Train mem usage and other improvements #2439

xaedes · 2023-07-28T22:20:48Z

This PR improves the memory requirements of training by removing unnecessary tensors from adam optimizer and with a (manual) gradient checkpointing training function, which reduces memory overhead from O(n_layer) to O(sqrt(n_layer)) as explained in readme of https://github.com/cybertronai/gradient-checkpointing

Other changes:

Adam(W) optimizer now supports gradient clipping which improves training convergence
optimizers can now be passed a callback, which is called before each optimization iteration. This can be used to set a learning rate based on custom learning schedule and change sample batches between each iteration, which improves training convergence and run time by avoiding the overhead of restarting the optimization loop to update learning rate and sample batches
fixed some issues in cross entropy loss and improved numerical stability of backward pass with a simplified computation (as in other frameworks), assuming target probability vector sums to one for each batch
cross entropy loss now returns the mean loss over all batches, which helps keeping the gradients in a sane range and decouples the gradients from the batch size
change AdamW decay parameter to work like the torch AdamW decay param, and change default AdamW weight decay parameter defined in ggml to 0.0, making Adam default instead of AdamW
add conditional compilation for F16 exp in flash attention and cross entropy loss, improving gradient quality
ggml : update ggml_rms_norm_back with configurable eps
ggml : add 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);
expose more optimizer parameters as training parameters
change sampling parameters for prediction after training to defaults of common.h and clarify what is context for prediction and what are generated tokens

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.

reduces memory overhead from O(n_layer) to O(sqrt(n_layer)) as explained in readme of https://github.com/cybertronai/gradient-checkpointing

…ows 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);

…eter 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]

…s used in nanoGPT

…aking Adam default instead of AdamW btw: the default weight decay parameter for torch.optim.AdamW is 0.01

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.

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

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)

…loat

this helps keeping the loss and gradients in a sane range

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.

--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

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

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

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

…t 2) this allows to not apply weight decay to bias parameters

…y that adam-min-alpha also applies to warmup

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

…of common.h and clarify what is context for prediction and what are generated tokens

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

… them

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.

…tness

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

… with opt_version=0

use main for prediction, it is better optimized

xaedes · 2023-08-28T17:23:06Z

Implemented GGUF checkpoint files and writing of GGUF models.
Use convert-train-checkpoint-to-gguf.py to convert old checkpoint files into GGUF format.

Fixes a bug that resulted in bad gradients during training.

This also fixes a memory-corruption bug when attempting to write any GGUF file from cpp:

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.

All the prediction code is removed from train-text-from-scratch to reduce code duplication with main.
Use main for prediction instead.

ggml.c

…uf ctx->kv & ctx->infos

ggerganov

Brb, training a tiny shakespeare llama 😄

* 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 <[email protected]>

xaedes added 30 commits July 28, 2023 21:17

fix track_max_mem in forward_batch_wo_cache_flash_attn_train

5d124d0

add gradient clipping to AdamW

d395b19

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

d7003a9

implement gradient checkpointing for training

6e3f95b

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

e05e441

add and use function ggml_build_backward_expand to avoid stack overfl…

ed4319e

…ows 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 param…

a80f184

…eter 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 a…

f175ead

…s used in nanoGPT

change default AdamW weight decay parameter defined in ggml to 0.0, m…

97964a4

…aking Adam default instead of AdamW btw: the default weight decay parameter for torch.optim.AdamW is 0.01

fix test-grad0 for cross_entropy_loss

2d1e6e0

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

fix test-grad0 for soft_max

864e7e3

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 f…

87febee

…loat

change cross_entropy_loss to output average over all rows

51dc770

this helps keeping the loss and gradients in a sane range

disable gradient checkpointing debug output

fc379a2

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

d0fbb7d

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

ce937bc

… 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

ff759d9

measure and print total training time

e843d6e

add optimization callback to ggml_opt_resume_g

bfc3119

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

add minimum number of tensor dimensions to apply weight decay (defaul…

e6ff072

…t 2) this allows to not apply weight decay to bias parameters

rename training parameter cos-decay-alpha to cos-decay-min and clarif…

58024d3

…y that adam-min-alpha also applies to warmup

fix increase of model.train_samples and model.train_tokens

17a0898

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 …

24a4b09

…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

1065c3b

add conditional compilation of using F16 exp in flash attention

dbbc263

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

xaedes added 23 commits August 27, 2023 23:32

use same GGUF_GET_KEY macro as in llama.cpp

91a4cca

use norm_rms_eps, and rope parameters and command line options to set…

0b2c85b

… them

add gguf example cmake file

5d94997

bug fixes in tokenize_file

76d2794

bug fixes in load_llama_model_gguf

4882ff0

bug fix: init model when no checkpoint was loaded

152cfaa

bug fix in read_tensor_by_name

1f83343

bug fix in load_opt_context_gguf

3d8d884

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

e86b3e3

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

daa0b6c

remove trailing whitespace

f97f92b

print data checksums before saving and after loading to verify correc…

c690c20

…tness

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

5f27ade

temporarily add code to write old checkpoint files

e8df9e6

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

bug fixes for convert-train-checkpoint-to-gguf.py loading checkpoints…

31c093c

… with opt_version=0

remove code used to verify correctness of checkpoint file conversion

63bf200

remove trailing whitespace

3155019

remove prediction related code

3e7dfd0

use main for prediction, it is better optimized

update train-text-from-scratch README.md

17ab46d

Merge branch 'master' into pr-train-mem-usage-improvements

12c4e5b

fix non-windows GGML_ALIGNED_REALLOC

a925e93

add missing blank line at end of file

440d221

ggerganov reviewed Aug 28, 2023

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xaedes and others added 2 commits August 28, 2023 20:21

remove GGML_ALIGNED_REALLOC and use normal malloc/realloc/free for gg…

f6828cb

…uf ctx->kv & ctx->infos

train : fix compile warnings

93535a4

ggerganov approved these changes Aug 28, 2023

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ggerganov merged commit 44c117f into ggerganov:master Aug 28, 2023

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Train mem usage and other improvements #2439

Train mem usage and other improvements #2439

xaedes commented Jul 28, 2023

xaedes commented Aug 28, 2023

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Train mem usage and other improvements #2439

Train mem usage and other improvements #2439

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