This commit adds support for the 18-layer model type in the Gemma3
series, which is the size of the Gemma3-270m model.
The motivation for this commit is was the only change required for
Gemma3-270m to be converted to GGUF format and used with llama.cpp.
Once the model has been converted and uploaded to Huggingface it can be
used like this:
```console
$ ./build/bin/llama-cli -hf ggml-org/gemma-3-270m-GGUF:Q8_0
```
* model : add harmony parser for gpt-oss
* gpt-oss : fix grammar trigger from causing empty stack
* gpt-oss: tweak the grammar trigger again
* gpt-oss : add support for recipient in role header
* gpt-oss : fix ungrouped tool calls in grammar
* gpt-oss : loosen function name matching during parse
* gpt-oss : clean up workarounds
* gpt-oss : add template tests
* gpt-oss : simulate thinking and tool call tags
* gpt-oss : undo think tags when reasoning_format is none
* gpt-oss : set special tokens back to user defined
* gpt-oss : update openai-gpt-oss template
* server : filter out harmony thought messages
* gpt-oss : simplify parsing
* server : add SWA checkpoints
ggml-ci
* cont : server clean-up
* server : handle state restore fails
* llama : add extended llama_state_seq_ API
* server : do not make checkpoints if --swa-full
ggml-ci
* llama : remove flags value for NONE
* server : configure number of SWA checkpoints with CLI arg
ggml-ci
* args : fix scope of new argument
When attempting to do llama-perplexity on certain tasks which have coupled sequences there is a cryptic error that does not tell you what to do, which is to set the -kvu flag. This adds a hint about that fact.
* examples/finetune -opt SGD (stochastic gradient descent) memory opt
add unit tested GGML_OPT_OPTIMIZER_SGD to ggml - avoids allocating
m, v tensors.
support finetune.cpp arg -opt SGD (or sgd). (default adamw as before)
llama 3.2-1b-F32 result: observed 11gb gpu ram (41 sec/epoch)
when using SGD instead of 19gb (55 sec/epoch) using adamw.
(wikipedia 100 lines finetune)
(
using the same GPU memory, adamw can only do before OOM 512
batch/context, reaching:
train: [███████▉] data=0000140/0000140 loss=0.02575±0.00099 acc=99.52±0.03% t=00:00:47 ETA=00:00:00
val: [███████▉] data=0000008/0000008 loss=4.76565±0.28810 acc=41.46±0.77% t=00:00:00 ETA=00:00:00
SGD is superior, though it converges slower, with max before OOM 1728
batch/context (esp see the better validation perf):
train: [███████▉] data=0000039/0000039 loss=0.00371±0.00010 acc=99.96±0.01% t=00:00:41 ETA=00:00:00
val: [███████▉] data=0000003/0000003 loss=5.11406±0.76034 acc=48.01±0.69% t=00:00:01 ETA=00:00:00
)
note: when finetuning long enough (or w/ enough -lr),
validation accuracy *eventually* drops ('catastrophic forgetting')
-lr-half (halflife) option useful for SGD to avoid oscillation or
super slow underdamped learning (makes setting -lr more forgiving).
terminal -lr for now is set by lr-halvings i.e. if you want at most
1/8 the inital -lr you set -lr-halvings 3.
note: objective loss not directly comparable between adamw, sgd? -
check perplexity or accuracy or consider relative improvements
for convergence
new finetune args -wd 1e-9 to enable weight decay in sgd or adamw,
and max -epochs N (default 2 as before)
cache (1 - wd*alpha) in 'adamw' opt struct -
no noticeable perf benefit, disabled (still done
for new SGD though)
since opt. memory is pre-allocated, the ggml_opt_get_optimizer_params
would probably be able to change between SGD and AdamW with each epoch
but would need to use adamw for the first (unconfirmed - no cmdline arg
to set such a policy yet)
test-opt checks adamw as before and now sgd (except for a few disabled
tests for sgd only; probably just needs logging values and adding
alternate reference values); tolerance on the 'regression'
test is broader for sgd (so we don't need many more epochs)
* Vulkan: Implement GGML_OP_OPT_STEP_SGD
* tests: Fix OPT_STEP_SGD test-backend-ops
* SGD op param store weight-decay and not 1-alpha*wd
* minor + cosmetic changes
* fix vulkan sgd
* try CI fix
---------
Co-authored-by: 0cc4m <picard12@live.de>
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
This commit updates `llama_kv_cache_unified::find_slot` to log
information for all streams when debug is enabled.
The motivation for this change is that currently if a non-unified
kv-cache is used, then only one stream will be logged because the
code was currently uses `seq_to_stream[1]`.
This commit removes the right alignment the `n_stream` value in the
log message in the `llama_kv_cache_unified` constructor.
The motivation for this change is to enhance the readability of log
message. Currently the output looks like this:
```console
llama_kv_cache_unified: size = 2048.00 MiB ( 4096 cells, 32 layers, 1/ 1 seqs), K (f16): 1024.00 MiB, V (f16): 1024.00 MiB
```
Notice that the `n_stream` value is right aligned, which makes it a
little harder to read.
With the change in this commit the output will look like
```console
llama_kv_cache_unified: size = 2048.00 MiB ( 4096 cells, 32 layers, 1/1 seqs), K (f16): 1024.00 MiB, V (f16): 1024.00 MiB
```
* Add support for Llada-8b: diffusion model
* Add README
* Fix README and convert_hf_to_gguf
* convert_hf_to_gguf.py: address review comments
* Make everything in a single example
* Remove model-specific sampling
* Remove unused argmax
* Remove braced initializers, improve README.md a bit
* Add diffusion specific gguf params in set_vocab, remove setting rope_theta and rms_norm_eps
* Remove adding the mask token
* Move add_add_bos_token to set_vocab
* use add_bool in gguf_writer.py
* support smallthinker
* support 20b softmax, 4b no sliding window
* new build_moe_ffn_from_probs, and can run 4b
* fix 4b rope bug
* fix python type check
* remove is_moe judge
* remove set_dense_start_swa_pattern function and modify set_swa_pattern function
* trim trailing whitespace
* remove get_vocab_base of SmallThinkerModel in convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* better whitespace
Apply suggestions from code review
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* use GGML_ASSERT for expert count validation
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Improve null pointer check for probs
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* use template parameter for SWA attention logic
* better whitespace
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* move the creation of inp_out_ids before the layer loop
* remove redundant judge for probs
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* llama : clarify comment about pp and tg graphs [no ci]
This commit clarifies the comment in `llama-context.cpp` regarding the
prefill prompt (pp), and token generation (tg) graphs.
The motivation for this is that I've struggled to remember these and had
to look them up more than once, so I thought it would be helpful to add
a comment that makes it clear what these stand for.
* squash! llama : clarify comment about pp and tg graphs [no ci]
Change "pp" to "prompt processing".
MiniCPM models use the llm_build_granite constructor which was changed
in the Granite Four PR to use hparams.rope_finetuned instead of a
use_rope parameter. MiniCPM models need rope enabled by default.
Fixes inference from gibberish to correct responses.
* Support diffusion models: Add Dream 7B
* Move diffusion to examples
* Move stuff to examples. Add patch to not use kv-cache
* Address review comments
* Make sampling fast
* llama: remove diffusion functions
* Add basic timings + cleanup
* More cleanup
* Review comments: better formating, use LOG instead std::cerr, re-use batch, use ubatch instead of max_length
* fixup!
* Review: move everything to diffusion-cli for now
* Add PLaMo-2 model using hybrid memory module
* Fix z shape
* Add cmath to include from llama-vocab.h
* Explicitly dequantize normalization weights before RoPE apply
* Revert unnecessary cast because the problem can be solved by excluding attn_k, attn_q when quantizing
* Use ATTN_K/Q_NORM for k,q weights to prevent quantization
* Remove SSM_BCDT that is not used from anywhere
* Do not duplicate embedding weights for output.weight
* Fix tokenizer encoding problem for multibyte strings
* Apply suggestion from @CISC
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-model.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Use LLM_FFN_SWIGLU instead of splitting ffn_gate and ffn_up
* Remove unnecessary part for Grouped Query Attention
* Fix how to load special token id to gguf
* Remove unused tensor mapping
* Update src/llama-model.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Remove llama_vocab_plamo2 class and replace it with llm_tokenizer_plamo2_session to follow the other tokenizer implementations
* Update src/llama-vocab.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-model.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-model.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Fix plamo2 tokenizer session to prevent multiple calls of build()
---------
Co-authored-by: Francis Couture-Harpin <git@compilade.net>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* wip: llama : separate recurrent states from the KV cache
This will be necessary to support Jamba
(and other recurrent models mixed with Attention).
Doesn't compile yet, and finding a slot isn't yet done correctly for recurrent states.
* llama : use std::find for seq_nodes in llama_rs_cache
* llama : state checkpoints for recurrent models
* llama : correctly handle more edge cases for the rs cache
* llama : rename many llama_kv_cache_* functions
* llama : remove useless return value for some llama_cache_* functions
* llama : rethink recurrent state cell counts
* llama : begin work on support for variable GQA
This will also be useful for Jamba if we consider the Mamba layers
to have 0 KV heads.
* llama : gracefully fail when not finding hybrid slot
* llama : support Jamba
* llama : fix BERT inference without KV cache
* convert-hf : check for unprocessed Jamba experts
* convert-hf : support Mini-Jamba conversion
* llama : fix Jamba quantization sanity checks
* llama : sequence-length-aware batch splitting
* llama : use equal-sequence-length sub-batches for recurrent models
* ggml : simplify SSM-related operators
* llama : make recurrent state slot allocation contiguous
* llama : adapt internal uses of batches to llama_ubatch
* llama : fix batch split output count for embeddings
* llama : minimize swaps when reordering logits
This reduces overhead when running hellaswag
on thousands of sequences with very small 100k params Mamba models.
* llama : fix edge case finding batch seq_id of split recurrent cell
This otherwise was a problem when running the HellaSwag benchmark
with small batch sizes, making it crash.
* llama : avoid copies for simple batch splits
* llama : use im2col and mul_mat to perform convolution for Mamba
This removes the need for ggml_ssm_conv!!!
But performance seems slighly worse on my system,
especially for prompt processing.
Maybe ggml_mul_mat isn't optimized for small row sizes?
More performance testing is necessary until GGML_OP_SSM_CONV is removed.
* ggml : make ggml_ssm_scan not modify its source tensors
* llama : fix shared recurrent tail cell count for small ubatch sizes
Otherwise it was impossible to run the 'parallel' example with '-ub 1'
with a Mamba or Jamba model.
* llama : fix .base() compilation error on Windows
* llama : allow doing the equivalent of SSM_CONV with SUM_ROWS and MUL
* ggml : allow GGML_OP_CONCAT to work on non-contiguous tensors
The implementation already supported it,
and this makes Mamba's conv step slightly faster.
* llama : rename llama_cache to llama_past
This can be changed back later if the name change is wrong.
I was renaming the functions anyway to generalize kv-cache-related
functions to hybrid and recurrent model architectures.
I think llama_past is a better name than llama_cache for a combined
kv cache and recurrent state cache, because the states it contains
pretty much always come before the newly-added ones for any particular
sequence. Also 'llama_past_clear' sounds more obvious in what it does
than 'llama_kv_cache_clear'. The future is what the models generate.
(For embeddings, the kv cache isn't really used anyway)
Still, I'm open to better suggestions.
* examples : replace llama_kv_cache_seq_* with llama_past_seq_*
* mamba : fix non-contiguous usage of ggml_silu
* llama : initial Mamba-2 support
* ggml : SIMD ggml_ssm_scan for Mamba-2
* ggml : improve ggml_mul speed when masking recurrent states
* llama : support running Mamba-Codestral-7B-v0.1
* llama : fix Mamba-2 conv state saving
* ggml : make the ggml_mul fast broadcast path more consistently formatted
* llama : remove unused variable
* llama : add missing break
* convert_hf : prefer SentencePiece tokenizer for Mamba-2 when present
The tokenzier.json of Mamba-Codestral-7B-v0.1 otherwise requires
workarounds to work correctly.
* llama : session saving and reloading for hybrid models
* convert_hf : fix Jamba conversion
* llama : fix mixed signedness comparison
* llama : use unused n_embd_k_gqa in k_shift
This also slightly reduces the diff from the master branch
* llama : begin renaming llama_past back to llama_kv_cache
* llama : avoid redundant state copy for Mamba 1 and 2
* metal : attempt to adapt SSM_SCAN for Mamba-2
* metal : fix SSM_SCAN pipeline scope
* metal : use log and exp instead of log1pf and expf in SSM_SCAN
* metal : remove unused arguments for SSM_SCAN
The max index is 31, so trimming the arguments is necessary.
* metal : add back n_seqs to SSM_SCAN args
Whoops, this is needed for the offset in the concatenated output.
* metal : fix SSM_SCAN state head offset
* metal : fix wrong number of tokens per sequence in SSM_SCAN
* ggml : remove unused fast broadcast path in GGML_MUL
This was initially added because states were masked with ggml_mul,
but this is no longer done and so this "optimisation" is no longer
necessary, or at least not worth the additional code complexity.
* ggml : avoid multiply by D in GGML_OP_SSM_SCAN
This makes the weight buft detection in src/llama.cpp simpler.
* convert : transpose Mamba-2 A, D and reshape SSM_NORM
This breaks existing conversions of Mamba-2 models
to avoid some reshapes.
Not sure if it's a good idea,
but it makes the graph slightly cleaner.
* llama : more appropriate SSM_SCAN and SSM_CONV buft support checks
* convert : fix flake8 lint
* llama : remove implicit recurrent state rollbacks
* llama : partially apply clang-format style
* metal : fix confusion between ; and ,
* metal : add missing args for nb references in ssm_scan_f32_group
* metal : single-user mamba2 inference works
* kv-cache : remove const_cast when setting inputs for s_copy
And also fix multi-user inference for recurrent models
by using cell_id instead of i as the kv cell index
when populating s_copy.
* convert : avoid AutoConfig for Mamba and Mamba2 hparams
* kv-cache : allow context shift for recurrent models
* graph : fix recurrent state copies when avoiding copies
Works, but using lambda functions might not be that clean.
* ggml : fix mamba2 ssm scan when compiled with SVE
* ggml-cpu : reorder SVE FMA for consistency with other SIMD arches
* cuda : implement ssm scan for Mamba2
There is still room for improvement, but it works!
* cuda : adapt Mamba1 ssm scan to shape changes from Mamba2
* feat: Add conversion for Bamba models
This is borrowed and adapted from the original implementation
https://github.com/ggml-org/llama.cpp/pull/10810
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add Granite 4 conversion
This is a manual copy from my draft branch
https://github.com/gabe-l-hart/llama.cpp/blob/GraniteFourDraft/convert_hf_to_gguf.py#L5076
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Plumb bamba through llama-arch
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add bamba to llama_arch_is_hybrid_recurrent
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add optional mamba ssm_in bias tensor
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add template specialization for get_arr to load a vector<uint32_t> for layer index arr in hparams
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Use an explicit bool to determine mamaba vs mamba2
This allows other architectures like bamba and granitemoehybrid to use
mamab2 without a growing architecture `if` statement inside the mamba
implementation.
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Isolate mamba(2) and granite attention layer building in static methods
This will allow these layer-builder methods to be used from other build
structs without complex inheritance.
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Use per-layer sizes in granite build_attention_layer
Also no need to pass in kv cache since it's already in the inp_attn
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: First (broken) pass at end-to-end Bamba implementation
It generates (garbage) tokens! Still lots of debugging to do.
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Only do Granite multipliers if set
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Pull granite ffn portion into a static function and reuse in hybrid
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(py): Allow gguf duplicate keys if they match by value and type
This is helpful for hybrid models that want to do gguf param setting by
calling multiple parent classes without needing to make those parent
classes try/except on every attempt to set a gguf value.
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor(py): Simplify granitemoehybrid conversion to use parents better
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add GRANITE_MOE_HYBRID through llama-arch
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Support GRANITE_MOE_HYBRID in llama-model
This re-uses the Bamba code paths heavily and simply adds the missing parts
for loading MoE and the shared expert.
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* style: Fix flake8 errors
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Fix recurrent cache get after rebase
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Fix hybrid granite implementation for signature changes in build_mamba*_layer
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Refactor relationship between non-hybrid classes and hybrid impl to use mixins
The challenge here is to give both the non-hybrid classes (llm_build_mamba
and llm_build_granite) AND the hybrid class (llm_build_hybrid_mamba) access
to the same intermediate "base class" functionality (build_mamba*_layer,
build_granite_attention_layer) without running into trouble with diamond
inheritance of llm_graph_context. Due to the non-trivial initialization
that happens in llm_graph_context, diamond inheritance results in multiple
initializations of the common base which cause problems around the unique
ptrs. I wanted to get away from `self->` everywhere, but this is still a
bit cleaner than making those methods static I think.
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Implement the full copy-paste version to duplicate the layer builders
This follows the pattern where the type of input is pinned to the type of
memory and that is used to dispatch to the correct version of `build_rs` /
`build_attn`. There's a lot of code duplication that can hopefully be
pulled into common functions in the graph later.
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Rename llm_build_hybrid_mamba -> llm_build_granite_hybrid
I've got back-and-forth a lot about how/if to try to implement reuse of the
"child model" layer types for hybrid models. At the end of the day, I think
hybrid models are their own beast and even if their layers are inspired by
other models, they should maintain control of their own layer building (in
other words, the copy-paste method). Given that, the name should reflect
that this is not a generic hybrid model builder, but rather a granite-
specific hybrid model builder that can do MoE (granite 4) or dense (bamba).
As part if this, I also cleaned up dangling comments from previous attempts
at using static methods for reusability.
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* mamba : fix mismatched new and delete size for llm_build_mamba
Subclasses of llm_graph_context cannot have extra fields,
because the called destructor is not the one from the subclass.
This otherwise would cause problems when runnning Mamba-(1|2) inference
when compiled -DGGML_SANITIZE_ADDRESS=ON
* memory : correctly handle failure in apply()
ggml-ci
* style: Remove TODO for adding first hybrid models to the switch
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Fix bad merge in tensor_mapping.py w/ SSM_NORM
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Fix bad merge resolution with variable renames/moves in llm_build_mamba
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* docs: Fix comment about duplicate key check
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Conform to standard way of initializing inp_out_ids
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* convert : fix jamba conv1d shape squeezing
* fix: Fix input initialization in granite_hybrid after removal of hybrid inputs
Branch: GraniteFourWithJamba
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Use llm_graph_context_mamba in llm_build_granite_hybrid
Branch: GraniteFourWithJamba
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Refactor mamba2/granite/jamba/granite_hybrid relationships as mixins
The key is for the mixin classes (llm_graph_context_mamba,
llm_graph_context_granite) to use virtual inheritance from
llm_graph_context. This allows the common members to exist only once in the
class hierarchy. The downside is that llm_graph_context will be
re-initialized once for each parent (ie 2x for single mixin, 3x for two
mixins, etc...).
Branch: GraniteFourWithJamba
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* graph : add back hybrid memory graph input
But this time it contains the sub-cache graph inputs.
This *should* make it easier to handle updating the inputs
when caching the graph (eventually).
* model : add Jamba to Mamba-specific hparams printing
* fix: Fix input setup after upstream merge
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* jamba : remove redundant nullptr initializations
* model : remove unnecessary prefix for tensor loading constants
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* model : use ggml_swiglu_split for Mamba
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* feat: Add support for dense FFN in GraniteMoeHybrid
This was already partially supported via reusing the granite ffn builder,
and there may be models that leverage this architecture going forward. The
naming is a bit odd, but in the transformers version, it reuses the same
model class and simply has zero regular experts and a single shared expert
(which is the same as a single dense FFN).
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add support for dense FFN tensor names on c++ side
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Use child inputs for Falcon H1 after merge resolution
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Remove unnecessary prefix on tensor constants
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* model : make falcon-h1 use shared mamba2 layer builder
* memory : avoid referring to KV in recurrent cache logs
* fix: Revert order changes for Falcon H1 to stay consistent with upstream
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* gguf-py : avoid adding duplicate tensor mappings for Jamba
Some of the tensor names are common with Llama4
* refactor: Collapse Bamba and GraniteMoeHybrid into GraniteHybrid
The only key difference is the use of rope which is now set via
rope_finetuned in the hparams
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Remove use of diamond inheritance
Per PR discussion, it's simpler to keep this with basic inheritance and not
introduce the complexity of virtual inheritance and multiple inheritance
https://github.com/ggml-org/llama.cpp/pull/13550#issuecomment-3053787556
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Log mamba params for Granite Hybrid
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Remove unused ssm_in_b
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Remove ATTENTION_LAYER_INDICES hparam in favor of n_head_kv
This matches how recurrent vs attention heads are identified for Jamba
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Remove unused template expansion for get_arr
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Review cleanup in convert_hf_to_gguf
The gist is to be explicit about which base class is being used with the
multiple inheritance setup
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Undo hidden warnings about duplicate identical keys in add_key_value
After further discussion, this encourages sloppy overwriting in the model
converters
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: If not using ROPE, context is "infinite"
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* doc: Add a comment outlining expected duplicate key warnings
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Remove unnecessary duplicate keys in converter
Co-authored-by: Francis Couture-Harpin <git@compilade.net>
(thanks for the sharp eyes and patience!)
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
---------
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Francis Couture-Harpin <git@compilade.net>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* wip: llama : separate recurrent states from the KV cache
This will be necessary to support Jamba
(and other recurrent models mixed with Attention).
Doesn't compile yet, and finding a slot isn't yet done correctly for recurrent states.
* llama : use std::find for seq_nodes in llama_rs_cache
* llama : state checkpoints for recurrent models
* llama : correctly handle more edge cases for the rs cache
* llama : rename many llama_kv_cache_* functions
* llama : remove useless return value for some llama_cache_* functions
* llama : rethink recurrent state cell counts
* llama : begin work on support for variable GQA
This will also be useful for Jamba if we consider the Mamba layers
to have 0 KV heads.
* llama : gracefully fail when not finding hybrid slot
* llama : support Jamba
* llama : fix BERT inference without KV cache
* convert-hf : check for unprocessed Jamba experts
* convert-hf : support Mini-Jamba conversion
* llama : fix Jamba quantization sanity checks
* llama : sequence-length-aware batch splitting
* llama : use equal-sequence-length sub-batches for recurrent models
* ggml : simplify SSM-related operators
* llama : make recurrent state slot allocation contiguous
* llama : adapt internal uses of batches to llama_ubatch
* llama : fix batch split output count for embeddings
* llama : minimize swaps when reordering logits
This reduces overhead when running hellaswag
on thousands of sequences with very small 100k params Mamba models.
* llama : fix edge case finding batch seq_id of split recurrent cell
This otherwise was a problem when running the HellaSwag benchmark
with small batch sizes, making it crash.
* llama : avoid copies for simple batch splits
* ggml : make ggml_ssm_scan not modify its source tensors
* llama : fix shared recurrent tail cell count for small ubatch sizes
Otherwise it was impossible to run the 'parallel' example with '-ub 1'
with a Mamba or Jamba model.
* llama : fix .base() compilation error on Windows
* llama : allow doing the equivalent of SSM_CONV with SUM_ROWS and MUL
* ggml : allow GGML_OP_CONCAT to work on non-contiguous tensors
The implementation already supported it,
and this makes Mamba's conv step slightly faster.
* mamba : fix non-contiguous usage of ggml_silu
* llama : session saving and reloading for hybrid models
* convert_hf : fix Jamba conversion
* llama : fix mixed signedness comparison
* llama : use unused n_embd_k_gqa in k_shift
This also slightly reduces the diff from the master branch
* llama : begin renaming llama_past back to llama_kv_cache
* llama : remove implicit recurrent state rollbacks
* llama : partially apply clang-format style
* convert : fix jamba conv1d shape squeezing
* graph : add back hybrid memory graph input
But this time it contains the sub-cache graph inputs.
This *should* make it easier to handle updating the inputs
when caching the graph (eventually).
* model : add Jamba to Mamba-specific hparams printing
* jamba : remove redundant nullptr initializations
* model : remove unnecessary prefix for tensor loading constants
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* model : use ggml_swiglu_split for Mamba
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* model : make falcon-h1 use shared mamba2 layer builder
* memory : avoid referring to KV in recurrent cache logs
* gguf-py : avoid adding duplicate tensor mappings for Jamba
Some of the tensor names are common with Llama4
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Splits producing more than one ubatch per batch for recurrent models
were broken with #14512.
This fixes it by moving the completeness check after the ubatch split loop.
* kv-cache : use ggml_set_rows
ggml-ci
* graph : separate k and v indices
ggml-ci
* cont : remove redundant ifs
ggml-ci
* kv-cache : improve find_slot impl
* kv-cache : bounds-check when accessing slot_info indices
* kv-cache : add comments
ggml-ci
* ggml : add TODOs for adding GGML_OP_SET_ROWS support in the backends
ggml-ci
* llama : initial Mamba-2 support
* ggml : SIMD ggml_ssm_scan for Mamba-2
* ggml : improve ggml_mul speed when masking recurrent states
* llama : support running Mamba-Codestral-7B-v0.1
* llama : fix Mamba-2 conv state saving
* ggml : make the ggml_mul fast broadcast path more consistently formatted
* llama : remove unused variable
* llama : add missing break
* convert_hf : prefer SentencePiece tokenizer for Mamba-2 when present
The tokenzier.json of Mamba-Codestral-7B-v0.1 otherwise requires
workarounds to work correctly.
* llama : avoid redundant state copy for Mamba 1 and 2
* metal : attempt to adapt SSM_SCAN for Mamba-2
* metal : fix SSM_SCAN pipeline scope
* metal : use log and exp instead of log1pf and expf in SSM_SCAN
* metal : remove unused arguments for SSM_SCAN
The max index is 31, so trimming the arguments is necessary.
* metal : add back n_seqs to SSM_SCAN args
Whoops, this is needed for the offset in the concatenated output.
* metal : fix SSM_SCAN state head offset
* metal : fix wrong number of tokens per sequence in SSM_SCAN
* ggml : remove unused fast broadcast path in GGML_MUL
This was initially added because states were masked with ggml_mul,
but this is no longer done and so this "optimisation" is no longer
necessary, or at least not worth the additional code complexity.
* ggml : avoid multiply by D in GGML_OP_SSM_SCAN
This makes the weight buft detection in src/llama.cpp simpler.
* convert : transpose Mamba-2 A, D and reshape SSM_NORM
This breaks existing conversions of Mamba-2 models
to avoid some reshapes.
Not sure if it's a good idea,
but it makes the graph slightly cleaner.
* llama : more appropriate SSM_SCAN and SSM_CONV buft support checks
* convert : fix flake8 lint
* metal : fix confusion between ; and ,
* metal : add missing args for nb references in ssm_scan_f32_group
* metal : single-user mamba2 inference works
* kv-cache : remove const_cast when setting inputs for s_copy
And also fix multi-user inference for recurrent models
by using cell_id instead of i as the kv cell index
when populating s_copy.
* convert : avoid AutoConfig for Mamba and Mamba2 hparams
* kv-cache : allow context shift for recurrent models
* graph : fix recurrent state copies when avoiding copies
Works, but using lambda functions might not be that clean.
* ggml : fix mamba2 ssm scan when compiled with SVE
* ggml-cpu : reorder SVE FMA for consistency with other SIMD arches
* cuda : implement ssm scan for Mamba2
There is still room for improvement, but it works!
* cuda : adapt Mamba1 ssm scan to shape changes from Mamba2
* mamba : fix mismatched new and delete size for llm_build_mamba
Subclasses of llm_graph_context cannot have extra fields,
because the called destructor is not the one from the subclass.
This otherwise would cause problems when runnning Mamba-(1|2) inference
when compiled -DGGML_SANITIZE_ADDRESS=ON
* cuda : graceful fallback for Mamba-1 models with weird embd size
* implement unary REGLU/GEGLU/SWIGLU cpu ops
* relax constraints
* duplicate shape of source
* fix ggml_vec_geglu_f16
* special case gated ops
* implement unary REGLU/GEGLU/SWIGLU cuda ops
* tighten constraints again
* refactor into GGML_GLU_OP
* metal : add glu kernels
ggml-ci
* add CUDA_GLU_BLOCK_SIZE [no ci]
* more constraints and use 64bit ints
ggml-ci
* 64bit multiplication [no ci]
* implement swapped variants (cpu/cuda)
* update comment [no ci]
ggml-ci
* Vulkan: Add GLU ops and shaders
* SYCL: Implement fused kernel GEGLU, SWIGLU and REGLU for single up+gate
* ggml : implement GLU for split up/gate (#14181)
* implement GLU for split up/gate
* add tests for ggml_glu_split
* Vulkan: Implement glu_split logic and shader support
* add split to logging [no ci]
* SYCL: refactor element_size ops and add split up and gate support to gated kernels
* SYCL: switch GEGLU to use tanh approximation
---------
Co-authored-by: 0cc4m <picard12@live.de>
Co-authored-by: Akarshan <akarshan@menlo.ai>
* GGML: increase OP count in assertion
* Refactor: Optimize SYCL element-wise operations with unary function inlining
This commit refactors the SYCL element-wise operations to improve performance by:
- Inlining unary operations (sgn, abs, elu, gelu, silu, etc.) to reduce kernel launch overhead.
- Introducing helper functions `op_xxx` for each unary operation to encapsulate the logic.
- Replacing direct kernel calls with calls to these inlined functions.
- Using `__dpct_inline__` to encourage compiler inlining.
- Minor code cleanup and consistency improvements.
The changes aim to reduce kernel launch overhead and improve the overall efficiency of element-wise operations on SYCL devices.
* vulkan: Increase workgroup size for GLU, for performance (#14345)
* vulkan: Increase workgroup size for GLU, for performance
* vulkan: change GLU shaders to do one element per invocation rather than one row per workgroup
* merge fix
* metal : add support for split and swap
ggml-ci
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: 0cc4m <picard12@live.de>
Co-authored-by: Akarshan <akarshan@menlo.ai>
Co-authored-by: Jeff Bolz <jbolz@nvidia.com>
* feat: Add llama_model_is_hybrid API call
Also, split llama_model_is_recurrent into llm_arch_is_recurrent in
llama-arch with llama_model_is_recurrent delegating to
llm_arch_is_recurrent. The same split is done for hybird. This is needed
because there are places where the llama_model has not yet been initialized
but we need to check if the model is recurrent (specifically for the
per-layer recurrent check array in hparams).
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add c++ side constants for attention layer indices hparam
Branch: GraniteFour
* feat: Add support for distinguishing recurrent vs non-recurrent layers in hparams
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Auto-fill hparams.recurrent_layer_arr based on whether the model is recurrent
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: rename *_is_hybrid -> *_is_hybrid_recurrent
The implementation of the hybrid cache intentionally does not specify the
types of the child caches, so there was a naming mismatch with these
predicate functions that used "hybrid" to imply "hybrid recurrent."
Branch: HybridCache
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add layer filter to recurrent cache
Branch: HybridCache
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Use per-layer sizing everywhere in kv caches
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: First pass at llama_kv_cache_hybrid_recurrent
This follows the pattern in iswa where the two child caches are held
explicitly to support the case where a model requires a single attention
cache and a single recurrent cache where each layer uses exactly one of the
caches.
This is a rewrite of the more generic approach in the original hybrid cache
PR: https://github.com/ggml-org/llama.cpp/pull/13276
Branch: HybridRecurrentCache
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Construct hybrid recurrent cache for hybrid recurrent models
This includes a refactor of the create_memory logic to avoid needing to use
the arch enum explicitly unless a model needs explicit cache instantiation
logic beyond the standard logic for recurrent, hybrid, unified, and iswa.
Branch: HybridRecurrentCache
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Fix wrong bool condition for split equal in hybrid cache
Branch: HybridRecurrentCache
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Fix shift logic to defer to unified cache
Branch: HybridRecurrentCache
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Support hybrid recurrent in llama-graph
NOTE: I intentionally did not add support for s_mask since it will be going
away soon
Branch: HybridRecurrentCache
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Fix logic for initializing inputs and attn layers for hybrid caches
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Update recurrent cache for changes to remove intermediate kv_cache interface
Branch: HybridRecurrentCache
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Fix status for init_update sig for recurrent cache state
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Add missing padding to n_ctx for hybrid cache construction
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Update clear signature for data argument after rebase
Branch: HybridRecurrentCache
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Remove errant virtual destructor leftover from previous impl attempt
Branch: HybridRecurrentCache
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Use per-layer n_embd_k/v_s calls for mamba (1) layers
Branch: HybridRecurrentCache
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Remove n_embd_k/v_s from unified cache
No longer needed now that unified isn't also supporting recurrent
https://github.com/ggml-org/llama.cpp/pull/13979#discussion_r2140761069
Branch: HybridRecurrentCache
* refactor: Remove layer index from n_embd_k/v_s
Now that it's not used at all in the unified cache, we don't need to use
the layer index to zero it out for attention layers.
Branch: HybridRecurrentCache
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Remove n_embd_k/v_gqa from recurrent cache
This is no longer needed now that there are separate implementations
https://github.com/ggml-org/llama.cpp/pull/13979#discussion_r2140825128
Branch: HybridRecurrentCache
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Allow custom layer filters for hybrid recurrent
This should help support architectures like Falcon H1 where there is
overlap between layers that need attention and recurrent caches.
https://github.com/ggml-org/llama.cpp/pull/13979#discussion_r2140748922
Branch: HybridRecurrentCache
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Remove logits_all after rebase
Branch: HybridRecurrentCache
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Remove llama_model_is_hybrid_Recurrent public API
https://github.com/ggml-org/llama.cpp/pull/13979#discussion_r2141728423
Branch: HybridRecurrentCache
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Use llama_memory_state_ptr for child states in hybrid memory state
Branch: HybridRecurrentCache
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Overhaul build_recurrent_state / build_inp_s_copy to match attention pattern
https://github.com/ggml-org/llama.cpp/pull/13979/files#r2141701738
This is a big overhaul to bring consistency between how inputs and per-
layer components are created for attention layers and recurrent layers. The
main changes are:
- Rename class llm_graph_input_s_copy -> llm_graph_input_rs
- Add a corresponding llm_graph_input_rs_hybrid_recurrent
- Rename build_inp_s_copy -> build_rs_inp_recurrent
- Add a corresponding build_rs_inp_hybrid_recurrent
- Rename build_recurrent_state -> build_rs to match build_attn w/
llm_graph_input_rs android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input
- Add a corresponding overload of build_rs w/
llm_graph_input_rs_hybrid_recurrent android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input
- Add a llm_graph_input_attn_kv_hybrid_recurrent analogous to
llm_graph_input_attn_kv_unified
- Add a build_attn override that takes
llm_graph_input_attn_kv_hybrid_recurrent android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input
This makes the two paradigms fully consistent. The main drawback is the
code duplication in the build_attn and build_rs implementations where the
only difference between implementations is how they cast the memory state.
Branch: HybridRecurrentCache
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Fix resize vs reserve and skip null tensors in size computation
https://github.com/ggml-org/llama.cpp/pull/13979/files#r2149469788
Branch: HybridRecurrentCache
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-Authored-By: @younesbelkada
* fix: Fix initialization of child states
Since initially writing this PR, the logic in the child state types changed
such that using the "init full" signature and keeping the ubatches on the
parent struct no longer worked.
Branch: HybridRecurrentCache
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Use a common build_recurrent_state method that is cache-agnostic
This reduces the code duplication between the different build_rs impls and
also retains a similar signature to the previous build_recurrent_state
method while standardizing on the input-dispatched build_rs implementation.
Branch: HybridRecurrentCache
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* recurrent : rework graph inputs + add TODOs
ggml-ci
* refactor: Make status and child states const in hybrid and iswa
Branch: HybridRecurrentCache
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Rename llama_kv_cache_[recurrent|hybrid_recurrent] to remove kv cache
This removes the notion of "kv" from the interface names for these memory
types. There are still many references to kv in the implementation of the
recurrent memory which will need further adjustment.
Branch: HybridRecurrentCache
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor!: Rename all k/v related values for recurrent/hybrid to r/s
Anywhere that "kv_<state|cell|size|etc>" is used, I've used the more
generic "mem_" prefix. The specifics of "k" (key) translate to "r"
(recurrent state) and "v" (value) translate to "s" (state-space embedding
states).
Branch: HybridRecurrentCache
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refacor: _recurrent -> _recr for brevity
It just _happens_ to have the same number of letters as _attn!
Branch: HybridRecurrentCache
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* style: Fix spacing for ref
Branch: HybridRecurrentCache
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: recurrent_layer() -> is_recurrent()
Branch: HybridRecurrentCache
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* style: Fix spacing for size_s_bytes declaration
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* llama : add thread safety test
* llamafile : remove global state
* llama : better LLAMA_SPLIT_MODE_NONE logic
when main_gpu < 0 GPU devices are not used
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Add Arcee AFM support
* Add draft update code
* Fix linter and update URL, may still not be final
* Update src/llama-model.cpp
Co-authored-by: Xuan-Son Nguyen <thichthat@gmail.com>
* Remote accidental blank line
---------
Co-authored-by: Xuan-Son Nguyen <thichthat@gmail.com>
Adds:
* Dots1Model to convert_hf_to_gguf.py
* Computation graph code to llama-model.cpp
* Chat template to llama-chat.cpp to detect this model's template.
---
The model is called "dots.llm1" (I decided to shorten it to dots1 or
DOTS1 in the code generally) architecture.
The only models that exist as of writing of this commit that follow this
architecture are "dots.llm1.inst" and "dots.llm1.base" from here:
* https://huggingface.co/rednote-hilab/dots.llm1.inst
* https://huggingface.co/rednote-hilab/dots.llm1.base
The model architecture is a combination of Qwen and Deepseek parts, as
seen here:
ffe12627b4/src/transformers/models/dots1/modular_dots1.py
* kv-cache : avoid modifying recurrent cells when setting inputs
* kv-cache : remove inp_s_mask
It was replaced with equivalent and simpler functionality
with rs_z (the first zeroed state) and the already-existing inp_s_copy.
* kv-cache : fix non-consecutive token pos warning for recurrent models
The problem was apparently caused by how the tail cells were swapped.
* graph : simplify logic for recurrent state copies
* kv-cache : use cell without src refs for rs_z in recurrent cache
* llama-graph : fix recurrent state copy
The `state_copy` shuffle assumes everything is moved at once,
which is not true when `states_extra` is copied back to the cache
before copying the range of states between `head` and `head + n_seqs`.
This is only a problem if any of the cells in [`head`, `head + n_seqs`)
have an `src` in [`head + n_seqs`, `head + n_kv`),
which does happen when `n_ubatch > 1` in the `llama-parallel` example.
Changing the order of the operations avoids the potential overwrite
before use, although when copies are avoided (like with Mamba2),
this will require further changes.
* llama-graph : rename n_state to state_size in build_recurrent_state
This naming should reduce confusion between the state size
and the number of states.
* llama : deprecate llama_kv_self_ API
ggml-ci
* llama : allow llama_memory_(nullptr)
ggml-ci
* memory : add flag for optional data clear in llama_memory_clear
ggml-ci
* kv-cache : simplify the "struct llama_kv_cache" interface
ggml-ci
* kv-cache : revert the (n_swa + n_ubatch) change (for next PR)
ggml-ci
* kv-cache : some comments
ggml-ci
* context : fix graph reserve for multiple sequences
ggml-ci
* kv-cache : fix typo [no ci]
* kv-cache : fix find_slot() logic for free slots
ggml-ci
* llama : add TODO for deprecating the defrag API in the future
* kv-cache : improve find_slot() using min/max seq pos info
ggml-ci
* llama : handle aborts and compute errors
ggml-ci
* memory : extract state into llama_memory_state
ggml-ci
* kv-cache : add comments
ggml-ci
* server : update batching logic to reset n_batch on successful decode
* server : upon full re-processing, remove the sequence from the cache
* kv-cache : add TODO for doing split_equal when split_simple fails
ggml-ci
* add distilbert
* small fixes
* add note for LLM_ARCH_DISTIL_BERT
* Use MODEL_ARCH.BERT for DistilBert
---------
Co-authored-by: dinhhuy <huy.dinh@brains-tech.co.jp>
* convert: add support for BertForSequenceClassification
* add support for reranking using BertForSequenceClassification
* merge checks of eos and sep
* fix lint
---------
Co-authored-by: dinhhuy <huy.dinh@brains-tech.co.jp>
* sampling : min-p should always return at least one token
ggml-ci
* sampling : same for typical sampling
* tests : sampling tests use min_keep == 0
ggml-ci
This matches how others do it, but will still avoid the extra
initialization when rope is disabled.
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add GGUF conversion for granitemoeshared
Branch: GraniteMoEShared
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: hparam and arch plumbing for granitemoeshared
Branch: GraniteMoEShared
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Split MoE fused tensors for shared experts in conversion
Branch: GraniteMoEShared
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: First WIP cut at model arch in cpp
The hparam and architecture plumbing should be correct, but the
implementation of the shared experts seems to still be broken.
Branch: GraniteMoEShared
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Cleaner (maybe more correct?) splitting for gate/up
Branch: GraniteMoEShared
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Fix the input to the shared experts
I had misread that the shared experts take the inputs _before_ the standard
MoE layer and was feeding the output of the MoE to the shared experts.
Branch: GraniteMoEShared
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Avoid architecture-specific checks for Granite MoE Shared
This is a cleaner way that will allow more flexibility in architecture
strings going forward.
Branch: GraniteMoEShared
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Split granite architectures out of llm_build_llama
This helps de-clutter the llama-family graph construction and allows
granite to diverge further (in preparation for Granite 4).
NOTE: I removed the granite scale factors from llm_build_deci because they
appear to only be there as copy-paste from llm_build_llama. The HF config
does not seem to set those values:
https://huggingface.co/Deci/DeciLM-7B/blob/main/config.json
Branch: GraniteMoEShared
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Fix compiler warning about uninitialized inp_pos
This should not have been reachable, but it warns on some compliers
Branch: GraniteMoEShared
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Consoladate GraniteMoEShared into GraniteMoE for conversion
Branch: GraniteMoEShared
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Consolidate GraniteMoEShared into GraniteMoE on the c++ side
Branch: GraniteMoEShared
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
---------
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* llama/ggml: add LLM training support
more compact progress bar
llama_save_model_to_file
llama_opt_param_filter
ggml_graph_dup force_grads
refactor ggml_opt, fix test-opt
* remove logits_all
* refactor CUDA implementation for ACC
* reset graph at beginning of opt period
Sigbjørn Skjæret
Daniel Bevenius
Aldehir Rojas
Georgi Gerganov
kallewoof
Jonathan Graehl
Xuan-Son Nguyen
stevenkuang
Juk Armstrong
compilade
Sam
Csaba Kecskemeti
Douglas Hanley
Ed Addario
Diego Devesa
Dongliang Wei
Aman Gupta
Gabriel Larson
Shunta Saito
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Molly Sophia
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Piotr Wilkin (ilintar)
Tarek Dakhran
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Tarek Dakhran
Dowon
Gabe Goodhart
Ryan Mangeno
ibrahim khadraoui
Weizhao Ouyang
Ruikai Peng
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Gabe Goodhart
Đinh Trọng Huy
Bartowski
Mikko Juola
Guy Goldenberg
zhangkaihuo
Piotr Jasiukajtis
Olivier Chafik
0cc4m
Gilad S.
Johannes Gäßler
David Huang
piDack
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