* Add fastdiv and fastmodulo to k_bin_bcast kernel
* Address review comments
* `prod_` instead of `prod` suffix
* Add test case for `k_bin_bcast_unravel` in CUDA backend
* CUDA: Add mul_mat_id support the mmf
Add support for mul_mat_id for bs < 16
* Review: use warp_size, fix should_use_mmf condition
* Launch one block per expert, stride along n_expert_used
* templatize mul_mat_id
* Pad shmem to 16 bytes, add helper function mul_mat_f_switch_ids
* Reduce compile times by dividing mmf into f16, bf16 and f32 variants
* Divide mmf by ncols_dst
* Add missing files
* Fix MUSA/HIP builds
* requirements : update transformers/torch for Embedding Gemma
This commit updates the requirements to support converting
Embedding Gemma 300m models.
The motivation for this change is that during development I had a local
copy of the transformers package which is what I used for converting
the models. This was a mistake on my part and I should have also updated
my transformers version to the official release.
I had checked the requirements/requirements-convert_legacy_llama.txt
file and noted that the version was >=4.45.1,<5.0.0 and came to the
conculusion that no updated would be needed, this assumed that
Embedding Gemma would be in a transformers release at the time
Commit fb15d649ed ("llama : add support
for EmbeddingGemma 300m (#15798)) was merged. So anyone wanting to
convert themselves would be able to do so. However, Embedding Gemma is
a preview release and this commit updates the requirements to use this
preview release.
* resolve additional python dependencies
* fix pyright errors in tokenizer test and remove unused import
* Add DeepSeek V3.1 thinking mode support
- Added COMMON_CHAT_FORMAT_DEEPSEEK_V3_1 enum value
- Created common_chat_params_init_deepseek_v3_1() function (currently uses R1 implementation)
- Created common_chat_parse_deepseek_v3_1() function that handles V3.1 thinking format:
- Extracts reasoning content before '</think>' tag into reasoning_content
- Extracts regular content after '</think>' tag into content
- No opening '<think>' tag in V3.1 format
- Added detection logic for V3.1 templates based on pattern: 'message['prefix'] is defined and message['prefix'] and thinking'
- Added V3.1 case to parsing switch statement
This addresses the issue where V3.1 outputs reasoning content followed by '</think>' and then regular content without the opening '<think>' tag.
* Another attempt by V3.1 non-thinking
* Fix test, but it's not asserting anything.
* Ignore vim swap files in tests dir
* Update the test
* Try using try_find_literal instead of regex
* passing test
* Revert "Try using try_find_literal instead of regex"
This reverts commit c50d887ec2.
* Remove unnecessary change
* Remove comment
* Add code to handle non-thinking mode.
* Try to set message['prefix'] when thinking is enabled.
* This fixes reasoning, but breaks normal content. We need state in the
chat parser.
* DeepSeek V3.1 thinking is now the default. Disable with `--reasoning-budget 0`.
* Simplify (DeepSeek V3.1 reasoning)
* Fix sign inversion bug
* Add some tool calling code (not working).
* Tool calls working in non-reasoning mode.
* Attempt a unit test for tool call parsing.
* Passing test
* Add tests for both happy path and broken fenced DeepSeek V3.1 tool call variants.
* Passing DeepSeek V3.1 tool call tests, but model is not working.
* Revert assistance response prefill change. Not my monkeys.
* Add fenced_thinking unit test variant. Passes, but thinking tool calling
still isn't working for some reason.
* Tests pass in reasoning mode. Also e2e tool test passes.
* Make a copy of the parse_json_tool_calls function for deepseek-v3.1 so
as to not accidentally introduce regressions.
* Fix thinking_forced_open logic. tool calling broken. Need to add another
test case.
* That's what I get for cargo culting a newline.
* Add multi tool call test for deepseek v3.1 non-reasoning
* Move test, remove .gitignore change
* Place deepseek-v3.1 reasoning test directly into existing reasoning
function per CISC's request.
* Address whitespace CI failure.
* Merge two assert_equals per CISC's request.
* Add DeepSeek-V3.1 tests to tests/test-chat.cpp per CISC's request.
* Merge deepseek V3.1 and regular parse_json_tool_calls() function
behaviors by adding optional update_cursor argument.
* Update tests/test-chat-parser.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update tests/test-chat-parser.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update tests/test-chat-parser.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update tests/test-chat-parser.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update tests/test-chat-parser.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update tests/test-chat-parser.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update tests/test-chat-parser.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update tests/test-chat-parser.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update tests/test-chat-parser.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* DeepSeek V3.1 fix reasoning_format none
* Strip grammar down to strictly what we expect based on model card. Throw
out parts we cargo culted from R1 that don't make sense.
* Update tests/test-chat-parser.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* DeepSeek V3.1 - Add edge case where thinking is forced open, there is
tool calling in the reasoning content, but then the model just stops the
output without closing the </think> tag, so it's not a partial. In this
case, use the tool call in the reasoning content.
* DeepSeek V3.1 - simplify update_cursor
* Update common/chat.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update common/chat.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update common/chat.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Fix indent
---------
Co-authored-by: openhands <openhands@all-hands.dev>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* ggml: allow casting between f32 and i32
* fix cuda
* add vulkan
* fix CPU non-cont
* add non-cont test case
* add note
* extend test number range
* correct note
* add cont version for vulkan
This commit adds two new command-line options to the
test-backend-ops.cpp that allow users to list all available GGML
operations and to show test coverage of these operations.
The motivation for this is that it can be useful to quickly see which
operations are currently covered by tests and which are not. Also it
migth be useful when using the `support` mode.
* sampling : optimize sorting using bucket sort in more places
ggml-ci
* sampling : do not sort in dist sampler
ggml-ci
* sampling : avoid heap allocations for sort buffers
ggml-ci
* common : add option to sort sampling candidates by probability
ggml-ci
* sampling : revert the change for preserving sort buffers
* sampling : use std::copy instead of memcpy
* sampling : clarify purpose of partial sort helpers
ggml-ci
* cont : remove wrong comment [no ci]
* common : update comment
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
---------
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* metal : mul_mm_id remove hdst
* metal : remove mul_mm_id hsrc1
* metal : mul_mm_id simplify + add test
* metal : opt mul_mm_id map0
* metal : optimize mul_mm_id id gathering
* metal : mul/div opt
* metal : optimize mul_mm_id_map0
ggml-ci
The scalar FA shader already handled multiples of 8. The coopmat1 FA
shader assumed 16x16x16 and the shared memory allocations need the HSK
dimensions padded to a multiple of 16. NVIDIA's coopmat2 implementation
requires multiples of 16 for N and K, and needs the matrix dimensions
padded and loads clamped.
Store the FA pipelines in a map, indexed by the pipeline state.
* vulkan: optimize rms_norm, and allow the work to spread across multiple SMs
There are really two parts to this change:
(1) Some optimizations similar to what we have in soft_max, to unroll with
different numbers of iterations.
(2) A fusion optimization where we detect add followed by rms_norm, and make
the add shader atomically accumulate the values^2 into memory. Then the
rms_norm shader can just load that sum. This allows the rms_norm to be
parallelized across multiple workgroups, it just becomes a simple per-element
multiply.
The fusion optimization is currently only applied when the rms_norm is on a
single vector. This previously always ran on a single SM. It could apply more
broadly, but when there are other dimensions the work can already spread across
SMs, and there would be some complexity to tracking multiple atomic sums.
* Change add+rms_norm optimization to write out an array of partial sums
rather than using atomic add, to make it deterministic. The rms_norm
shader fetches a subgroup's worth in parallel and uses subgroupAdd to
add them up.
* complete rebase against fused adds - multi_add shader can also compute partial sums
* fix validation errors
* disable add_rms_fusion for Intel due to possible driver bug
* resolve against #15489, sync after clearing partial sums
bssrdf
Oliver Simons
Daniel Bevenius
Jesse
Jeff Bolz
Aman Gupta
Aldehir Rojas
Georgi Gerganov
Xuan-Son Nguyen
Piotr Wilkin (ilintar)
leejet
rmatif
Diego Devesa
Eve