing whitespace

* add lora finetune support on quantized base model tensors

* add ggml_add_cast API function

this function works like ggml_add, but accepts a data type for the resulting tensor.
only supported for quantized src0 input.

* use ggml_add_cast in finetuning

lora-applied weights will now have data type F32, which improves gradients when finetuning quantized base models

* bug fix: actually use result type passed to ggml_add_cast

* make sure base model tensors data cannot be used in viewable operations

memory allocator would try to make lora application inplace on base model tensors.
since those are memory mapped this will result in memory access violations

* fix bug in ggml_out_prod which resulted in wrong n_dims of result tensors

* avoid keeping in memory ALL of the gradients

The problem here stems from ggml_graph_reset. This function is called in the optimization function, before each graph computation, to reset the gradients to zero. This required a unique memory slot for each gradient: allocating memory from a previosly freed memory location might lead to non-zero input gradients.

During ggml_compute_backward the gradients are build stepwise by adding or substracting new values, starting from a OP_NONE tensor which needs to contain zero-values. This requires the graph reset.

To avoid this I now remember in ggml_build_backward_expand the original OP_NONE gradient tensors in a hash table, which is passed to ggml_compute_backward. There instead of using add (or sub or similar) I test whether the existing gradient to be changed is a zero-valued-tensor by looking up its existence in the hash table. When it is such a zero-tensor it will not be modified, but replaced by the value to be added, otherwise the regular add (not inplace, allocator will take care of this) will be used. This way none of those zero-tensor values will be necessary in the final backward graph and more importantly they won't need a unique memory slot, just to make them zero.

* remove trailing whitespace

* remove debug prints and function to compute tensor data hash

* improve optimization iteration prints

* adjust maximal values to support finetuning 3B models

* change default finetune params lora_r and lora_alpha to match the n_rank parameters of 4

* bug fix: make sure finetune input gradient is allocated at begin and kept until end

* remove unnecessary src tensor from ggml_get_rows_back

we don't need data of src[2] for computation, only to setup the correct output shape.
remove dependency on src[2], so that allocator can work more freely.

the computational graph is still completely determined, because the output shape is naturally included.
this is similar to how ggml_reshape does it.

* remove unnecessary src tensor from ggml_repeat & ggml_repeat_back

we don't need data of src[1] for computation, only to setup the correct output shape.
remove dependency on src[1], so that allocator can work more freely.

the computational graph is still completely determined, because the output shape is naturally included

* resolve todo

allocator will only make it inplace when they are of the same type

* mixing multiple LORA adapters is now possible

pass more than one '--lora FNAME' argument to apply more than one LORA.
use '--lora-scaled FNAME S' when you want to specify a user-defined scale for an adapter.

* add option to save finetune output every N iterations

* also save latest finetune output with ITERATION="LATEST" and print where files are saved

saving with LATEST makes it easier to resume training from the latest checkpoint
the string "LATEST" can be configured with command line option "--fn-latest STR"

* update checkpoint train stats before saving via "--save-every"

* add command line option `--rank-wo N` for rank of wo tensor

* update finetune README

* fix dump_non_result_info_yaml to output multiple lora adapters

* bug fix: replace GGML_TYPE_SIZE[t] by ggml_type_size(t)

* replace llama_n_mult by llama_n_ff

* finetune bug fixes to compile with merged in code from master

* remove prediction related code to reduce duplicated code with main

use main instead

* reduce large memory overhead in train-text-from-scratch

all gradients had to be pinned so that graph_reset works correctly.
this is no longer necessary with the changes to ggml_compute_backward introduced in this PR.

* add comment explaining why finetune checkpoints are allocated in one block

* make default value of float member a float literal

* handle rms_norm and rope parameters the same as in train-text-from-scratch

* remove unused code

* remove vocab related code as it is unnecessary

* add LLM_KV_TRAINING_TYPE to train-text-from-scratch checkpoints

so that they can be differentiated from lora finetune checkpoints

* add gguf constants and load/save functions from train-text-from-scratch

* add load & save lora finetune checkpoints via gguf

* add python script to convert old finetune checkpoint files to gguf

* remove old checkpoint save & load code

* remove code to print data checksums which was used to verify correctness of new gguf code

* omit tokenization when training is disabled, only save llama lora adapter

training can be disabled by passing '-n 0' to finetune

* remove trailing whitespace

* update README.md

* implement ggml_compute_forward_repeat_f16

* avoid stack overflow of large cgraphs in test-grad0

* add ggml API functions ggml_unravel_index, ggml_get_i32_nd and its analogs for set and for f32

ggml_get_i32_1d, ggml_set_i32_1d, ggml_get_f32_1d, ggml_set_f32_1d now support non-contiguous tensors.
in case of non-contiguous tensor, the 1d index is unraveled into a multi index using ggml_unravel_index to be passed to '_nd' function equivalent.

this fixes a bug in test-grad0 which happens due to ggml_build_backward not building purely contiguous tensors anymore

* increase test-grad0 context mem size to accommodate for bigger cgraph

* add sanity check to ggml_compute_backward, asserting the correct shape of gradients

* fix ggml_acc_or_set to return tensor of correct shape

* remove unused 'inplace' argument from ggml_compute_backward function

inplace operations to add gradients are no longer created by ggml_compute_backward
use allocator to automatically make inplace operations

* add missing argument 'int i0' to ggml_get_i32_nd & ggml_set_i32_nd header declarations

* fix error message in ggml_allocr_alloc to display actual max_avail

* fix check_gradient

ggml_build_backward_expand was previously replaced by ggml_build_backward, but the assignment of forward graph to backward graph missing

* use tensor->view_src instead of ggml_is_view and get_view_source

* move gradient checkpointing code into ggml, new API function:

// build gradient checkpointing backward graph gb for gf using provided checkpoints
// gb_tmp will contain original backward graph with rewritten backward process nodes,
// but without the second forward pass nodes.
GGML_API void ggml_build_backward_gradient_checkpointing(
        struct ggml_context   * ctx,
        struct ggml_cgraph    * gf,
        struct ggml_cgraph    * gb,
        struct ggml_cgraph    * gb_tmp,
        struct ggml_tensor  * * checkpoints,
        int                     n_checkpoints);

* replace custom data getters and setters by ggml functions

* train-text-from-scratch can train (full finetune) gguf models

just pass the gguf model via `--checkpoint-in FN`.
after this, to continue training, pass the generated checkpoint instead of the original gguf model.

tested with smaller models, bigger models may exceed available memory.
use (LORA) finetune for those.

* remove trailing whitespace

* add option to save train-text-from-scratch output every N iterations

* update README.md

* fix warnings

* fix warnings

* remove finetune option to disable allocator

the allocator should always be used.
by making sure that it is always used it gets easier to implement automatic memory requirements computation

* add tensor checkpoints only when gradient checkpointing is enabled

* initialize opt ggml context if none was provided

* add ggml-alloc API function 'ggml_allocr_max_size' to get max size of alloc

GGML_API size_t ggml_allocr_max_size(struct ggml_allocr * alloc);

* finetune: automatically allocate all memory and changes to command line options

remove '--n_examples N' parameter, as it no longer makes sense to call optimization process multiple times in a loop.
add '--only_write_lora' command line option: will skip tokenization and training, to only write a llama.cpp comptabile LORA adapter.
remove memory buffer related command line options.
improve iteration console output.

* add finetune to Makefile

* update README.md

* print time per iteration and estimate remaining time

* increase measured alloc size by tensor_alignment

ggml_allocr_reset will reduce the given size by up to tensor_alignment-1

* fix README.md

* add some more allocator debug prints

* bug fix, probably solves the 'ggml_allocr_alloc: not enough space in the buffer' issue

* revert last commit

"bug fix, probably solves the 'ggml_allocr_alloc: not enough space in the buffer' issue"

"alloc was freeing an externally allocated tensor, because it calculated the end of allocator memory as alloc->data + alloc->max_size instead of alloc->data + alloc->size."

This is intentional to reduce the risk of freeing external tensors when measuring. Unless max_size is not properly calculated, I don't see why this is an issue.

* remove unnecessary "0x" before "%p" output

* move measurement memory segment to upper region of the address space

* update README.md

* fix printf format warnings

* add missing gguf_free in load_checkpoint_lora_file

* load default rms_norm and rope parameters from base model

* add gradient accumulation

specify number accumulation steps with '--grad-acc N'.
this will simulate a bigger batch size of grad_acc*batch.

* fix tracking of train_samples and train_tokens

* build : fix compile warnings

* ggml : fix L-BFGS linesearch loop

* improve finetune time measurement

fix printf warnings on system where int64_t is (long int).
change time datatypes to double because values get big with long training times.
exclude file saving from time measurement.
converge faster to actual time per iteration by removing very small first duration before first iteration was performed.
fix bug in output of total training time, the reported value was 1000 times to small.

* specify default lora rank with '--lora-r N'

'--lora-r N' will specify default rank for all tensors
'--rank-wq N', etc. will override this default rank for specific tensor types.

* fix gradient accumulation bug where the same batch was used for each microstep

* fix gradient accumulation bug where the same batch was used for each microstep

* support grouped-query-attention in ggml_flash_attn and ggml_flash_attn_back

k and v can now be repeated in q along ne[2]

in forward pass just use modulo to compute k and v indices, like ik2 = iq2 % nek2.

in backard pass this won't work as easy, because multiple threads will compete to accumulate to the same k->grad[:,ik1,ik2,ik3] and v->grad[:,iv1,iv2,iv3].
so we change the parallelization over q rows to be over k rows. this ensures non-overlapping (ik2,ik3) across threads.
in each thread we then iterate over the number of repetitions of k/v in q to compute iq2 as iq2 = ik2 + irep*nek2.

since ne2 is not the same for q,k and v we also change how the gradients are concatenated into the result tensor.
additionally the offsets of gradq, gradk and gradv in the result tensor are now memory aligned.

we also simplify the compute_backward part of flash_attn to use ggml_reshape instead of switching over the number of dimensions.
this needs a small change to ggml_reshape, removing the assertion of second argument to be contiguous.
since only the shape (ne) of the second reshape argument is of relevance, its memory layout (nb) is irrelevant -> it can very well be non-contiguous.

change test-grad0 to also test for repeated k/v in q.

this changes the rng and now results in small gradient differences in softmax. these solely come from using f16 exp table lookup in forward softmax: when temporarily changing softmax to use actual exp function, the reported gradient differences go away. gradient differences coming solely from f16 table lookup are acceptable.
added a note to explain this.

* add llama API functions to get grouped-query-attention n_head parameter 'n_head_kv'.

* fix finetune to support grouped-query-attention (using flash-attention)

note: ggml changes to ggml_out_prod are necessary to support grouped-query-attention without flash-attention.

* support broadcastable a in out_prod(a, b) and backward pass of broadcasting mul_mat(a, b)

* test broadcasting mul_mat backward pass

* decouple random number generator of each operation test

when changing one test the rng of others tests is not influenced anymore

* add comment briefly describing what ggml_repeat_back does

* simplify broadcasting mul_mat backward using ggml_repeat_back

* add cgraph evaluation order member and corresponding enum type

this controls in which order ggml_build_forward visits source nodes.
by default the nodes are visited left to right, i.e. src[0] first.
in some cases it is beneficial for ggml-alloc to visit in a different order.
two possible orders are supported: left-to-right (src[0] first) and right-to-left (src[0] last).

* measure max compute size for each cgraph eval order and use best order

this can bring huge memory savings:
e.g. codellama-34b with n_ctx=64, n_batch=1 goes from 92927.8mb down to 4627.6 MB

* remove unused command line options

* add sample start patterns and options to force new or by default resume last shuffling

* update shuffle rng state on reshuffle

* exclude known zero values from computations in flash_attn_f32 & flash_attn_back_f32

* remove probably unnecessary exception type flags from stringstream

* pass correct max number of tokens to llama_tokenize

* account for possible leading whitespace that will be added by tokenizer
e.g. '\t' will be tokenized by llama spm tokenizer to [29871, 12]

* use unrolled vec_mad in out_prod

y is vec_mad result vec.
x is vec_mad input vec.
v is vec_mad input scalar.

ggml_vec_mad_f32_unroll will internally loop over x and v with same y.

GGML_VEC_MAD_UNROLL is by default defined to 32.

This value is empirical optimized using performance test runs of out-prod in openllama-3b finetune with 256 context length and batch size 1. It gives 23% performance boost for out_prod.

Full measurements of out-prod runtime in ms:
	unroll_xv	unroll_yv
1	67014.643	87826.469
2	77117.552	89077.656
4	72091.311	109121.657
8	61077.543	88678.334
16	56914.67	79514.947
24	59024.595	84350.254
28	55952.446	83368.73
32	51476.658	85177.745
36	55973.792	84659.92
40	55139.616	93844.738
48	60736.392	93330.267
64	99856.878	116994.99

Second column is when unrollying yv instead of xv

* set lora_alpha to value of lora_r if it is not set via command line

otherwise only changing lora_r will change scaling of lora adapter used in prediction

* reshuffle original sample order instead of the previous shuffled order

otherwise resumed reshuffle will not result in same sample order

* block tiling for out-prod inspired by mul-mat

block sizes are empirically optimized

roughly doubles the flops of out-prod

* exclude some more known zero values from computations in flash_attn_f32 & flash_attn_back_f32

* add static keywords

* remove outcommented old code

* update train-text-from-scratch with tokenization, sample selection and shuffling from finetune

* remove lbfgs related train parameters

* move common train functions into common/train.[h|cpp]

* move train state into struct train_state

* move train data saving code into callback to unify code of opt_callback

train_params are still different in finetune and train-text-from-scratch, so it can't yet be moved to train.h|cpp

* move common train params into common/train

* move common opt_callback into common/train

* fix consume_common_train_arg

* save and load head_count_kv in lora checkpoints

* increase train_samples by used_samples instead of number of batches

on batch can contain more than one sample when option "fill_with_next_samples" is used

* fix usage of llama_tokenize

* remove static from process_escape since we need it exposed in header

* fix code formating of long function declarations

* fix condition in load_train_state_gguf

* use die("msg") instead of replace GGML_ASSERT(!"msg") or throw std::runtime_error("msg")

* fix saving and loading of training type

* remove terminating '\0' from tokenization

(llama_tokenize is now passed the string length instead of relying on terminating '\0')

* fix compile warnings

* fix compile warnings

* use new/delete for train_state instead of malloc/free

using malloc may result in seg faults when trying to assign string fields

* assert that sample_count > 0, avoiding division by zero

* fix frand to return value in interval [0,1)

* add train option "--sample-random-offsets"

Use samples beginning at random offsets.
The offset is only applied to the first sample in each batch context window.
Together with "--fill-with-next-samples" this may help for training endless text generation.

For example given a dataset containing samples "abcd", "ABCD", "0123".
With context size of 8 and options "--fill-with-next-samples", "--no-separate-with-eos", "--no-separate-with-bos",
the context windows of batches could only be filled with "abcdABCD", "ABCDabcd", "0123abcd", etc.

With "--sample-random-offsets" it can also be filled with "23abcdAB", "bcd0123A", etc.

* deduplicate code into function

* remove n_rot hparam, as it must always be hparam.n_embd_head()

* align code

* assert correct base model tensor shapes

* move some params from lora hparams into model hparams and load model params from gguf

this equalizes the model definition in finetune and text-from-scratch and removes the need for additional llama api functions to get model parameters

* remove now unnecessary llama API functions to get model params that where added by this PR

* train-text-from-scratch: automatically allocate model tensors, remove option '--mem-model N'

* train-text-from-scratch: automatically allocate opt context

* train-text-from-scratch: automatically allocate input tensors

* train-text-from-scratch: automatically allocate compute memory

* remove unused options and equalize train-text-from-scratch with finetune

* initialize opt->loss_after with zero

* add export-lora program

* remove trailing whitespace

* add export-lora build in Makefile

* remove unused struct tensor_info from export-lora

* add export-lora build dependency to llama

because it depends on common, which depends on llama

* update finetune README.md

* cancel optimization when specified number of epochs is completed

* improve handling of export-lora arguments

print errors and warnings when files could not be read or created

* Fix export-lora.cpp "not enough space in the context's memory pool" (#1)

* Fix export-lora.cpp "not enough space in the context's memory pool"

Without this patch, export-lora would sometimes error with "not enough space in the context's memory pool (needed 656784, available 656800)".

* increase required context size by 5*GGML_MEM_ALIGN instead of plain 16

---------

Co-authored-by: xaedes <xaedes@gmail.com>

* improve handling of not yet supported tensor types

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: meatbag-18a <145869052+meatbag-18a@users.noreply.github.com>
2023-09-28 21:40:11 +03:00
Georgi Gerganov
ec893798b7
llama : custom attention mask + parallel decoding + no context swaps (#3228)
* tests : verify that RoPE is "additive"

* llama : replace ggml_diag_mask_inf with ggml_add (custom -inf mask)

* ggml : ggml_rope now takes a vector with positions instead of n_past

* metal : add rope_f16 kernel + optimize cpy kernels

* llama : unified KV cache + batch inference API

* llama : add new llama_decode() API that works with llama_batch

* llama : add cell_max heuristic for more efficient kv_cache

* llama : extend llama_kv_cache API

* llama : more robust cell_max heuristic + wip shift

* metal : disable concurrency optimization

* llama : add llama_kv_cache_shift_seq + no more context swaps

* llama : apply K-cache roping for Falcon and Baichuan

* speculative : fix KV cache management

* parallel : example for serving multiple users in parallel

* parallel : disable hot-plug to avoid cache fragmentation

* fixes : speculative KV cache + llama worst-case graph

* llama : extend batch API to select which logits to output

* llama : fix worst case graph build

* ggml-cuda : update rope implementation for parallel decoding (#3254)

* ggml-cuda : update rope implementation for parallel decoding

* better solution for p0 computation

* fix rope

* simpler rope implementation

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* make : add parallel to build + fix static functions in llama.cpp

* simple : fix token counting

* parallel : various improvements

* llama : fix cell_max logic + rename functions

* parallel : try smaller batches when the KV cache is fragmented

* parallel : fix sequence termination criteria

* llama : silence errors KV cache errors

* parallel : remove new line from prompt

* parallel : process system prompt once + configurable paramters + llama API

* parallel : remove question with short answers

* parallel : count cache misses

* parallel : print misses on each request

* parallel : minor

* llama : fix n_kv to never become 0

* parallel : rename hot-plug to continuous-batching

* llama : improve llama_batch API + simplify parallel example

* simple : add parallel decoding support

* simple : improve comments + free batch

* ggml-cuda : add rope f16, restore performance with parallel decoding (#3272)

* ggml-cuda : add rope f16, restore performance

* offload KQ_mask with all models

* fix rope shift

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* llama : disable MPI for now

ggml-ci

* train : make KQ_pos memory buffer permanent via dummy scale op

* ggml : revert change to ggml_cpy, add ggml_cont_Nd instead (#3275)

ggml-ci

* parallel : fix bug (extra BOS) + smaller token_prev array

* parallel : fix cases where the input prompts can overflow the batch

* parallel : add disabled experimental batch chunking in powers of two

* llama : llama.h formatting + comments

* simple : add README.md

* llama : fix kv cache heuristic when context is less than 32

* parallel : fix crash when `-n -1`

* llama : simplify returns if/else branches

* metal : use mm kernels for batch size > 2

* examples : utilize new llama_get_logits_ith()

* examples : add example for batched decoding

* examples : do not eval prompt 2 times (close #3348)

* server : clear the KV cache beyond n_past before llama_decode

* server : avoid context swaps by shifting the KV cache

---------

Co-authored-by: slaren <slarengh@gmail.com>
2023-09-28 19:04:36 +03:00
Richard Roberson
ac43576124
make-ggml.py : compatibility with more models and GGUF (#3290)
* Resync my fork with new llama.cpp commits

* examples : rename to use dash instead of underscore

* New model conversions

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-09-27 19:25:12 +03:00
Cebtenzzre
20c7e1e804
gguf : fix a few general keys (#3341) 2023-09-27 12:18:07 -04:00
Rickard Hallerbäck
dc6897404e
metal : reusing llama.cpp logging (#3152)
* metal : reusing llama.cpp logging

* cmake : build fix

* metal : logging callback

* metal : logging va_args memory fix

* metal : minor cleanup

* metal : setting function like logging macro to capital letters

* llama.cpp : trailing whitespace fix

* ggml : log level enum used by llama

* Makefile : cleanup ggml-metal recipe

* ggml : ggml_log_callback typedef

* ggml : minor

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-09-27 18:48:33 +03:00
BarfingLemurs
ffe88a36a9
readme : add some recent perplexity and bpw measurements to READMES, link for k-quants (#3340)
* Update README.md

* Update README.md

* Update README.md with k-quants bpw measurements
2023-09-27 18:30:36 +03:00
slaren
c091cdfb24
llama-bench : add README (#3317)
* llama-bench : add README

* minor edit
2023-09-23 21:48:24 +02:00
yuiseki
f56c418ab0
embedding : update README.md (#3224) 2023-09-21 11:57:40 +03:00
Cebtenzzre
a5661d7e71
llama : allow gguf RoPE keys to be overridden with defaults (#3240) 2023-09-20 12:12:47 -04:00
Cebtenzzre
65c2c1c5ab
benchmark-matmult : do not use integer abs() on a float (#3277) 2023-09-20 12:06:08 -04:00
Georgi Gerganov
d119c04c15
examples : fix benchmark-matmult (#1554)
The precision for Q4_0 has degraded since #1508
2023-09-20 10:02:39 +03:00
Cebtenzzre
8781013ef6
make : restore build-info.h dependency for several targets (#3205) 2023-09-18 10:03:53 -04:00
goerch
b08e75baea
Fixing the last deviations from sentencepiece indicated by test-tokenizer-1 (#3170)
* Fix für #2721

* Reenable tokenizer test for LLaMa

* Add `console.cpp` dependency

* Fix dependency to `common`

* Fixing wrong fix.

* Make console usage platform specific

Work on compiler warnings.

* Adapting makefile

* Remove trailing whitespace

* Adapting the other parts of the makefile

* Fix typo.

* Fixing the last deviations from sentencepiece indicated by test-tokenizer-1

* Simplify logic

* Add missing change...

* Fix ugly compiler warning

* llama_tokenize should accept strings containing NUL now

* Adding huichen's test case
2023-09-16 13:41:33 +02:00
Cebtenzzre
e6616cf0db
examples : add compiler version and target to build info (#2998) 2023-09-15 16:59:49 -04:00
Cebtenzzre
3aefaab9e5
check C++ code with -Wmissing-declarations (#3184) 2023-09-15 15:38:27 -04:00
Georgi Gerganov
8c00b7a6ff
sync : ggml (Metal F32 support + reduce ggml-alloc size) (#3192)
* sync : ggml (Metal F32 support + reduce ggml-alloc size)

ggml-ci

* llama-bench : fix ggml_cpu_has_metal() duplicate function

ggml-ci
2023-09-15 19:06:03 +03:00
Roland
2d770505a8
llama : remove mtest (#3177)
* Remove mtest

* remove from common/common.h and examples/main/main.cpp
2023-09-15 10:28:45 +03:00
bandoti
990a5e226a
cmake : add relocatable Llama package (#2960)
* Keep static libs and headers with install

* Add logic to generate Config package

* Use proper build info

* Add llama as import library

* Prefix target with package name

* Add example project using CMake package

* Update README

* Update README

* Remove trailing whitespace
2023-09-14 20:04:40 +03:00
Leng Yue
35f73049af
speculative : add heuristic algorithm (#3006)
* Add heuristic algo for speculative

* Constrain minimum n_draft to 2

* speculative : improve heuristic impl

* speculative : be more rewarding upon guessing max drafted tokens

* speculative : fix typos

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-09-14 19:14:44 +03:00
FK
84e723653c
speculative: add --n-gpu-layers-draft option (#3063) 2023-09-13 08:50:46 +02:00
Cebtenzzre
e64f5b5578
examples : make n_ctx warning work again (#3066)
This was broken by commit e36ecdcc ("build : on Mac OS enable Metal by
default (#2901)").
2023-09-08 11:43:35 -04:00
Przemysław Pawełczyk
cb6c44c5e0
build : do not use _GNU_SOURCE gratuitously (#2035)
* Do not use _GNU_SOURCE gratuitously.

What is needed to build llama.cpp and examples is availability of
stuff defined in The Open Group Base Specifications Issue 6
(https://pubs.opengroup.org/onlinepubs/009695399/) known also as
Single Unix Specification v3 (SUSv3) or POSIX.1-2001 + XSI extensions,
plus some stuff from BSD that is not specified in POSIX.1.

Well, that was true until NUMA support was added recently,
so enable GNU libc extensions for Linux builds to cover that.

Not having feature test macros in source code gives greater flexibility
to those wanting to reuse it in 3rd party app, as they can build it with
FTMs set by Makefile here or other FTMs depending on their needs.

It builds without issues in Alpine (musl libc), Ubuntu (glibc), MSYS2.

* make : enable Darwin extensions for macOS to expose RLIMIT_MEMLOCK

* make : enable BSD extensions for DragonFlyBSD to expose RLIMIT_MEMLOCK

* make : use BSD-specific FTMs to enable alloca on BSDs

* make : fix OpenBSD build by exposing newer POSIX definitions

* cmake : follow recent FTM improvements from Makefile
2023-09-08 15:09:21 +03:00
Cebtenzzre
00d62adb79
fix some warnings from gcc and clang-tidy (#3038)
Co-authored-by: xaedes <xaedes@gmail.com>
2023-09-07 13:22:29 -04:00
slaren
15b67a66c2
llama-bench : use two tokens in the warmup run for prompt evals (#3059) 2023-09-07 15:52:34 +02:00
Cebtenzzre
de2fe892af
examples : replace fprintf to stdout with printf (#3017) 2023-09-05 15:10:27 -04:00
Georgi Gerganov
921772104b
speculative : add grammar support (#2991)
* speculative : add grammar support

* grammars : add json_arr.gbnf

* grammar : add comments to new grammar file

* grammar : remove one nested level

* common : warm-up with 2 tokens - seems to work better

* speculative : print draft token pieces

* speculative : reuse grammar parser + better logs and comments

* speculative : avoid grammar_mem

* make : fix speculative build
2023-09-05 08:46:17 +03:00
Georgi Gerganov
e36ecdccc8
build : on Mac OS enable Metal by default (#2901)
* build : on Mac OS enable Metal by default

* make : try to fix build on Linux

* make : move targets back to the top

* make : fix target clean

* llama : enable GPU inference by default with Metal

* llama : fix vocab_only logic when GPU is enabled

* common : better `n_gpu_layers` assignment

* readme : update Metal instructions

* make : fix merge conflict remnants

* gitignore : metal
2023-09-04 22:26:24 +03:00
Cebtenzzre
3103568144
llama-bench : make cpp file non-executable (#2999) 2023-09-04 13:40:18 +03:00
Aarni Koskela
e4386f417f
server : add a subtle loading animation to the edit box (#2466)
* editorconfig: add override for the server HTML (which already is 2-space indented)

* server: add a subtle loading animation to the edit box
2023-09-04 16:28:55 +08:00
Georgi Gerganov
47068e5170
speculative : PoC for speeding-up inference via speculative sampling (#2926)
* speculative : initial example

* speculative : print encoding speed

* speculative : add --draft CLI arg
2023-09-03 15:12:08 +03:00
Georgi Gerganov
8f429fa511
perplexity : fix ETA by warming up the model with an empty run 2023-09-03 13:43:17 +03:00
momonga
c42f0ec6b3
examples : fix gpt-neox (#2943)
Co-authored-by: mmnga <mmnga1mmnga@gmail.com>
2023-09-03 08:36:28 +03:00
Jhen-Jie Hong
571083f508
server : avoid aniprompt in probabilities of final response (#2849) 2023-09-02 08:31:46 +08:00
ZHAOKAI WANG
69fdbb9abc
readme : quick start command fix (#2908)
* quick start command fix

* quick start win command fix
2023-09-01 17:06:44 +03:00
Kerfuffle
5d6f19f16b
Allow quantize to only copy tensors, some other improvements (#2931)
* Allow quantize tool to only copy tensors to allow repackaging models.

* Slightly better logic when requantizing.

* Change help message to go to `stdout`.
2023-09-01 08:02:48 -06:00
Georgi Gerganov
0d58936686
llama2c : rename function 2023-09-01 17:01:11 +03:00
m3ndax
ee8654bcd0
minor : add const qualifiers (#2853)
* made the methods const

# Conflicts:
#	examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp

* made method const

* Update convert-llama2c-to-ggml.cpp

removed write_raw and write_u32

* llama2c : remove misleading const

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-09-01 16:47:27 +03:00
Cebtenzzre
ef15649972
build : fix most gcc and clang warnings (#2861)
* fix most gcc and clang warnings

* baby-llama : remove commented opt_params_adam

* fix some MinGW warnings

* fix more MinGW warnings
2023-09-01 16:34:50 +03:00
Cebtenzzre
18705a30ef
llama2c : fix segfault and alloc-dealloc-mismatch (#2913)
* llama2c : fix segfault if vocab is not found

* llama2c : fix mismatch between new[] and delete

* llama2c : fix basename on Windows

* llama2c : use a destructor to prevent memory leaks
2023-09-01 12:03:49 +03:00
Kerfuffle
aeefac4ff7
scripts: Use local gguf package when running from repo (#2927)
* scripts: Use local gguf when running from repo
2023-08-31 16:49:24 -06:00
Georgi Gerganov
c90d135eb4
examples : fix underscore in beam-search + .gitignore (close #2900) 2023-08-30 12:53:24 +03:00
chaihahaha
ad9ddcff6e
llm.vim : stop generation at multiple linebreaks, bind to <F2> (#2879) 2023-08-30 09:50:55 +03:00
staviq
8341a25957
main : log file (#2748)
* initial, base LOG macro

* add *.log to .gitignore

* added basic log file handler

* reverted log auto endline to better mimic printf

* remove atomics and add dynamic log target

* log_enable/disable, LOG_TEE, basic usage doc

* update .gitignore

* mv include to common, params, help msg

* log tostring helpers, token vectors pretty prints

* main: replaced fprintf/LOG_TEE, some trace logging

* LOG_DISABLE_LOGS compile flag, wrapped f in macros

* fix LOG_TEELN and configchecker

* stub LOG_DUMP_CMDLINE for WIN32 for now

* fix msvc

* cleanup main.cpp:273

* fix stray whitespace after master sync

* log : fix compile warnings

- do not use C++20 stuff
- use PRIu64 to print uint64_t
- avoid string copies by using const ref
- fix ", ##__VA_ARGS__" warnings
- compare strings with == and !=

* log : do not append to existing log + disable file line func by default

* log : try to fix Windows build

* main : wip logs

* main : add trace log

* review: macro f lowercase, str append to sstream

* review: simplify ifs and str comparisons

* fix MSVC, formatting, FMT/VAL placeholders

* review: if/else cleanup

* review: if/else cleanup (2)

* replace _ prefix with _impl suffix

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-08-30 09:29:32 +03:00
Kawrakow
fa3582f509
Tell users attmepting to run perplexity with too few tokens to use more (#2882)
Closes #2858

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2023-08-29 23:55:45 +03:00
xaedes
44c117f41e
train : mem usage and other improvements (#2439)
* fix track_max_mem in forward_batch_wo_cache_flash_attn_train

* remove unnecessary Adam(W) optimizer tensors.

reduces optimizer memory overhead from 7*modelsize to 2*modelsize.

additionally allows to optimize models with more than 2^31 parameters by replacing int with int64_t.

bumps training checkpoint file version, but old checkpoints can still be read.
new version with less tensors is saved.

* add gradient clipping to AdamW

* Fix reset of unused g->nodes and g->grads to NULL

* implement gradient checkpointing for training

reduces memory overhead from O(n_layer) to O(sqrt(n_layer))

as explained in readme of https://github.com/cybertronai/gradient-checkpointing

* remove unused compute buffer 3

* add and use function ggml_build_backward_expand to avoid stack overflows with large maximum number of nodes

GGML_API void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool keep);

* change AdamW decay parameter to work like the torch AdamW decay parameter

It is now relative to Adam learning rate `alpha*sched`.
Before that it was relative to `sched` only.

`alpha` being the maximum learning rate and `sched` being a scaling parameter in [0..1]

* change default AdamW weight decay parameter used in training to 0.1 as used in nanoGPT

* change default AdamW weight decay parameter defined in ggml to 0.0, making Adam default instead of AdamW

btw: the default weight decay parameter for torch.optim.AdamW is 0.01

* bug fixes for cross entropy loss

ggml_cross_entropy_loss: sums where not correctly added in workload of each thread
ggml_cross_entropy_loss_back: simplify backward process, reducing numerical issues

guard usage of exp f16 lookup in cross entropy by #define GGML_CROSS_ENTROPY_EXP_FP16

cross entropy loss is only used once during training, but it is quite sensitive to numerical errors introduced by exp-f16-lookup.
so exp-f16-lookup for cross entropy loss is disabled by default, trading better gradients for very slightly worse runtime performance.

* fix test-grad0 for cross_entropy_loss

the second argument to cross_entropy_loss must sum up to 1 for each row

* fix test-grad0 for soft_max

dont use only sum as aggregation, because sum of softmax is always 1 -> finite differences should not work
instead use sum(log(soft_max()*(1-eps)+eps)); use eps to avoid log(0)

* improve finite differences of test-grad0 by using double instead of float

* change cross_entropy_loss to output average over all rows

this helps keeping the loss and gradients in a sane range

* improve gradient checkpointing

sqrt(n_layers) is only the best checkpoint step when mem size of checkpoints and mem size of layers are equal.
since layers require more memory than the single-tensor-checkpoint we use, the optimal values are compute different:

```
  given: n, u, v
  objective: minimize(a*u+b*v) where a*b=n, a>0, b>0
  b=n/a
  minimize(a*u+v*n/a)
  diff(a*u+v*n/a, a) = u - (v*n/a)/a
  diff(a*u+v*n/a, a) == 0
  u - (v*n/a)/a == 0
  u == v*n/(a*a)
  u*a*a = v*n
  a*a = v*n/u
  a = sqrt(n*v/u)
```

this change results in more checkpoints, requiring less layers to store between checkpoints, overall improving memory usage.

* disable gradient checkpointing debug output

* llama : fix rope usage in train-text-from-scratch after ChatGLM change

* add more training parameters:

--enable-restart N         Only for Adam optimizer. Enable restarts of cos-decay
--disable-restart N        Only for Adam optimizer. Disable restarts of cos-decay
--opt-past N               Number of optimization iterations to track for delta convergence test. Disabled when zero.
--opt-delta N              Maximum delta for delta convergence test. Disabled when <= zero.
--opt-max-no-improvement N Maximum number of optimization iterations with no improvement. Disabled when <= zero.
--adam-epsf N              AdamW epsilon for convergence test. Disabled when <= zero.
--adam-min-alpha N         Adam minimum learning rate alpha, usually 0.1 * alpha

* replace memcpy with reshape operation so that the graph is not cut at the input

this makes it possible to store other values into the input tensor and then simply recompute the graph without rebuilding it

* remove unused function argument from get_example_targets_batch

* measure and print total training time

* add optimization callback to ggml_opt_resume_g

this callback is called before each iteration with custom data and pointer to learning schedule parameter (only used in Adam(W)).

can be used for dynamic learning schedule and setting input data for batches before each iteration

* use optimization callback in training

allows dynamic learning schedule and different batch data for each iteration without relying on low n_iter and high n_examples parameters

reduces runtime by avoiding restart of optimization function and improves training convergence by providing a different batch for each iteration

* add minimum number of tensor dimensions to apply weight decay (default 2)

this allows to not apply weight decay to bias parameters

* rename training parameter cos-decay-alpha to cos-decay-min and clarify that adam-min-alpha also applies to warmup

* fix increase of model.train_samples and model.train_tokens

now that each optimizer iteration gets its own batch we need to multiply by number of opt iterations

* change sampling parameters for prediction after training to defaults of common.h

and clarify what is context for prediction and what are generated tokens

* tighten abs error bounds for cross_entropy_loss in test-grad0

* add conditional compilation of using F16 exp in flash attention

uncomment `// #define GGML_FLASH_ATTN_EXP_FP16` to enable usage of f16 exp in flash attention

* tighten abs error bounds for flash_attn in test-grad0

* tighten abs error bounds for sqrt in test-grad0

* remove out-commented vectorized code of opt_adam

the vectorized code might be bit faster for low number of parameters, but it had a big memory usage overhead

* ggml : update ggml_rms_norm_back with configurable eps

* llama training : fix ggml_rms_norm_back calls to pass configurable eps

* remove trailing whitespace

* add train function using automatic gradient checkpointing backward pass and allocator

* in train function replace add_inplace by regular add

because using add_inplace seems to result in different gradients

* don't use allocate hash_map on context

because the context has no_alloc=True when using memory allocator resulting in NULL data pointers

* correctly clone reshape and permute operations by also cloning tensor->nb values

* fix variable name and add missing type cast

* terminate recursive tensor cloning when reaching tensor without src tensors

* correctly clone view tensors by setting data pointers

without this the checkpointing would only work when being used together with memory allocator

* fix variable names

* swap arguments to commutative ops to be the same as in `forward_batch_wo_cache_flash_attn`

* add input tensors as checkpoints

so that recursive tensor cloning of gradient checkpointing terminates on input tensors

* fix variable name and add missing boolean negation

* make sure some tensors are not reallocated by inserting new temporary nodes depending on them:

output and parameter gradient tensors need to be available at the end of the graph execution

parameter gradient tensors also need to be available before the graph execution because they are set to zero before each optimizer iteration

checkpoint tensors are allocated all together to reduce memory allocator fragmentation

afterwards, in addition to the temporary nodes, we also need to reset the temporary leafs

* fix ASSERT to work with zero layers

* add training options whether to use allocator and/or unified training function

* integrate unified training function which may use memory allocator

the unified training function also supports arguments whether to use flash attention and/or gradient checkpointing

* format name of cloned tensors with " (clone)" suffix

* set names for tensors in unified train function for easier debugging

* allocate graph on context using ggml_new_graph

* remove handwritten training functions

* remove unused training parameters "use_scratch" and "use_unified"

* remove trailing whitespace

* remove unused train params: mem_compute1_gb & mem_compute2_gb

mem_compute_gb is used for compute when automatic memory allocator is not enabled, otherwise it can be very small to only hold the tensor definitions
mem_compute0_gb is used for automatic memory allocator (as long as measurement of max required size is not implemented)

* remove unused forward_batch function

* add debug asserts in ggml_allocr_alloc to some common pitfalls when using this function directly

* only use ggml_allocr_alloc when tensor has NULL data and is no view

* fix test when to create temporary backward graph

temporary backward graph is only necessary when using checkpointing

* fix memory "leak" in optimizers

each iteration a new cplan with new memory for work data was allocated.
now cplan creation only happens at the start of optimization, with each iteration reusing the cplan and its work data.

* reverse order of for loop in ggml_build_backward_expand to save memory when using gradient checkpointing and allocator

with this loop order gradient checkpointing with allocator on 16 layer model saves 13% memory; 2 layer memory it saves 2% memory.

the computation results are the same

* add missing lctx argument to get_example_targets_batch

* implement llama model file saving using gguf

checkpoint loading and saving disabled, to be replaced by loading and saving via gguf

* implement loading/saving of checkpointing files using GGUF

* bug fixes

* add checkpoint file version for future compatibility

* update readme with gguf filenames

* save & load opt->just_initialized value

* add first draft for checkpoint conversion script

* add gguf arch and ftype

* save opt parameter counter as uint64

* add gguf key and tensor names for optimizer and training

* add layer_norm_rms_eps to checkpoint convert script

* use same GGUF_GET_KEY macro as in llama.cpp

* use norm_rms_eps, and rope parameters and command line options to set them

* fix memory corruption bug in gguf

ctx->kv and ctx->infos was reallocated using not-aligned realloc, but freed with aligned free.
to fix this a GGML_ALIGNED_REALLOC was added, but there is no posix_memalign_realloc function.
so on non-windows and non-mingw32 platforms we fall back to aligned malloc, followed by copying
and freeing the old data.

* add gguf example cmake file

* bug fixes in tokenize_file

* bug fixes in load_llama_model_gguf

* bug fix: init model when no checkpoint was loaded

* bug fix in read_tensor_by_name

* bug fix in load_opt_context_gguf

* avoid printing lots of spaced on the unusual case that loss gets nan

* set name of tensors with empty name from what was read from gguf

* remove trailing whitespace

* print data checksums before saving and after loading to verify correctness

* bug fixes for convert-train-checkpoint-to-gguf

* temporarily add code to write old checkpoint files

used to verify that old checkpoint files are correctly converted to gguf

* bug fixes for convert-train-checkpoint-to-gguf.py loading checkpoints with opt_version=0

* remove code used to verify correctness of checkpoint file conversion

* remove trailing whitespace

* remove prediction related code

use main for prediction, it is better optimized

* update train-text-from-scratch README.md

* fix non-windows GGML_ALIGNED_REALLOC

* add missing blank line at end of file

* remove GGML_ALIGNED_REALLOC and use normal malloc/realloc/free for gguf ctx->kv & ctx->infos

* train : fix compile warnings

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-08-28 22:51:47 +03:00
slaren
43033b7bb4
llama-bench : set locale to utf8 (#2832) 2023-08-28 19:19:18 +02:00
Johannes Gäßler
6b73ef1201
YAML result logging + preset script (#2657) 2023-08-28 17:59:39 +02:00
Cebtenzzre
ebcee207b6
quantize : make output filename optional again (#2823)
* quantize : make output filename optional again

* quantize : fix path parsing on Windows

suggested by @slaren
2023-08-28 09:32:25 +03:00
Olivier Chafik
230d46c723
examples : update llama2.c converter to read vocab and write models in GGUF format (#2751)
* llama2.c: direct gguf output (WIP)

* Simplify vector building logic

* llama2.c gguf conversion: fix token types in converter

* llama2.c: support copying vocab from a llama gguf model file

* llama2.c: update default path for vocab model + readme

* llama2.c: use defines for gguf keys

* llama2.c: escape whitespaces w/ U+2581 in vocab converter the llama.cpp way

* llama2.c converter: cleanups + take n_ff from config
2023-08-27 17:13:31 +03:00
Kawrakow
463173a6c0
llama : speedup tokenization (#2831)
* 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>
2023-08-27 16:50:33 +03:00
Georgi Gerganov
d0cee0d36d
gguf : add 64-bit support (GGUF v2) (#2821)
* gguf : bump version to 2

* gguf : add support for 64-bit (no backwards comp yet)

* gguf : v1 backwards comp

* gguf.py : bump GGUF version

* gguf.py : uint64_t on all lengths, sizes and counts, enums still uint32_t

* gguf.py : string lengths uint32_t

* gguf : update all counts to 64-bit

* gguf.py : string len uint64_t and n_dims uint32_t

* gguf : fix typo

* llama.cpp : print gguf version

---------

Co-authored-by: klosax <131523366+klosax@users.noreply.github.com>
2023-08-27 14:19:54 +03:00
Georgi Gerganov
edd4c14817
llama : more tokenizer fixes (#2810)
* 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>
2023-08-27 14:19:19 +03:00
Bruce MacDonald
c1ac54b77a
server : add /detokenize endpoint (#2802)
* Add a /detokenize endpoint to the example server

* remove trailing white-space
2023-08-27 07:11:45 +08:00
Dr. Tom Murphy VII Ph.D
72f895c923
main : fix bug (penalize_nl=false doesn't work) + suppress warning on mingw (#1528)
* Fix bug in main.cpp where penalize_nl=false has no effect. It modifies the underlying logits array, but at this point we are already working on the candidates copy.

* Suppress redefinition warning for NOMINMAX on mingw. In my installation, this macro is already defined by /usr/lib/gcc/x86_64-w64-mingw32/11/include/c++/x86_64-w64-mingw32/bits/os_defines.h:45.

* main : fix indentation

* main : pass ctx to llama_token_nl()

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-08-26 21:12:56 +03:00
Kawrakow
771551a793
Fix HellaSwag (#2805)
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2023-08-26 16:48:53 +03:00
klosax
2ba83c8685
Fix spm whitespaces (#2806)
* 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
2023-08-26 13:45:53 +02:00
lon
bae5c5f679
examples : skip unnecessary external lib in server README.md how-to (#2804) 2023-08-26 16:07:43 +08:00
Kawrakow
d046dcee08
Faster perplexity computation (#2786)
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2023-08-25 19:05:02 +03:00
Matt Pulver
c82742ac9c
llama : add llama_beam_search() (#2267)
* 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.
2023-08-25 18:18:48 +03:00
slaren
154725c543
llama-bench : add model sizes (#2771)
* llama-bench : add model sizes

* more compact markdown output

* back to GiB

* adjust column sizes
2023-08-25 15:16:19 +02:00
Jhen-Jie Hong
29674ab4e8
server : display token probabilities in the UI (#2489)
* server : add n_probs param in chat UI

* server : keep message data array & show in probabilites component

* server : add simple popover component

* server : fix completion_probabilities undefined if not set n_probs

* server : implement Probabilites

* server : handle bytes

* server : make n_probs max to 10 for easy scroll

* server : adjust for dark/light mode

* server : Fix regenerated prompt

* server : update index.html.hpp

* server : convert prob to percentage + show original value as div title

* server : fix Probabilites not used if included empty str

* server : skip byte pair in display probabilites

* server : remove array check of completion_probabilities in messages

* skip empty array or byte pair (> 1) in Probabilites

* generate index.html.hpp

* fix incorrect prob convert if the str is already a known token

* use final response to show probabilities on stop

* revert unnecessary change

* correct probabilites usage

* remove unused function

* always send partial response for get correct probs of last to_send

* fix typo

* fix content of format_final_response

* refactor probs render & make pColor transparent if not found

* send empty string when got stop_pos in partial

* avoid unnecessary empty data event & send rest of partial tokens on stop

* use <br /> for new line

* skip -1 tok in loop to avoid send '' on end

* trim last new lines on stop

* revert unnecessary change
2023-08-25 18:32:45 +08:00
Henri Vasserman
6bbc598a63
ROCm Port (#1087)
* 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>
2023-08-25 12:09:42 +03:00
Kerfuffle
7694adda8d
Fix for main example getting stuck when -n -2 and --interactive (#2767)
* Fix for main example getting stuck when -n -2 and --interactive

* Add a comment so future generations may suffer less.
2023-08-24 10:11:13 -06:00
Georgi Gerganov
cf658adc83
llm : add Falcon support (#2717)
* llama : refactor GGUF constants into static maps

* llama : check if model architecture is known

* llama : refactor llama_model_load_internal()

* gguf : add KV constant maps

* llm : read arch-specific KVs

* convert : add dummy scores + types

* falcon : load tensor data (CPU only)

* llama : fix loading progress bar

* llama : add arch member to llama_model

* falcon : CPU inference working

* falcon : support non-40B models

* falcon : minor

* llama : minor updates

ggml-ci

* convert-falcon-hf-to-gguf.py : fix special token mapping

* llama.cpp : llama default UNK token = id 0

* llama.cpp : fix bpe tokenizer

* llama.cpp : fix the fix of bpe tokenizer

* ggml : pass eps to ggml_norm

* metal : implement RoPE (mode = 2) + avoid ggml_repeat

* ggml : ggml_repeat always creates new tensor

* falcon : copy-paste self-attention from LLaMA

* metal : print extra compute pipeline info

* falcon : minor changes (still chasing the Metal problem)

* llama.cpp : fix linefeed token

* metal : fix GELU kernel numerical stability by using precise::tanh

* metal : temporary workaround for the concurrency optimization bug

* falcon : add CUDA offloading (#2739)

* llama : better model naming and size reporting

* llama : prep new tokenizer support

* llama : advanced BPE tokenizer based on ggllm.cpp imlpementation

* llama : remove oboslete comment

ggml-ci

* common : remove obsolete BPE API + disable test-tokenizer-1

* llama : revert BPE special-case in llama_byte_to_token()

* cuda : add TODOs for RoPE NeoX implementation

* llama : default special tokens based on vocab type

* perplexity : add log for start of tokenization

---------

Co-authored-by: klosax <131523366+klosax@users.noreply.github.com>
Co-authored-by: slaren <slarengh@gmail.com>
2023-08-23 23:08:04 +03:00
Georgi Gerganov
a192860cfe
minor : fix trailing whitespace 2023-08-23 22:37:39 +03:00
Olivier Chafik
95385241a9
examples : restore the functionality to import llama2.c models (#2685)
* Fix import of llama2.c models that don't share weights between embedding layers

* llama2c: reinstate ggmlv3 conversion output + update readme w/ gguf conv

* llama2.c: comment out legacy "load from ggml model" logic

* llama2.c: convert special-cased "<0xXX>" single byte tokens from tokenizer.bin
2023-08-23 22:33:05 +03:00
klosax
5290c38e6e
main : insert bos if no tokens (#2727)
* main.cpp : insert bos if no tokens

* Update examples/main/main.cpp

* Update examples/main/main.cpp

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-08-23 16:46:03 +02:00
Cebtenzzre
7c2227a197
chmod : make scripts executable (#2675) 2023-08-23 17:29:09 +03:00
Kawrakow
8207214b6a
Fix values shown in the quantize tool help (#2735)
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2023-08-23 12:57:12 +03:00
Kawrakow
62959e740e
Strided perplexity (#2714)
* Implementing strided computation of perplexity

* Alternative way to output PPL results

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2023-08-23 12:56:42 +03:00
Xiao-Yong Jin
b8ad1b66b2
server : allow json array in prompt or content for direct token input (#2306)
* server: allow json array in prompt or content

We accept an array of strings and numbers representing tokens,
in addition to the current string valued prompt or content.

This allows direct token input, so that any special tokens
can be processed and used at the frontend during the construction
of the json data, before sending to the server. And the server
does not need to know or parse special tokens from textual input.

With this, we can use EOS and BOS used in llama-2-chat models.

* server: use tokenizePrompt(json) and default "" if empty prompt

* server: fix prompt check

* server: tokenize endpoint no longer adds BOS
2023-08-23 15:12:12 +08:00
Evan Jones
f5fe98d11b
docs : add grammar docs (#2701)
* docs : add grammar docs

* tweaks to grammar guide

* rework GBNF example to be a commented grammar
2023-08-22 21:01:57 -04:00
Johannes Gäßler
c63bb1d16a
CUDA: use mul_mat_q kernels by default (#2683) 2023-08-22 22:47:05 +02:00
slaren
519c981f8b
embedding : evaluate prompt in batches (#2713) 2023-08-22 16:03:12 +02:00
Georgi Gerganov
ef3f333d37
ggml : sync latest (SAM + SD operators, CUDA alibi) (#2709)
* ggml : sync latest (SAM + SD operators, CUDA alibi)

ggml-ci

* ggml : fix tabs
2023-08-22 14:22:08 +03:00
slaren
8e4364f2af
llama-bench : minor fixes (#2695) 2023-08-22 10:56:03 +03:00
Jhen-Jie Hong
226255b44e
server : fallback to default if client param is null (#2688)
* server : fallback to default if client param is null

* server : do not overwrite 404 if status is 500 from exception_handler
2023-08-22 08:32:00 +08:00
Georgi Gerganov
6381d4e110
gguf : new file format with flexible meta data (beta) (#2398)
* gguf : first API pass

* gguf : read header + meta data

* gguf : read tensor info

* gguf : initial model loading - not tested

* gguf : add gguf_get_tensor_name()

* gguf : do not support passing existing ggml_context to gguf_init

* gguf : simplify gguf_get_val

* gguf : gguf.c is now part of ggml.c

* gguf : read / write sample models

* gguf : add comments

* refactor : reduce code duplication and better API (#2415)

* gguf : expose the gguf_type enum through the API for now

* gguf : add array support

* gguf.py : some code style changes

* convert.py : start a new simplified implementation by removing old stuff

* convert.py : remove GGML vocab + other obsolete stuff

* GGUF : write tensor (#2426)

* WIP: Write tensor

* GGUF : Support writing tensors in Python

* refactor : rm unused import and upd todos

* fix : fix errors upd writing example

* rm example.gguf

* gitignore *.gguf

* undo formatting

* gguf : add gguf_find_key (#2438)

* gguf.cpp : find key example

* ggml.h : add gguf_find_key

* ggml.c : add gguf_find_key

* gguf : fix writing tensors

* gguf : do not hardcode tensor names to read

* gguf : write sample tensors to read

* gguf : add tokenization constants

* quick and dirty conversion example

* gguf : fix writing gguf arrays

* gguf : write tensors one by one and code reuse

* gguf : fix writing gguf arrays

* gguf : write tensors one by one

* gguf : write tensors one by one

* gguf : write tokenizer data

* gguf : upd gguf conversion script

* Update convert-llama-h5-to-gguf.py

* gguf : handle already encoded string

* ggml.h : get array str and f32

* ggml.c : get arr str and f32

* gguf.py : support any type

* Update convert-llama-h5-to-gguf.py

* gguf : fix set is not subscriptable

* gguf : update convert-llama-h5-to-gguf.py

* constants.py : add layer norm eps

* gguf.py : add layer norm eps and merges

* ggml.h : increase GGML_MAX_NAME to 64

* ggml.c : add gguf_get_arr_n

* Update convert-llama-h5-to-gguf.py

* add gptneox gguf example

* Makefile : add gptneox gguf example

* Update convert-llama-h5-to-gguf.py

* add gptneox gguf example

* Update convert-llama-h5-to-gguf.py

* Update convert-gptneox-h5-to-gguf.py

* Update convert-gptneox-h5-to-gguf.py

* Update convert-llama-h5-to-gguf.py

* gguf : support custom alignment value

* gguf : fix typo in function call

* gguf : mmap tensor data example

* fix : update convert-llama-h5-to-gguf.py

* Update convert-llama-h5-to-gguf.py

* convert-gptneox-h5-to-gguf.py : Special tokens

* gptneox-main.cpp : special tokens

* Update gptneox-main.cpp

* constants.py : special tokens

* gguf.py : accumulate kv and tensor info data + special tokens

* convert-gptneox-h5-to-gguf.py : accumulate kv and ti + special tokens

* gguf : gguf counterpart of llama-util.h

* gguf-util.h : update note

* convert-llama-h5-to-gguf.py : accumulate kv / ti + special tokens

* convert-llama-h5-to-gguf.py : special tokens

* Delete gptneox-common.cpp

* Delete gptneox-common.h

* convert-gptneox-h5-to-gguf.py : gpt2bpe tokenizer

* gptneox-main.cpp : gpt2 bpe tokenizer

* gpt2 bpe tokenizer (handles merges and unicode)

* Makefile : remove gptneox-common

* gguf.py : bytesarray for gpt2bpe tokenizer

* cmpnct_gpt2bpe.hpp : comments

* gguf.py : use custom alignment if present

* gguf : minor stuff

* Update gptneox-main.cpp

* map tensor names

* convert-gptneox-h5-to-gguf.py : map tensor names

* convert-llama-h5-to-gguf.py : map tensor names

* gptneox-main.cpp : map tensor names

* gguf : start implementing libllama in GGUF (WIP)

* gguf : start implementing libllama in GGUF (WIP)

* rm binary commited by mistake

* upd .gitignore

* gguf : calculate n_mult

* gguf :  inference with 7B model working (WIP)

* gguf : rm deprecated function

* gguf : start implementing gguf_file_saver (WIP)

* gguf : start implementing gguf_file_saver (WIP)

* gguf : start implementing gguf_file_saver (WIP)

* gguf : add gguf_get_kv_type

* gguf : add gguf_get_kv_type

* gguf : write metadata in gguf_file_saver (WIP)

* gguf : write metadata in gguf_file_saver (WIP)

* gguf : write metadata in gguf_file_saver

* gguf : rm references to old file formats

* gguf : shorter name for member variable

* gguf : rm redundant method

* gguf : get rid of n_mult, read n_ff from file

* Update gguf_tensor_map.py

* Update gptneox-main.cpp

* gguf : rm references to old file magics

* gguf : start implementing quantization (WIP)

* gguf : start implementing quantization (WIP)

* gguf : start implementing quantization (WIP)

* gguf : start implementing quantization (WIP)

* gguf : start implementing quantization (WIP)

* gguf : start implementing quantization (WIP)

* gguf : quantization is working

* gguf : roper closing of file

* gguf.py : no need to convert tensors twice

* convert-gptneox-h5-to-gguf.py : no need to convert tensors twice

* convert-llama-h5-to-gguf.py : no need to convert tensors twice

* convert-gptneox-h5-to-gguf.py : simplify nbytes

* convert-llama-h5-to-gguf.py : simplify nbytes

* gptneox-main.cpp : n_layer --> n_block

* constants.py : n_layer --> n_block

* gguf.py : n_layer --> n_block

* convert-gptneox-h5-to-gguf.py : n_layer --> n_block

* convert-llama-h5-to-gguf.py : n_layer --> n_block

* gptneox-main.cpp : n_layer --> n_block

* Update gguf_tensor_map.py

* convert-gptneox-h5-to-gguf.py : load model in parts to save memory

* convert-llama-h5-to-gguf.py : load model in parts to save memory

* convert : write more metadata for LLaMA

* convert : rm quantization version

* convert-gptneox-h5-to-gguf.py : add file_type key

* gptneox-main.cpp : add file_type key

* fix conflicts

* gguf : add todos and comments

* convert-gptneox-h5-to-gguf.py : tensor name map changes

* Create gguf_namemap.py : tensor name map changes

* Delete gguf_tensor_map.py

* gptneox-main.cpp : tensor name map changes

* convert-llama-h5-to-gguf.py : fixes

* gguf.py : dont add empty strings

* simple : minor style changes

* gguf : use UNIX line ending

* Create convert-llama-7b-pth-to-gguf.py

* llama : sync gguf-llama.cpp with latest llama.cpp (#2608)

* llama : sync gguf-llama.cpp with latest llama.cpp

* minor : indentation + assert

* llama : refactor gguf_buffer and gguf_ctx_buffer

* llama : minor

* gitignore : add gptneox-main

* llama : tokenizer fixes (#2549)

* Merge tokenizer fixes into the gguf branch.

* Add test vocabularies

* convert : update convert-new.py with tokenizer fixes (#2614)

* Merge tokenizer fixes into the gguf branch.

* Add test vocabularies

* Adapt convert-new.py (and fix a clang-cl compiler error on windows)

* llama : sync gguf-llama with llama (#2613)

* llama : sync gguf-llama with llama

* tests : fix build + warnings (test-tokenizer-1 still fails)

* tests : fix wstring_convert

* convert : fix layer names

* llama : sync gguf-llama.cpp

* convert : update HF converter to new tokenizer voodoo magics

* llama : update tokenizer style

* convert-llama-h5-to-gguf.py : add token types

* constants.py : add token types

* gguf.py : add token types

* convert-llama-7b-pth-to-gguf.py : add token types

* gguf-llama.cpp :  fix n_head_kv

* convert-llama-h5-to-gguf.py : add 70b gqa support

* gguf.py : add tensor data layout

* convert-llama-h5-to-gguf.py : add tensor data layout

* convert-llama-7b-pth-to-gguf.py : add tensor data layout

* gptneox-main.cpp : add tensor data layout

* convert-llama-h5-to-gguf.py : clarify the reverse permute

* llama : refactor model loading code (#2620)

* llama : style formatting + remove helper methods

* llama : fix quantization using gguf tool

* llama : simplify gguf_file_saver

* llama : fix method names

* llama : simplify write_header()

* llama : no need to pass full file loader to the file saver

just gguf_ctx

* llama : gguf_file_saver write I32

* llama : refactor tensor names (#2622)

* gguf: update tensor names searched in quantization

* gguf : define tensor names as constants

* gguf : initial write API (not tested yet)

* gguf : write to file API (not tested)

* gguf : initial write API ready + example

* gguf : fix header write

* gguf : fixes + simplify example + add ggml_nbytes_pad()

* gguf : minor

* llama : replace gguf_file_saver with new gguf write API

* gguf : streaming support when writing files

* gguf : remove oboslete write methods

* gguf : remove obosolete gguf_get_arr_xxx API

* llama : simplify gguf_file_loader

* llama : move hparams and vocab from gguf_file_loader to llama_model_loader

* llama : merge gguf-util.h in llama.cpp

* llama : reorder definitions in .cpp to match .h

* llama : minor simplifications

* llama : refactor llama_model_loader (WIP)

wip : remove ggml_ctx from llama_model_loader

wip : merge gguf_file_loader in llama_model_loader

* llama : fix shape prints

* llama : fix Windows build + fix norm_rms_eps key

* llama : throw error on missing KV paris in model meta data

* llama : improve printing + log meta data

* llama : switch print order of meta data

---------

Co-authored-by: M. Yusuf Sarıgöz <yusufsarigoz@gmail.com>

* gguf : deduplicate (#2629)

* gguf : better type names

* dedup : CPU + Metal is working

* ggml : fix warnings about unused results

* llama.cpp : fix line feed and compiler warning

* llama : fix strncpy warning + note token_to_str does not write null

* llama : restore the original load/save session implementation

Will migrate this to GGUF in the future

* convert-llama-h5-to-gguf.py : support alt ctx param name

* ggml : assert when using ggml_mul with non-F32 src1

* examples : dedup simple

---------

Co-authored-by: klosax <131523366+klosax@users.noreply.github.com>

* gguf.py : merge all files in gguf.py

* convert-new.py : pick #2427 for HF 70B support

* examples/gguf : no need to keep q option for quantization any more

* llama.cpp : print actual model size

* llama.cpp : use ggml_elements()

* convert-new.py : output gguf (#2635)

* convert-new.py : output gguf (WIP)

* convert-new.py : add gguf key-value pairs

* llama : add hparams.ctx_train + no longer print ftype

* convert-new.py : minor fixes

* convert-new.py : vocab-only option should work now

* llama : fix tokenizer to use llama_char_to_byte

* tests : add new ggml-vocab-llama.gguf

* convert-new.py : tensor name mapping

* convert-new.py : add map for skipping tensor serialization

* convert-new.py : convert script now works

* gguf.py : pick some of the refactoring from #2644

* convert-new.py : minor fixes

* convert.py : update to support GGUF output

* Revert "ci : disable CI temporary to not waste energy"

This reverts commit 7e82d25f40.

* convert.py : n_head_kv optional and .gguf file extension

* convert.py : better always have n_head_kv and default it to n_head

* llama : sync with recent PRs on master

* editorconfig : ignore models folder

ggml-ci

* ci : update ".bin" to ".gguf" extension

ggml-ci

* llama : fix llama_model_loader memory leak

* gptneox : move as a WIP example

* llama : fix lambda capture

ggml-ci

* ggml : fix bug in gguf_set_kv

ggml-ci

* common.h : .bin --> .gguf

* quantize-stats.cpp : .bin --> .gguf

* convert.py : fix HF tensor permuting / unpacking

ggml-ci

* llama.cpp : typo

* llama : throw error if gguf fails to init from file

ggml-ci

* llama : fix tensor name grepping during quantization

ggml-ci

* gguf.py : write tensors in a single pass (#2644)

* gguf : single pass for writing tensors + refactoring writer

* gguf : single pass for writing tensors + refactoring writer

* gguf : single pass for writing tensors + refactoring writer

* gguf : style fixes in simple conversion script

* gguf : refactor gptneox conversion script

* gguf : rename h5 to hf (for HuggingFace)

* gguf : refactor pth to gguf conversion script

* gguf : rm file_type key and method

* gguf.py : fix vertical alignment

* gguf.py : indentation

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* convert-gptneox-hf-to-gguf.py : fixes

* gguf.py : gptneox mapping

* convert-llama-hf-to-gguf.py : fixes

* convert-llama-7b-pth-to-gguf.py : fixes

* ggml.h : reverse GGUF_MAGIC

* gguf.py : reverse GGUF_MAGIC

* test-tokenizer-0.cpp : fix warning

* llama.cpp : print kv general.name

* llama.cpp : get special token kv and linefeed token id

* llama : print number of tensors per type + print arch + style

* tests : update vocab file with new magic

* editorconfig : fix whitespaces

* llama : re-order functions

* llama : remove C++ API + reorganize common source in /common dir

* llama : minor API updates

* llama : avoid hardcoded special tokens

* llama : fix MPI build

ggml-ci

* llama : introduce enum llama_vocab_type + remove hardcoded string constants

* convert-falcon-hf-to-gguf.py : falcon HF --> gguf conversion, not tested

* falcon-main.cpp : falcon inference example

* convert-falcon-hf-to-gguf.py : remove extra kv

* convert-gptneox-hf-to-gguf.py : remove extra kv

* convert-llama-7b-pth-to-gguf.py : remove extra kv

* convert-llama-hf-to-gguf.py : remove extra kv

* gguf.py : fix for falcon 40b

* falcon-main.cpp : fix for falcon 40b

* convert-falcon-hf-to-gguf.py : update ref

* convert-falcon-hf-to-gguf.py : add tensor data layout

* cmpnct_gpt2bpe.hpp : fixes

* falcon-main.cpp : fixes

* gptneox-main.cpp : fixes

* cmpnct_gpt2bpe.hpp : remove non-general stuff

* Update examples/server/README.md

Co-authored-by: slaren <slarengh@gmail.com>

* cmpnct_gpt2bpe.hpp : cleanup

* convert-llama-hf-to-gguf.py : special tokens

* convert-llama-7b-pth-to-gguf.py : special tokens

* convert-permute-debug.py : permute debug print

* convert-permute-debug-master.py : permute debug for master

* convert-permute-debug.py : change permute type of attn_q

* convert.py : 70b model working (change attn_q permute)

* Delete convert-permute-debug-master.py

* Delete convert-permute-debug.py

* convert-llama-hf-to-gguf.py : fix attn_q permute

* gguf.py : fix rope scale kv

* convert-llama-hf-to-gguf.py : rope scale and added tokens

* convert-llama-7b-pth-to-gguf.py : rope scale and added tokens

* llama.cpp : use rope scale kv

* convert-llama-7b-pth-to-gguf.py : rope scale fix

* convert-llama-hf-to-gguf.py : rope scale fix

* py : fix whitespace

* gguf : add Python script to convert GGMLv3 LLaMA models to GGUF (#2682)

* First pass at converting GGMLv3 LLaMA models to GGUF

* Cleanups, better output during conversion

* Fix vocab space conversion logic

* More vocab conversion fixes

* Add description to converted GGUF files

* Improve help text, expand warning

* Allow specifying name and description for output GGUF

* Allow overriding vocab and hyperparams from original model metadata

* Use correct params override var name

* Fix wrong type size for Q8_K

Better handling of original style metadata

* Set default value for gguf add_tensor raw_shape KW arg

* llama : improve token type support (#2668)

* Merge tokenizer fixes into the gguf branch.

* Add test vocabularies

* Adapt convert-new.py (and fix a clang-cl compiler error on windows)

* Improved tokenizer test

But does it work on MacOS?

* Improve token type support

- Added @klosax code to convert.py
- Improved token type support in vocabulary

* Exclude platform dependent tests

* More sentencepiece compatibility by eliminating magic numbers

* Restored accidentally removed comment

* llama : add API for token type

ggml-ci

* tests : use new tokenizer type API (#2692)

* Merge tokenizer fixes into the gguf branch.

* Add test vocabularies

* Adapt convert-new.py (and fix a clang-cl compiler error on windows)

* Improved tokenizer test

But does it work on MacOS?

* Improve token type support

- Added @klosax code to convert.py
- Improved token type support in vocabulary

* Exclude platform dependent tests

* More sentencepiece compatibility by eliminating magic numbers

* Restored accidentally removed comment

* Improve commentary

* Use token type API in test-tokenizer-1.cpp

* py : cosmetics

* readme : add notice about new file format

ggml-ci

---------

Co-authored-by: M. Yusuf Sarıgöz <yusufsarigoz@gmail.com>
Co-authored-by: klosax <131523366+klosax@users.noreply.github.com>
Co-authored-by: goerch <jhr.walter@t-online.de>
Co-authored-by: slaren <slarengh@gmail.com>
Co-authored-by: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com>
2023-08-21 23:07:43 +03:00
Kawrakow
cb1c0727bd
HellaSwag: split token evaluation into batches if needed (#2681)
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2023-08-21 11:11:31 +03:00
Kawrakow
5e9ff54a67
More efficient Hellaswag implementation (#2677)
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2023-08-20 16:44:46 +03:00
Georgi Gerganov
1f0bccb279
server : better default prompt (#2646) 2023-08-19 05:45:36 +08:00
Jhen-Jie Hong
f63564adfa
server : update xxd usage for older versions compatibility (#2649)
* server : update xxd usage for older versions compatibility

* remove unused $func
2023-08-19 05:41:32 +08:00
slaren
097e121e2f
llama : add benchmark example (#2626)
* 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>
2023-08-18 12:44:58 +02:00
Georgi Gerganov
e9b12c332e
perplexity : more meaningful ETA number - 2 decimal points 2023-08-18 12:48:55 +03:00
staviq
10151bee2e
server : support for saving templates in browser LocalStorage (#2486)
* support for templates in browser LocalStorage

* sync accepted #2409 fix from upstream

* convert autosave invocation to useEffect

* Apply suggestions from code review

Co-authored-by: Jhen-Jie Hong <iainst0409@gmail.com>

* Regen index.html.cpp, suggested from code review

---------

Co-authored-by: Jhen-Jie Hong <iainst0409@gmail.com>
2023-08-18 07:34:01 +08:00
Kerfuffle
8dae7ce684
Add --cfg-negative-prompt-file option for examples (#2591)
Add --cfg-negative-prompt-file option for examples
2023-08-17 07:29:44 -06:00
Jhen-Jie Hong
3ebb00935f
server : add missing /json-schema-to-grammar.mjs (#2616)
fixes #2611
2023-08-15 06:14:14 +08:00
Cheng Shao
d75561df20
server : add --numa support (#2524) 2023-08-14 16:36:42 +03:00
Jhen-Jie Hong
2feb8934eb
server : fix default grammar by use empty string in the UI (#2604) 2023-08-14 16:20:17 +08:00
Jhen-Jie Hong
5517d6e692
server : implement json-schema-to-grammar.mjs & add grammar param in the UI (#2588)
* server : implement json-schema-to-grammar.mjs by follow python impl

* server : add grammar support in chat.mjs

* server : implement grammer param in the UI

* server : generate .hpp

* server : remove trailing whitespaces

* server : generate .hpp

* server : fix sort of prop pairs

* server : optimize regex & iteration
2023-08-14 15:16:54 +08:00
byte-6174
b19edd54d5
Adding support for llama2.c models (#2559) 2023-08-12 01:17:25 +02:00
Equim
53dc399472
server: fixed wrong variable name in timing json (#2579)
* server: fixed wrong variable name in timing json

* remove redunct entry
2023-08-12 00:35:14 +02:00
DannyDaemonic
9ca4abed89
Handle ENABLE_VIRTUAL_TERMINAL_PROCESSING more gracefully on earlier versions of Windows. 2023-08-10 13:11:36 -07:00
Christian Demsar
e59fcb2bc1
Add --n-predict -2 for stopping generation on full context (#2565) 2023-08-10 16:28:27 +02:00
Martin Krasser
1638757767
Fix grammar-based sampling issue in server (#2566) 2023-08-10 13:16:38 +03:00
Martin Krasser
f5bfea0580
Allow passing grammar to completion endpoint (#2532)
* Allow passing grammar to completion endpoint
2023-08-08 16:29:19 +03:00
chaihahaha
7ed8d1fe7f
llm.vim : multiline autocompletion, get rid of "^@" (#2543) 2023-08-08 15:07:02 +03:00
Georgi Gerganov
e7f94d6fdc
vim : bring back simple llm.vim example 2023-08-08 15:06:18 +03:00
AustinMroz
2d7baaf50f
vim : streaming and more (#2495)
* Update Vim plugin

* Remove getbufoneline usage, Add input bind example.

getbufoneline() appears to be a recently added function and has been
replaced with getbufline for compatibility.

An additional example that explains how to add a keybind that works in
insert mode was added.
2023-08-08 14:44:48 +03:00
klosax
f3c3b4b167
Add --rope-scale parameter (#2544)
* common.cpp : Add --rope-scale parameter
* README.md : Add info about using linear rope scaling
2023-08-07 19:07:19 +02:00
DannyDaemonic
86c3219895
console : fix issue related to Windows 11 PowerShell console mode persistence (#2521) 2023-08-06 09:49:34 +03:00
Jonas Wunderlich
332311234a
fix firefox autoscroll (#2519) 2023-08-04 22:16:11 +02:00
Cebtenzzre
182af739c4
server: regenerate completion.js.hpp (#2515) 2023-08-04 21:00:57 +02:00
DannyDaemonic
3498588e0f
Add --simple-io option for subprocesses and break out console.h and cpp (#1558) 2023-08-04 08:20:12 -07:00
Stephen Nichols
5f631c2679
Fixing race condition in server and partial stream handling in frontend. (#2391)
* Fixing race condition in server.cpp and partial stream handling in completion.js

* Reverting assert edits.

* Adding newline to eof
2023-08-04 13:37:24 +02:00
Borislav Stanimirov
ff966e7ca6
build : fix several cast and printf warnings (#2499) 2023-08-04 13:07:21 +03:00
Evan Jones
8183159cf3
examples : generate JSON according to schema (#1887)
* examples : add JSON schema grammars

* complete JSON grammar

* ensure primitive types can be used as root of schema

* support integer type and adjust usage text
2023-08-02 22:05:44 -04:00
Eve
81844fbcfd
tests : Fix compilation warnings (Linux/GCC) (#2451)
* fix hellaswag print format, cast away warning in test-double-float

* c++11 cannot use designated initializers

* add static to test-grad0.c internal functions

* use memcpy in test-double-float.c

* port c tests to c++

* use initializer list for ggml_init_params
2023-08-02 11:06:19 +03:00
Bono Lv
c574bddb36
fix a typo in examples/server/README.md (#2478) 2023-08-01 14:54:28 +02:00
ebraminio
86aeb27734
server : Support dark mode (#2414)
* server : Support dark mode

So it respects user system light / dark settings.

* Update index.html.hpp by running ./deps.sh
2023-08-01 10:56:23 +02:00
Johannes Gäßler
0728c5a8b9
CUDA: mmq CLI option, fixed mmq build issues (#2453) 2023-07-31 15:44:35 +02:00
klosax
8a88e5855c
perplexity : add Hellaswag calculation (#2389)
* common.h : add hellaswag / remove perplexity-lines

* common.cpp : add hellaswag / remove perplexity-lines

* perplexity.cpp : add hellswag scores / remove perplexity-lines

* perplexity.cpp : clean up

* common.h : change default param value

* common.cpp : Change default param

* perplexity.cpp : alter wording

* common.h : alter wording

* common.cpp : alter wording
2023-07-28 21:25:36 +03:00
Georgi Gerganov
d73b8d48b4
examples : fix whitespace 2023-07-28 21:05:08 +03:00
nhamanasu
34ae1caf7f
examples : server chat mode with llama2 (#2400)
* add: server chat mode with llama2

* fix: remove the unnecessary last \n
2023-07-28 21:02:10 +03:00
Weird Constructor
d91f3f0c55
readme : fix the description of the Tail free sampling (TFS) method (#2431) 2023-07-28 11:44:43 +03:00
Rand Xie
65cdf34bdc
llama : use n_embd_gqa instead of n_embd to handle llama-2 70B (#2433) 2023-07-28 11:42:53 +03:00
Kawrakow
eb542d3932
Add LLAMA_DEFAULT_RMS_EPS so we can change the default (#2384)
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2023-07-25 18:35:53 +03:00
Xiao-Yong Jin
0c06204fb3
main : add --in-prefix-bos to prefix BOS to user inputs; keep EOS (#2304)
* add `--in-prefix-bos` to prefix BOS to user inputs; keep EOS

The BOS precedes the string specified by `--in-prefix`.
Model generated EOS is now kept in the context.

It provides a way to strictly following the prompt format used in
Llama-2-chat.

The EOS handling also benefits some existing finetunes that uses
EOS to mark the end of turn.

* examples/common: move input_prefix_bos to other bools
2023-07-25 15:19:11 +03:00
slaren
d5512b782b
server: add rms_norm_eps parameter (#2380) 2023-07-25 12:36:17 +03:00
Henri Vasserman
c798308e3a
[Server] Escape HTML in webchat (#2368)
* escape HTML in webchat
* add amp
2023-07-25 10:27:34 +03:00
slaren
41c674161f
make rms_norm_eps a parameter (#2374)
* 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
2023-07-24 17:57:12 +02:00
Aarni Koskela
b3f138d058
Chat UI extras (#2366)
* makefile: correct deps for server

* server: tighten settings layout a little

* server: expose all currently configured generation params in UI

* server: expose remaining generation params, for the adventurous

* server: embetter mirostat fields
2023-07-24 17:54:22 +03:00
Evan Jones
84e09a7d8b
llama : add grammar-based sampling (#1773)
* 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>
2023-07-23 23:58:10 -04:00
IgnacioFDM
4f06592cc6
Add gqa parameter support to the server (#2351)
* Add gqa parameter support to the server
* Change help from stderr to stdout
2023-07-23 23:31:17 +03:00
wzy
57921ca6db
common : n_threads == -1 uses std:🧵:hardware_concurrency() (#2347)
* Fix #2345, fix incorrect n_threads

* Update examples/common.cpp

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-07-23 16:33:02 +03:00
Georgi Gerganov
e76d630df1
llama : grouped-query attention + LLaMAv2 70B support (#2276)
* CUDA: GQA implementation

* llama : support for GQA and LLaMAv2 70B

ggml-ci

* py : fix hparams parsing (if-else blocks)

ggml-ci

* py : oh boy ..

ggml-ci

* help : fix gqa value for 70B

ggml-ci

---------

Co-authored-by: JohannesGaessler <johannesg@5d6.de>
2023-07-23 15:09:47 +03:00
maddes8cht
1d0824b247
llama : print help to stdout (#2338) 2023-07-23 14:59:48 +03:00
AustinMroz
355c80f49e
examples : simplify vim plugin (#2327)
Uses builtin json_encode and json_decode functions to simplify escaping
Removes the need for temp files
2023-07-23 14:16:48 +03:00
Georgi Gerganov
b47b8a9cfe
llama : optimize memory buffers (#2325) 2023-07-22 21:17:57 +03:00
klosax
b5fe67f8c6
Perplexity: Compute scores correlated to HellaSwag (#2312)
* Add parameter --perplexity-lines to perplexity.cpp
2023-07-22 14:21:24 +02:00
whoreson
24baa54ac1
examples : basic VIM plugin
VIM plugin for server exe
2023-07-22 13:34:51 +03:00
Richard Roberson
7d5f18468c
examples : add easy python script to create quantized (k-bit support) GGML models from local HF Transformer models (#2311)
* Resync my fork with new llama.cpp commits

* examples : rename to use dash instead of underscore

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-07-21 22:01:10 +03:00
Ikko Eltociear Ashimine
03e566977b
examples : fix typo in minigpt4.py (#2298)
promt -> prompt
2023-07-21 14:53:07 +03:00
Georgi Gerganov
513f861953
ggml : fix rope args order + assert (#2054) 2023-07-21 14:51:34 +03:00
Guillaume "Vermeille" Sanchez
ab0e26bdfb
llama : remove cfg smooth factor as it is only a reparameterization of the guidance scale (#2280) 2023-07-21 13:58:36 +03:00
Jose Maldonado
73643f5fb1
gitignore : changes for Poetry users + chat examples (#2284)
A fix in Makefile for FreeBSD users. In the platfrom x86_64 is amd64. This fix resolve compilation using CFLAGS and CXXFLAGS with -march=native and -mtune=native
Add two examples for interactive mode using Llama2 models (thx TheBloke for models)

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-07-21 13:53:27 +03:00
Georgi Gerganov
ae178ab46b
llama : make tensor_split ptr instead of array (#2272) 2023-07-21 13:10:51 +03:00
Hatsune Miku
019fe257bb
MIKU MAYHEM: Upgrading the Default Model for Maximum Fun 🎉 (#2287)
* Miku.sh: Set default model to llama-2-7b-chat

* Miku.sh: Set ctx_size to 4096

* Miku.sh: Add in-prefix/in-suffix opts

* Miku.sh: Switch sampler to mirostat_v2 and tiny prompt improvements
2023-07-21 11:13:18 +03:00
Przemysław Pawełczyk
9cf022a188
make : fix embdinput library and server examples building on MSYS2 (#2235)
* make : fix embdinput library and server examples building on MSYS2

* cmake : fix server example building on MSYS2
2023-07-21 10:42:21 +03:00
wzy
b1f4290953
cmake : install targets (#2256)
fix #2252
2023-07-19 10:01:11 +03:00
Georgi Gerganov
d01bccde9f
ci : integrate with ggml-org/ci (#2250)
* 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
2023-07-18 14:24:43 +03:00
Georgi Gerganov
6cbf9dfb32
llama : shorten quantization descriptions 2023-07-18 11:50:49 +03:00
Xiao-Yong Jin
6e7cca4047
llama : add custom RoPE (#2054)
* 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/bloc97
   https://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>
2023-07-15 13:34:16 +03:00
Shangning Xu
c48c525f87
examples : fixed path typos in embd-input (#2214) 2023-07-14 21:40:05 +03:00
Howard Su
32c5411631
Revert "Support using mmap when applying LoRA (#2095)" (#2206)
Has perf regression when mlock is used.

This reverts commit 2347463201.
2023-07-13 21:58:25 +08:00
Spencer Sutton
5bf2a27718
ggml : remove src0 and src1 from ggml_tensor and rename opt to src (#2178)
* Add ggml changes

* Update train-text-from-scratch for change

* mpi : adapt to new ggml_tensor->src

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-07-11 19:31:10 +03:00
Bach Le
c9c74b4e3f
llama : add classifier-free guidance (#2135)
* 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
2023-07-11 19:18:43 +03:00
Howard Su
2347463201
Support using mmap when applying LoRA (#2095)
* Support using mmap when applying LoRA

* Fix Linux

* Update comment to reflect the support lora with mmap
2023-07-11 22:37:01 +08:00
Evan Miller
5656d10599
mpi : add support for distributed inference via MPI (#2099)
* 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>
2023-07-10 18:49:56 +03:00
Nigel Bosch
db4047ad5c
main : escape prompt prefix/suffix (#2151) 2023-07-09 11:56:18 +03:00
Qingyou Meng
1d656d6360
ggml : change ggml_graph_compute() API to not require context (#1999)
* 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>
2023-07-07 19:24:01 +03:00
Judd
36680f6e40
convert : update for baichuan (#2081)
1. guess n_layers;
2. relax warnings on context size;
3. add a note that its derivations are also supported.

Co-authored-by: Judd <foldl@boxvest.com>
2023-07-06 19:23:49 +03:00
tslmy
a17a2683d8
alpaca.sh : update model file name (#2074)
The original file name, `ggml-alpaca-7b-q4.bin`, implied the first-generation GGML. After the breaking changes (mentioned in https://github.com/ggerganov/llama.cpp/issues/382), `llama.cpp` requires GGML V3 now. Those model files are named `*ggmlv3*.bin`. We should change the example to an actually working model file, so that this thing is more likely to run out-of-the-box for more people, and less people would waste time downloading the old Alpaca model.
2023-07-06 19:17:50 +03:00
Tobias Lütke
31cfbb1013
Expose generation timings from server & update completions.js (#2116)
* 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>
2023-07-05 16:51:13 -04:00
Jesse Jojo Johnson
983b555e9d
Update Server Instructions (#2113)
* Update server instructions for web front end
* Update server README
* Remove duplicate OAI instructions
* Fix duplicate text

---------

Co-authored-by: Jesse Johnson <thatguy@jessejojojohnson.com>
2023-07-05 21:03:19 +03:00
Stephan Walter
1b107b8550
ggml : generalize quantize_fns for simpler FP16 handling (#1237)
* 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>
2023-07-05 19:13:06 +03:00
Jesse Jojo Johnson
8567c76b53
Update server instructions for web front end (#2103)
Co-authored-by: Jesse Johnson <thatguy@jessejojojohnson.com>
2023-07-05 18:13:35 +03:00
Nigel Bosch
7f0e9a775e
embd-input: Fix input embedding example unsigned int seed (#2105) 2023-07-05 07:33:33 +08:00
jwj7140
f257fd2550
Add an API example using server.cpp similar to OAI. (#2009)
* add api_like_OAI.py
* add evaluated token count to server
* add /v1/ endpoints binding
2023-07-04 21:06:12 +03:00
Tobias Lütke
7ee76e45af
Simple webchat for server (#1998)
* expose simple web interface on root domain

* embed index and add --path for choosing static dir

* allow server to multithread

because web browsers send a lot of garbage requests we want the server
to multithread when serving 404s for favicon's etc. To avoid blowing up
llama we just take a mutex when it's invoked.


* let's try this with the xxd tool instead and see if msvc is happier with that

* enable server in Makefiles

* add /completion.js file to make it easy to use the server from js

* slightly nicer css

* rework state management into session, expose historyTemplate to settings

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-07-04 16:05:27 +02:00
Henri Vasserman
1cf14ccef1
fix server crashes (#2076) 2023-07-04 00:05:23 +03:00
WangHaoranRobin
d7d2e6a0f0
server: add option to output probabilities for completion (#1962)
* server: add option to output probabilities for completion
* server: fix issue when handling probability output for incomplete tokens for multibyte character generation
* server: fix llama_sample_top_k order
* examples/common.h: put all bool variables in gpt_params together
2023-07-03 00:38:44 +03:00
Georgi Gerganov
79f634a19d
embd-input : fix returning ptr to temporary 2023-07-01 18:46:00 +03:00
Georgi Gerganov
04606a1599
train : fix compile warning 2023-07-01 18:45:44 +03:00
Howard Su
b8c8dda75f
Use unsigned for random seed (#2006)
* Use unsigned for random seed. Keep -1 as the value to use a time based seed.

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-06-29 06:15:15 -07:00
Johannes Gäßler
7f9753fa12
CUDA GPU acceleration for LoRAs + f16 models (#1970) 2023-06-28 18:35:54 +02:00
ningshanwutuobang
cfa0750bc9
llama : support input embeddings directly (#1910)
* 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
2023-06-28 18:53:37 +03:00
Howard Su
0be54f75a6
baby-llama : fix build after ggml_rope change (#2016) 2023-06-27 08:07:13 +03:00
Georgi Gerganov
181e8d9755
llama : fix rope usage after ChatGLM change 2023-06-27 00:37:33 +03:00
David Yang
eaa6ca5a61
ggml : increase max tensor name + clean up compiler warnings in train-text (#1988)
* Clean up compiler warnings in train-text

Some brackets to disambiguate order of operations

* Increase GGML_MAX_NAME

Avoiding strncpy danger in train-text-from-scratch and reducing potential future name length issues
2023-06-26 22:45:32 +03:00
zrm
b853d45601
ggml : add NUMA support (#1556)
* 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>
2023-06-26 20:57:59 +03:00
anon998
c2a08f87b8
fix server sampling: top k sampler first (#1977)
Co-authored-by: anon <anon@example.org>
2023-06-25 10:48:36 +02:00
Didzis Gosko
527b6fba1d
llama : make model stateless and context stateful (llama_state) (#1797)
* 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>
2023-06-24 11:47:58 +03:00
Henri Vasserman
20568fe60f
[Fix] Reenable server embedding endpoint (#1937)
* Add back embedding feature

* Update README
2023-06-20 01:12:39 +03:00
Kawrakow
90cc59d6ab
examples : fix examples/metal (#1920)
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2023-06-18 10:52:10 +03:00
Georgi Gerganov
4f9c43e3bd
minor : warning fixes 2023-06-17 20:24:11 +03:00
Johannes Gäßler
2c9380dd2f
Only one CUDA stream per device for async compute (#1898) 2023-06-17 19:15:02 +02:00
Georgi Gerganov
051e1b0e6a
llama : fix kv_cache n init (close #1903) 2023-06-17 19:31:20 +03:00
Randall Fitzgerald
794db3e7b9
Server Example Refactor and Improvements (#1570)
A major rewrite for the server example.

Note that if you have built something on the previous server API, it will probably be incompatible.
Check out the examples for how a typical chat app could work.

This took a lot of effort, there are 24 PR's closed in the submitter's repo alone, over 160 commits and a lot of comments and testing.

Summary of the changes:

- adds missing generation parameters: tfs_z, typical_p, repeat_last_n, repeat_penalty, presence_penalty, frequency_penalty, mirostat, penalize_nl, seed, ignore_eos
- applies missing top k sampler
- removes interactive mode/terminal-like behavior, removes exclude parameter
- moves threads and batch size to server command-line parameters
- adds LoRA loading and matches command line parameters with main example
- fixes stopping on EOS token and with the specified token amount with n_predict 
- adds server timeouts, host, and port settings
- adds expanded generation complete response; adds generation settings, stop reason, prompt truncated, model used, and final text
- sets defaults for unspecified parameters between requests
- removes /next-token endpoint and as_loop parameter, adds stream parameter and server-sent events for streaming
- adds CORS headers to responses
- adds request logging, exception printing and optional verbose logging
- adds better stopping words handling when matching multiple tokens and while streaming, or when it finishes on a partial stop string
- adds printing an error when it can't bind to the host/port specified
- fixes multi-byte character handling and replaces invalid UTF-8 characters on responses
- prints timing and build info on startup
- adds logit bias to request parameters
- removes embedding mode
- updates documentation; adds streaming Node.js and Bash examples
- fixes code formatting
- sets server threads to 1 since the current global state doesn't work well with simultaneous requests
- adds truncation of the input prompt and better context reset
- removes token limit from the input prompt
- significantly simplified the logic and removed a lot of variables

---------

Co-authored-by: anon998 <131767832+anon998@users.noreply.github.com>
Co-authored-by: Henri Vasserman <henv@hot.ee>
Co-authored-by: Felix Hellmann <privat@cirk2.de>
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
Co-authored-by: Lesaun Harvey <Lesaun@gmail.com>
2023-06-17 14:53:04 +03:00
Jiří Podivín
5ddf7ea1fb
hooks : setting up flake8 and pre-commit hooks (#1681)
Small, non-functional changes were made to non-compliant files.
These include breaking up long lines, whitespace sanitation and
unused import removal.

Maximum line length in python files was set to a generous 125 chars,
in order to minimize number of changes needed in scripts and general
annoyance. The "txt" prompts directory is excluded from the checks
as it may contain oddly formatted files and strings for a good reason.

Signed-off-by: Jiri Podivin <jpodivin@gmail.com>
2023-06-17 13:32:48 +03:00
David Yang
92f20d9942
train : get raw text instead of page with html (#1905)
We probably want to train using just the text of Shakespeare instead of the html of the page displaying his work.
2023-06-17 09:51:54 +03:00
SuperUserNameMan
b41b4cad6f
examples : add "simple" (#1840)
* Create `simple.cpp`

* minimalist example `CMakeLists.txt`

* Update Makefile for minimalist example

* remove 273: Trailing whitespace

* removed trailing white spaces simple.cpp

* typo and comments simple.cpp

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-06-16 21:58:09 +03:00
FrankHB
5b9ccaf104
Fixed possible macro redefinition (#1892)
MinGW libstdc++ may define `NOMINMAX` unconditionally. This fixes the case when it is already defined.
2023-06-16 21:25:01 +03:00
Borislav Stanimirov
9cbf50c041
build : fix and ignore MSVC warnings (#1889) 2023-06-16 21:23:53 +03:00
yangli2
c36e81da62
examples : add chat-vicuna.sh (#1854)
Co-authored-by: Yang Li <yangliyl@google.com>
2023-06-15 21:05:53 +03:00
Srinivas Billa
9dda13e5e1
readme : server compile flag (#1874)
Explicitly include the server make instructions for C++ noobsl like me ;)
2023-06-15 20:36:38 +03:00
Johannes Gäßler
6b8312e797
Better error when using both LoRA + GPU layers (#1861) 2023-06-15 19:06:46 +02:00
Johannes Gäßler
254a7a7a5f
CUDA full GPU acceleration, KV cache in VRAM (#1827)
* 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
2023-06-14 19:47:19 +02:00
0xspringtime
9254920265
baby-llama : fix operator!= (#1821)
* Update baby-llama.cpp

Seems to be an error in the implementation of the operator!= function. It attempts to compare the this pointer (a llama_hparams_lora object) with the other pointer (a llama_hparams object) using memcmp. This can lead to incorrect results because the sizes of the objects being compared (sizeof(llama_hparams) and sizeof(llama_hparams_lora)) are different, should now be able to compare two llama_hparams_lora objects for inequality.

* Update baby-llama.cpp

* Update baby-llama.cpp
2023-06-13 22:37:54 +03:00
xaedes
e32089b2c2
train : improved training-from-scratch example (#1652)
* 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>
2023-06-13 22:04:40 +03:00
Georgi Gerganov
2347e45e7b
llama : do a warm-up eval at start for better timings (#1824) 2023-06-13 20:20:07 +03:00
Kerfuffle
74d4cfa343
Allow "quantizing" to f16 and f32 (#1787)
* 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
2023-06-13 04:23:23 -06:00
Kerfuffle
fa84c4b3e8
Fix issue where interactive mode crashes when input exceeds ctx size (#1789)
* Fix issue where interactive mode in the main example crashes when input exceeds ctx size

* Ensure the context size is at least 8 tokens in the main example.

Closes #1768
2023-06-11 08:19:17 -06:00
Kerfuffle
4f0154b0ba
llama : support requantizing models instead of only allowing quantization 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.
2023-06-10 10:59:17 +03:00
Willy Tarreau
35a84916fb
main: add the possibility to open the prompt cache read-only (#1640)
The prompt cache constitutes a nice speed up when using the same prompt
prefix across multiple evaluations, but when using it, it will also be
updated, which is not always desirable. One use case is to have a large
prompt containing some context and usage rules, and a second part
containing variable data of the problem being studied. In this case it's
desirable to be able to save the first part once, and to always reuse it
as-is without updating it with the second part.

The new argument --prompt-cache-ro enables this read-only mode on the
prompt cache. The prompt's contents that match the cache are loaded
from the cache but the rest is not modified. This allowed to reduce a
total analysis time from 112s to 49.7s here, without having to backup
and restore a copy of the prompt, which takes significant time at 500
MB.

Signed-off-by: Willy Tarreau <w@1wt.eu>
2023-06-06 22:10:17 -04:00
Johannes Gäßler
17366df842
Multi GPU support, CUDA refactor, CUDA scratch buffer (#1703)
* 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
2023-06-06 21:33:23 +02:00
Kawrakow
99009e72f8
ggml : add SOTA 2,3,4,5,6 bit k-quantizations (#1684)
* 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>
2023-06-05 22:56:18 +03:00
Georgi Gerganov
ecb217db4f
llama : Metal inference (#1642)
* 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 98c267fc77.

* metal : add comments

* metal : clean-up stuff, fix typos

* readme : add Metal instructions

* readme : add example for main
2023-06-04 23:34:30 +03:00