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94 Commits
| Author | SHA1 | Message | Date | |
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Georgi Gerganov
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f93af02488
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sync : ggml (conv 1d + 2d updates, UB fixes) (#3468)
* sync : ggml (conv 1d + 2d updates) ggml-ci * ggml : fix UB in q5_0 and q5_1 quantize code ggml.c:1033:39: runtime error: left shift of 1 by 31 places cannot be represented in type 'int' SUMMARY: UndefinedBehaviorSanitizer: undefined-behavior ggml.c:1081:39: runtime error: left shift of 1 by 31 places cannot be represented in type 'int' SUMMARY: UndefinedBehaviorSanitizer: undefined-behavior ggml-ci * tests : fix UB in test-quantize-perf |
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Cebtenzzre
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bc39553c90
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build : enable more non-default compiler warnings (#3200) | ||
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Hua Jiang
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0ccfc62a96
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ggml_tensor: update the structure comments. (#3283)
* ggml_tensor: update the structure comments. * remove semicolon Co-authored-by: slaren <slarengh@gmail.com> * Update ggml.h --------- Co-authored-by: Cebtenzzre <cebtenzzre@gmail.com> Co-authored-by: slaren <slarengh@gmail.com> |
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xaedes
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0e76a8992c
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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> |
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Cebtenzzre
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2db94d98ed
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gguf : basic type checking in gguf_get_* (#3346) | ||
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Georgi Gerganov
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ec893798b7
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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> |
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|
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> |
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|
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 |
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|
Eric Sommerlade
|
b52b29ab9d
|
arm64 support for windows (#3007)
Co-authored-by: Cebtenzzre <cebtenzzre@gmail.com> |
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|
slaren
|
06abf8eeba
|
ggml : add view_src and view_offs to ggml_tensor for views (#2874)
* ggml : add view_src and view_offs * update ggml-alloc to use view_src * update ggml_diag_mask to work correctly with automatic inplace * exclude other ops that set an inplace flag from automatic inplace |
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|
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> |
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|
Georgi Gerganov
|
35feac6560
|
ggml : sync (mem align to header + conv_transpose_2d fixes + ggml_alloc) (#2852)
* ggml : sync (mem align to header + conv_transpose_2d fixes) ggml-ci * ggml-alloc : minor fix * ggml-alloc : sync more fixes |
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|
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> |
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|
Przemysław Pawełczyk
|
1591e2e590
|
ggml : detect SSSE3 (#2825)
* ggml : add ggml_cpu_has_ssse3 * llama : show SSSE3 in system info |
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|
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> |
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|
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 |
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|
Kylin
|
1e3bc523d8
|
ggml : support CUDA's half type for aarch64(#1455) (#2670)
* ggml: support CUDA's half type for aarch64(#1455) support CUDA's half type for aarch64 in ggml_fp16_t definition * ggml: use __CUDACC__ to recognise nvcc compiler |
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|
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
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slaren
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9e232f0234
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ggml : move all type info to ggml_type_traits (#2663) | ||
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Georgi Gerganov
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99d29c0094
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ggml : sync (custom ops) (#2537)
ggml-ci |
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slaren
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a113689571
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ggml : add graph tensor allocator (#2411)
* ggml : add graph tensor allocator * ggml : don't calculate data pointer of unallocated tensors when creating a view with an offset * ggml : refactor ggml_view_Nd into ggml_view_tensor_offset |
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slaren
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5488fb789e
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ggml : allocate graphs in a context (#2392)
* ggml : graph allocation in contexts * allocate work buffer as a ggml_object in ggml_graph_compute_with_ctx * llama.cpp : allocate graph in the context * add GGML_PAD --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> |
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slaren
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da1889834a
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ggml : improve graph build time via hash table lookup (#2329)
* improve graph build time * ggml_tensor : use 1 bit per flag * use a hash table instead |
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slaren
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41c674161f
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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 |
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Georgi Gerganov
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5b2b2dc6ae
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ggml : sync (unary ops refactor, static-correctness) (#2370)
* ggml : sync (unary ops, tests) ggml-ci * tests : remove unnecessary funcs |
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slaren
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95a6c595e7
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ggml: move op parameters from tensors to ggml_tensor::op_params (#2333)
* ggml: move op parameters from tensors to ggml_tensor::op_params * alibi: use memcpy for float params * remove `src[1] = NULL` in ops |
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Georgi Gerganov
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513f861953
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ggml : fix rope args order + assert (#2054) | ||
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Xiao-Yong Jin
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6e7cca4047
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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> |
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Georgi Gerganov
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4523d10d0c | ggml : add ggml_pool_1d and ggml_pool_2d | ||
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Georgi Gerganov
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20d7740a9b
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ggml : sync (abort callback, mul / add broadcast, fix alibi) (#2183) | ||
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Spencer Sutton
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5bf2a27718
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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> |
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Qingyou Meng
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1d656d6360
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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> |
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Georgi Gerganov
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dfd9fce6d6
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ggml : fix restrict usage | ||
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Stephan Walter
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1b107b8550
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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> |
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Georgi Gerganov
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ed9a54e512
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ggml : sync latest (new ops, macros, refactoring) (#2106)
- add ggml_argmax() - add ggml_tanh() - add ggml_elu() - refactor ggml_conv_1d() and variants - refactor ggml_conv_2d() and variants - add helper macros to reduce code duplication in ggml.c |
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Qingyou Meng
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b1ca8f36a9
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ggml : disable GGML_TASK_INIT and GGML_TASK_FINALIZE by default (#1995)
Will not be scheduled unless explicitly enabled. |
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Georgi Gerganov
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d9779021bd
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ggml : add support for ChatGLM RoPE | ||
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David Yang
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eaa6ca5a61
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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 |
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zrm
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b853d45601
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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> |
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Georgi Gerganov
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bd34cdde38
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ggml : sync latest ggml (custom operators) | ||
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slaren
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f2c754e1c3
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ggml : improve ggml_graph_dump_dot, add ggml_format_name (#1978)
* Improve ggml_graph_dump_dot, add ggml_format_name * add more automatic names to view ops * fix name of copies |
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Georgi Gerganov
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b97ca431db
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ggml : sync latest ggml repo (#1924)
* ggml : sync latest ggml repo * ggml : remove unused comments * ggml : asserts |
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Georgi Gerganov
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ce2c7d72e2
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metal : handle buffers larger than device's maxBufferLength (#1826)
* metal : handle buffers larger than device's maxBufferLength * metal : print more verbose device info + handle errors * metal : fix prints for overlapping views * metal : minimize view overlap to try to utilize device memory better |
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Johannes Gäßler
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254a7a7a5f
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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 |
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xaedes
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e32089b2c2
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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> |
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Johannes Gäßler
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17366df842
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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 |
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Kawrakow
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99009e72f8
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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> |
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Georgi Gerganov
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ecb217db4f
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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
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Georgi Gerganov
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7552ac5863
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ggml : sync cgraph import / export API | ||
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Georgi Gerganov
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93618031c7
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ggml : add ggml_tensor_overhead() |