* server: spec checkpoints for recurrent models
* fix: save/restore sampler state during speculative checkpoint
When speculative decoding rejects draft tokens and restores the
recurrent state checkpoint, the sampler (RNG, grammar, prev tokens)
must also be restored to maintain consistency. Without this, the
sampler state reflects the rejected draft tokens, leading to
potential divergence.
Uses common_sampler_clone() to snapshot the sampler before the
speculative batch decode, and restores it on rejection.
* server: snapshot recurrent state in tensor
* reset ngram mod state for rejected tokens
* server: refactor checkpoint state logic
* speculative: fix sampler for checkpoints
* recurrent model: implement recurrent kernel checkpoint
* recurrent model: refactor api
* spec: free rbudget before overwriting
* Autoparser - complete refactoring of parser architecture
Autoparser: add optional argument reshuffle capability
Autoparser: True streaming (#20177)
* Relax atomicity constraint for nicer, more pleasent, True Streaming parsing
* Whitespace
* Remove redundant atomics
Revert to OAI-compatible args (#20213)
* Revert to OAI-compatible args
* Apply workaround::func_args_not_string
Fix structured outputs (#20223)
* Fix structured outputs
* Update common/chat-auto-parser-generator.cpp
Co-authored-by: Aldehir Rojas <hello@alde.dev>
---------
Co-authored-by: Aldehir Rojas <hello@alde.dev>
Fix compile bug (#20203)
* Fix compile bug
* Update common/chat-auto-parser-helpers.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
# Conflicts:
# common/chat-auto-parser-helpers.cpp
common : gracefully handle incomplete output (#20191)
* common : handle incomplete UTF-8 at end of input in PEG parser
* cont : if reached end prematurely, emit needs_more_input to propagate partial output
* cont: refactor peg parse context to add lenient flag
* cont : remove partial flag, keep lenient flag
PEG parser for LFM2 (#20251)
* PEG parser for LFM2
* Simplify using python_value()
common: map developer role to system (#20215)
* Map developer role to system
* Simplify
common: consolidate PEG string parsers (#20263)
* common : consolidate PEG string parsers
* cont : fix json_string_content()
examples : fix empty items in json_schema_to_grammar.py [no ci] (#19968)
* Fix logic for retrieving schema items in `json_schema_to_grammar.py`
If `schema['items']` is `{}` and `prefixItems not in schema', as `{}` is Falsy, the original code here will raise an error.
I think if `schema['items']` is `{}`, them items should just be `{}`
* Apply suggestion from @CISC
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Add tests for arrays with empty items
Add two unit tests to `tests/test-json-schema-to-grammar.cpp` that validate handling of arrays when 'items' is an empty schema and when 'prefixItems' is present alongside an empty 'items'. Both tests expect the same generated grammar, ensuring the JSON Schema->grammar conversion treats an empty 'items' schema (and the presence of 'prefixItems') correctly and covering this edge case.
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Reduce level of content parser warning message to avoid log spam on non-debug verbosity (#20347)
do not return if template parse failed
add arg to enable parallel tool call
common : fix incorrect uses of stoul (#20313)
# Conflicts:
# common/arg.cpp
# src/llama-grammar.cpp
examples : fix empty items in json_schema_to_grammar.py [no ci] (#19968)
* Fix logic for retrieving schema items in `json_schema_to_grammar.py`
If `schema['items']` is `{}` and `prefixItems not in schema', as `{}` is Falsy, the original code here will raise an error.
I think if `schema['items']` is `{}`, them items should just be `{}`
* Apply suggestion from @CISC
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Add tests for arrays with empty items
Add two unit tests to `tests/test-json-schema-to-grammar.cpp` that validate handling of arrays when 'items' is an empty schema and when 'prefixItems' is present alongside an empty 'items'. Both tests expect the same generated grammar, ensuring the JSON Schema->grammar conversion treats an empty 'items' schema (and the presence of 'prefixItems') correctly and covering this edge case.
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Add support for MiroThinker with new jinja template
common/parser: handle reasoning budget (#20297)
* v1
* Finished!
* Handlie cli
* Reasoning sampler
* Apply suggestions from code review
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Less explosive terminology :)
* Add utf-8 case and tests
* common : migrate reasoning budget sampler to common
* cont : clean up
* cont : expose state and allow passing as initial state
* cont : remove unused imports
* cont : update state machine doc string
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Co-authored-by: Alde Rojas <hello@alde.dev>
common/parser: use nlohmann::ordered_json to preserve parameter order (#20385)
common/parser: add GigaChatV3/3.1 models support (#19931)
Co-authored-by: Mishusha <pmv26021975@gmail.com>
common/parser: gracefully handle undetected tool parser, print error message. (#20286)
fix: prevent nullptr dereference (#20552)
common : fix iterator::end() dereference (#20445)
# Conflicts:
# common/regex-partial.cpp
jinja : add capability check for object args (#20612)
common/parser: add `--skip-chat-parsing` to force a pure content parser. (#20289)
* Add `--force-pure-content` to force a pure content parser.
* Update common/arg.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
common : rework gpt-oss parser (#20393)
* common : rework gpt-oss parser
* cont : fix gpt-oss tests
* cont : add structured output test
* cont : rename final to final_msg
common : fix gpt-oss content removal (#20745)
common/parser: add proper reasoning tag prefill reading (#20424)
* Implement proper prefill extraction
* Refactor cli parameters, update docs, move reasoning budget sampler part to common/reasoning-budget.cpp
* Update tools/server/server-task.cpp
* refactor: move grammars to variant, remove grammar_external, handle exception internally
* Make code less C++y
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
chat : handle tool calls with no required args in TAG_WITH_TAGGED format (#20764)
* chat : handle tool calls with no required args in TAG_WITH_TAGGED format
* Update tests/test-chat.cpp [no ci]
Co-authored-by: Aldehir Rojas <hello@alde.dev>
---------
Co-authored-by: Piotr Wilkin (ilintar) <piotr.wilkin@syndatis.com>
Co-authored-by: Aldehir Rojas <hello@alde.dev>
common/parser : fix out_of_range crash in throw path (#20424 regression) (#20777)
* chat : fix out_of_range crash in throw path (#20424 regression)
#20424 introduced effective_input = generation_prompt + input, but the
throw path uses input.substr(result.end) where result.end is a position
within effective_input. Every thinking model with a non-empty
generation_prompt crashes with std::out_of_range instead of the intended
error message.
Test crashes on unpatched master, passes with fix:
cmake -B build -DLLAMA_BUILD_TESTS=ON -DLLAMA_BUILD_TOOLS=OFF
cmake --build build --target test-chat
./build/bin/test-chat
* Update test-chat.cpp
* Update test-chat.cpp
* Update test-chat.cpp
---------
Co-authored-by: Piotr Wilkin (ilintar) <piotr.wilkin@syndatis.com>
jinja : fix heap OOB read in value equality comparison (#20782)
Address GHSA-q9j6-4hhc-rq9p and GHSA-2q4c-9gq5-5vfp.
The three-iterator overload of std::equal in value_array_t::equivalent()
and value_object_t::equivalent() reads past the end of the shorter
container when comparing arrays or objects of different lengths.
Use the four-iterator overload (C++14) which checks both range lengths.
Found-by: Pwno
common : fix typo in debug log ('extracft' -> 'extract') (#20807)
common/parser: fix nasty bug causing subtle corruption of generation prompt (#20825)
jinja : refactor token advancement (#20864)
* refactor token advancement
* exercise sub-expressions
common/autoparser : detect reasoning markers when enable_thinking changes system prompt (#20859)
common : replace wrap_for_generation with a prefix convenience function and fix gpt-oss (#20912)
jinja: fix macro with kwargs (#20960)
* jinja: fix macro with kwargs
* Apply suggestions from code review
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* fix newline problem
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
common : inhibit lazy grammar sampler while reasoning is active (#20970)
* common : inhibit grammar while reasoning budget is active
* cont : update force_pos in accept
* cont : fix tests
* cont : tweak should apply logic
* cont : return early not using grammar sampler
* Add tests
* cont : prevent backend sampling when reasoning budget enabled
* cont : fix typo
---------
Co-authored-by: Piotr Wilkin <piotr.wilkin@syndatis.com>
# Conflicts:
# common/reasoning-budget.h
# common/sampling.cpp
# tools/cli/cli.cpp
# tools/server/server-common.cpp
# tools/server/server-task.cpp
common/parser: fix reasoning whitespace bugs + extra parser tests (#21085)
* fix whitespace reasoning issues + add reconstruction tests
* Proper fix
* fix Nemotron autoparser test expectations to include newline in marker
common : add reasoning_format = none support to gpt-oss (#21094)
common/json-schema: fix: handle non-capturing groups (?:...) in JSON schema pattern converter (#21124)
The regex-to-grammar converter in _visit_pattern() crashes with SIGSEGV
when a JSON schema "pattern" field contains a non-capturing group (?:...).
Root cause: when the parser sees '(' followed by '?', it pushes a warning
but does not advance past '?:'. The recursive transform() call then
interprets '?' as a quantifier and calls seq.back() on an empty vector,
causing undefined behavior.
This commonly occurs when serving OpenAI-compatible tool calls from
clients that include complex regex patterns in their JSON schemas (e.g.,
date validation patterns like ^(?:(?:\d\d[2468][048]|...)-02-29|...)$).
The fix:
- Skip '?:' after '(' to treat non-capturing groups as regular groups
- For unsupported syntax (?=, ?!, etc.), skip to matching ')' safely,
handling escaped characters to avoid miscounting parenthesis depth
- Adjust the ')' unbalanced-parentheses check using direct char
comparisons instead of substr
- Add test cases for non-capturing groups (C++ only, as the JS/Python
implementations do not yet support this syntax)
common/parser: fix handling of tool definition with missing properties key (#21128)
jinja : handle empty expressions correctly (#20913)
* Reject empty computed member expressions before returning slices[0] from parse_member_expression_arguments().
* Treat empty computed member expressions with Jinja2 undefined semantics
Treat empty computed member expressions like `a[]` as undefined instead of
raising a parser error, to match Jinja2 behavior.
- return a noop expression for empty computed member arguments
- return undefined when a computed member key evaluates to undefined
- add Jinja tests covering `a[]|default('fallback')` and `a[] is undefined`
* Handle undefined computed member properties
Move undefined-property handling to the common member access path, and add a test covering `a[undefined] is undefined`.
* Use default undefined value in member access
Initialize val and then return it when property is undefined.
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* empty statement parses to blank_expression instead of noop_statement
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
common : gpt-oss handle builtin and unsolicited tool calls (#21213)
fix: tool call parsing for LFM2 and LFM2.5 models (#21242)
* fix: tool call parsing for LFM2 and LFM2.5 models'
* refactor: add test / break out lfm2 and lfm2.5 parsing logic
# Conflicts:
# common/chat.cpp
Relax prefill parser to allow space. (#21240)
* Relax prefill parser to allow space.
* Move changes from prefix() to parser generation
* Only allow spaces if we're not having a pure content parser next
common : add commentary rules for gpt-oss-20b (#21286)
add reasoning budget
model, mtmd: fix gguf conversion for audio/vision mmproj (#21309)
* fix gguf conversion for audio/vision mmproj
* fix test
# Conflicts:
# convert_hf_to_gguf.py
# examples/eval-callback/eval-callback.cpp
# examples/mtmd/CMakeLists.txt
# examples/mtmd/clip-impl.h
# examples/mtmd/mtmd.cpp
# gguf-py/gguf/constants.py
# gguf-py/gguf/gguf_writer.py
# gguf-py/gguf/tensor_mapping.py
# src/CMakeLists.txt
# src/llama-arch.cpp
# src/llama-arch.h
# src/llama-model.cpp
# src/llama-model.h
# src/llama-vocab.cpp
# src/models/models.h
# tests/test-llama-archs.cpp
# tools/mtmd/clip-graph.h
# tools/mtmd/clip-model.h
# tools/mtmd/clip.cpp
# tools/mtmd/models/models.h
fix: gemma 4 template (#21326)
chat : avoid including json in chat.h (#21306)
jinja: coerce input for string-specific filters (#21370)
common : fix tool call type detection for nullable and enum schemas (#21327)
* common : fix tool call type detection for nullable and enum schemas
* common, tests : fix grammar delegation for nullable/enum schemas and add tests
Fix enum type inference to scan all enum values (not just index 0) so
schemas like {"enum": [0, "celsius"]} correctly detect string type.
Fix schema_delegates in peg-parser to handle nullable type arrays
(["string", "null"]) and typeless enum schemas in raw mode, allowing
the tagged parser to use raw text instead of JSON-formatted strings.
Add test cases for Qwen3-Coder (TAG_WITH_TAGGED format):
- nullable string ["string", "null"]
- nullable string with null first ["null", "string"]
- nullable integer ["integer", "null"]
- enum without explicit type key
common/parser: fix call ID detection (Mistral parser mostly) + atomicity for tag-json parsers (#21230)
* Fix call ID detection (Mistral parser mostly) + atomicity for tag-json parsers
* Rename
* Update common/chat-auto-parser-generator.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
common : add gemma 4 specialized parser (#21418)
* common : add gemma4 dedicated parser
* cont : add '<|tool_response>' as eog
* cont : emit JSON from Gemma4 tool call AST
* cont : more fixes
* cont : refactor convert function
* cont : refine rules and mapping
* cont : add more tests
* cont : clean up
* cont : remove autoparser gemma4 implementation
* cont : more cleanup
* cont : rename gemma4.jinja to match the others
* cont : add custom template to support interleaved thinking
* cont : preserve reasoning in model turns
* cont : fix initializer error
* cont : fix unused vars
* cont : fix accidental static
* cont : fix specialized_template signature
* fix extra semicolon
* remove debug line and extra space [no ci]
fix reasoning budget
parser: fix MiniMax handling (#21573)
jinja : support ensure_ascii=true, string repetition and int/float self-filtering (#21623)
* feat: jinja engine improvements for reka-edge
Port three Jinja engine improvements needed for the reka-edge model:
1. Python-style string repetition ("ab" * 3 → "ababab")
2. ensure_ascii=true support for tojson filter (escapes non-ASCII to \uXXXX)
3. int() builtin on value_int_t (identity, needed for Reka Edge template)
* fix: escape invalid utf8 bytes when ensure_ascii=true
The json_ensure_ascii_preserving_format function does not correctly
handle an edge case where if UTF-8 parsing fails, it adds the non-ascii
character back to the output as a raw byte.
This commit fixes that by adding the unicode standard replacement
character \\ufffd to the output instead. This is the standard behavior
for various programming languages like Python, Rust, Go, etc.
* chore: address PR comments
1. Add todo comment for supporting string repetition for array/tuples
2. Add support for float identity operation
3. Move invalid ascii test case to test_fuzzing
* chore: accept suggestion for common/jinja/value.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
common : simplify autoparser tagged parser rules (#21216)
* common : simplify autoparser tagged parser rules
* cont : remove upper limit on optional args
* cont : revert changes to parsing at the end
* cont : undo arbitrary ordering of optional args
* cont : fix uninitialized required parameters
* revert to simplify merge
* re-apply patches
* restore flexible optional arg ordering tests
common : fix ambiguous grammar rule in gemma4 (#21661)
* common : fix ambiguous grammar rule in gemma4
* cont : fix missing comma...
common : enable reasoning budget sampler for gemma4 (#21697)
* fix: enable reasoning budget sampler for gemma4
Add thinking_start_tag and thinking_end_tag to
common_chat_params_init_gemma4(). Without these, the reasoning
budget sampler never activates for gemma4.
Make the newline after "thought" optional in the PEG parser to
handle budget=0 (sampler forces end tag before the newline).
Add test case for empty thinking block.
Fixes#21487
* use p.space() instead of p.optional(p.literal("\n")) in gemma4 thought parser
common : better align to the updated official gemma4 template (#21704)
fix: Fix broken structured output when using $refs in json_schema (#21699)
chat: dedicated DeepSeek v3.2 parser + "official" template (#21785)
Hide render_message_to_json warning
common/gemma4 : handle parsing edge cases (#21760)
common: skip reasoning budget sampler when no budget is requested (#21870)
* common: skip reasoning budget sampler when no budget is requested
After I added thinking_start_tag / thinking_end_tag for gemma4 in #21697, the reasoning budget sampler gets unconditionally created even when no budget is configured (the default -1). The same applies to kimi_k2, lfm2, lfm2_5, and ministral_3 which also set these tags. The budget gets converted to INT_MAX, so the sampler never actually forces any tokens but still runs per-token checks (start tag matching in IDLE state, token-to-piece conversion + UTF-8 checks in COUNTING state).
More importantly, the mere existence of the sampler (non-null rbudget) disables backend sampling. Backend sampling lets the GPU select tokens directly, avoiding a full logits transfer from GPU to CPU every token. This could explain the 30% speed regression reported in #21784 (98 t/s to 70 t/s on Vulkan).
So I added a reasoning_budget_tokens >= 0 check to the sampler creation condition. When the budget is unlimited, the sampler is not created, backend sampling stays enabled, and no per-token overhead is added. When a budget is explicitly set (0, 128, 1024, etc.), the sampler is created and works as before.
* common: preserve rbudget when grammar is lazy
Following up on the review feedback on #21870: keep the reasoning budget sampler when grammar_lazy is true, so the thinking-block grammar suppression from #20970 still works when tools are in use. This way, we only skip the sampler when both no budget is set AND grammar is not lazy.
autoparser: support case of JSON_NATIVE with per-call markers (test case: Reka-Edge) (#21892)
* fix grammar
* fix add sampled token
---------
Co-authored-by: Piotr Wilkin (ilintar) <piotr.wilkin@syndatis.com>
Co-authored-by: firecoperana <firecoperana>
* wip: build spec tuner for spefic args
* wip: test different reward system
* spec-tune: fix the reward to find best params given a good TPS
* spec-tune: refactor logic for its own file
* minor clean for comments and modules
Purpose:
Add --minilog flag to llama-sweep-bench that filters log output to show only essential GPU/layer distribution information while suppressing verbose model metadata and per-layer device assignment messages.
Changes:
- Add llama_selective_log_callback with blacklist approach (sweep-bench.cpp)
Blacklisted patterns (hidden):
- Per-layer device assignments ('Setting default device in layer')
- KV metadata dump header and entries
- Tensor type counts
- Model validation messages
- EOG/special token cache info
- Metadata printout (llm_load_print_meta, print_info)
- Layer sizes table
- Tensor loading info (llm_load_tensors)
- Separator lines
- Most common cases of incomplete/continuation lines are also hidden
All other log output is shown, including:
- GPU VRAM info
- Split/buffer distribution per device
- Graph split estimates
- Final benchmark table and timings
Two changes:
1. Add four missing environment variable bindings to
gpt_params_parse_from_env():
- LLAMA_ARG_CACHE_TYPE_K (string, e.g. "q8_0")
- LLAMA_ARG_CACHE_TYPE_V (string, e.g. "q8_0")
- LLAMA_ARG_MLOCK (bool, "1"/"true")
- LLAMA_ARG_K_CACHE_HADAMARD (bool, "1"/"true")
2. Call gpt_params_parse_from_env() from gpt_params_parse() so that
ALL tools (llama-cli, llama-bench, etc.) respect env vars, not
just llama-server. Env vars act as defaults; CLI flags override.
Follows the existing get_env() pattern and uses the same
LLAMA_ARG_ prefix convention as the other env vars.
Co-authored-by: Pipboyguy <>
* server: enable checkpoint for recurrent models
create checkpoint after cancel
fix ban string and rm context during rewind
add checkpoint interval
only save recurrent cache
* save checkpoint during pp
---------
Co-authored-by: firecoperana <firecoperana>
* Revive fused delta-net
* Add command line argument for fused delta net
* Simplify/improve CUDA delta-net
* Add -fdn to llama-bench
* More CUDA fused delta net optimizations
* CPU optimizations
* Much faster fused delta-net on the CPU
It seems it is faster than the chunked implementation!
* Change meaning of fdn from bool flag to threshold value
* Use eps = 1e-6
* Give some nodes a name
* wip: port MTP architecture
Ports the Multi-Token Prediction (MTP) architecture to the older `llama.cpp` codebase used by `ikllama`.
Changes include:
- Updating `llama_batch` to support `mtp_params`.
- Modifying `llama_decode_internal` (and `encode`) to handle MTP operations (Warmup, Update, Draft).
- Adding public APIs for MTP state management (`llama_set_draft_input_hidden_state`).
- Adapting the embedding extraction logic to skip MTP update passes.
* Refactors `server_slot` to support generic speculative decoding (MTP or Draft Model).
* core: enable hybrid outputs (logits + embeddings) for MTP support
* fix(mtp): correct KV-cache slot finding for updates
* fix(mtp): persist hidden states to prevent context corruption during drafting
* refactor(mtp): clean unused code
* fix(mtp): update server to new functions name
* fix(mtp): fix graph and save hidden state
* mtp: refactor integration, context params and kv cache search
* mtp: fix hidden state extraction and speculative acceptance flow
* server: fix MTP warmup for long prompts and reset token buffer
* llama: refactor MTP operation state to context parameters
* server: fix n_past calculation in MTP acceptance
* llama: fix mtp enable flags
* speculative: refactor MTP to use common_speculative interface
* context: remove unused signatures
* clip: fix deprecated enum-enum conversion warning
* common: fix format string crash in help message
* context: fix mtp activation logic
* raw parameters.md
* fix small typos in common.cpp
* Update build args in parameters.md
* Update parameters.md
- format as table
- sections
* Update README.md
- quickstart
- build and run
* Update parameters.md
other tools examples
* add PR links
* multiple updates to parameters.md
- description
- add jargon section
- add suggestions from feedbacks
* don't imply that only linux is supported in README.md
* add alias to parameters.md
* Update README.md with recent models and features
* Update parameters.md with latest features
* address suggestions
- no-ooae
- placeholder for common commands
- no-kv-offload
- llama-sweep-bench
- placeholder for unique parameters
* specify Linux distro in README.md
* qwen3next: add architecture support and recurrent-state fixes
* qwen3next: optimize broadcast sub and single-seq ssm conv
* cuda: build MoE row mapping on device in mul_mat_id
* cuda: add guarded multi-seq fast path for ssm_conv
* docs: update qwen3next perf report for cuda MoE/SSM tuning
* cuda: reduce qwen3next moe/ssm sync overhead and refresh eval
* qwen3next: split cpu/cuda eval builds and tune PP scheduling
* qwen3next: harden seq-state flow and support optional dense FFN layers
* qwen3next: trim delta-net graph overhead in chunking path
* qwen3next: remove redundant v_conv cont in delta path
* qwen3next: avoid extra cont on linear attention output
* qwen3next: drop redundant cont before recurrent state flatten
* qwen3next: keep recurrent state in 4d layout through delta path
* qwen3next: add fused delta-net op and wire model path
* tests: add backend-op coverage for ggml_delta_net
* qwen3next: add runtime switch for fused delta-net path
* docs: refresh qwen3next perf review and benchmark matrix
* qwen3next: default fused delta-net off and document quality checks
* qwen3next: add decode-only fused delta mode
* qwen3next: make fused delta safe by default and fix fused tensor layout
* qwen3next: warn when forcing fused decode mode
* qwen3next: add fused-delta regression runner script
* qwen3next: integrate fused regression into eval harness
* qwen3next: clean up chunked delta-net shape handling
* qwen3next: add absolute sanity guards to fused regression
* qwen3next: add unified regression runner script
* qwen3next: disable flash-attn for cpu-only contexts
* docs: reconcile qwen3next status and remaining upstream gaps
* common: add qwen3next fused-delta runtime flag
* cuda: add qwen3next delta-net kernel dispatch override
* docs: update qwen3next quality and serving baseline findings
* qwen3next: keep fused delta on safe path and remove PR artifacts
* qwen3next: align autoregressive delta-net decode layout
* Revert "qwen3next: align autoregressive delta-net decode layout"
This reverts commit 9241164a5e.
* cuda: port solve-tri fast-paths for qwen3next delta-net
* qwen3next: add fused-delta runtime flag and drop env toggle
* qwen3next: make fused delta single-flag and default on
* Account for GPU arch differences
* Revert "cuda: build MoE row mapping on device in mul_mat_id"
This reverts commit 89e9ecfa84.
* qwen3next: drop non-essential MoE scheduling and split heuristics
* qwen3next: avoid generic ggml_sub broadcast changes
* llama: restore only_active_experts log message
* Remove unnecessary hacks, disable fusion for now.
* qwen3next: port hybrid recurrent state memory semantics
* qwen3next: clean up recurrent state slot plumbing
* qwen3next: fix hybrid V-cache layout plumbing
* qwen3next: guard recurrent state slots against kv capacity
* qwen3next: persist recurrent state in session data
- serialize/restore qwen3next cache.s_l in state/session paths\n- bump session and sequence-state file versions for format change\n- fallback to single-token chunking for mixed repeated seq_id batches
* qwen3next: drop unused fused-delta builder path
- remove dead build_delta_net_fused lambda\n- remove unused llm_build_context::fused_delta member
* qwen3next: remove unused fused-delta CLI/context plumbing
- drop -fd/-no-fd options and related YAML dump field\n- remove fused_delta fields from public/internal context params\n- remove fused_delta assignment and logging in context init
* ggml: remove unused DELTA_NET operator stack
* Missing include
* Reorder ops/unary ops
So we don't change again the enum values of the mul mat ops
* Minor
* Discard unnecessary changes in llama-build-context.cpp
* Minor
* Revert "Discard unnecessary changes in llama-build-context.cpp"
This reverts commit edadb80ed6.
* Increase GGML_SCHED_MAX_SPLITS - required for larger u-batches
* Fix CPU concat in the TG case: 7.25 -> 10.5 t/s for Qwen3Next
* Fix CPU sum_rows: 10.5 -> 13.6 t/s for Qwen3Next
It was single-threaded and was taking ~25% of the computation time
during TG. It is now down to 2%.
Strangely enough, I measure 13.6 t/s with llama-bench, but if I
let the model give me an actual response with llama-cli, I get close
to 17 t/s.
* Fix CPU scale: 13.6 -> 16.7 t/s for Qwen3Next
For Qwen3Next there is a scale op on a largish tensor (548k elements)
that has a single row for TG, so was done in a single thread.
We now simply use blocks of 1024 elements.
* Optimize CPU mul: 16.7 -> 17.6 t/s for Qwen3Next
* CPU: fuse transpose -> cont -> sum_rows -> transpos: 17.6 -> 23.1 t/s for Qwen3Next
* Optimize CPU repeat: 176 -> 200 t/s for Qwen3Next PP-512
* Multithreading for OP_SUB
* Don't commit with timing trace on
* Multithread neg and sigmoid
* Be able to turn on/off fusion more easily (CPU)
* Name the mul_mat ops so we know where the time goes
* WIP
* Much better PP on CUDA
* CUDA: fuse transpose -> cont -> sum_rows -> transpose
Needs non-coontiguous variant of sum_rows.
On the CPU this gave 30+% improvement in TG performance,
on CUDA ist is disapointing 6-7%. I guess, this is because
Georgi's cont CPU implementation was so bad that skipping
it made such a big difference.
* CUDA: faster mul for special case relevant for Qwen3Next
Worth 1% in TG
* Fix CPU OP_CONT
---------
Co-authored-by: yurko <yurko@local>
Co-authored-by: Yurko <yurko@example.com>
Co-authored-by: yurko <yurko@pop-os.tail5a1a6b.ts.net>
Co-authored-by: Yurko Hoshko <YurkoHoshko@users.noreply.github.com>
* spec : add self speculative decoding and ngram-mod and refactor
common : use common_ prefix for common library function
llama : use LLAMA_TOKEN_NULL
spec : add self speculative decoding (no draft model required) + refactor
spec : add ngram-mod
spec : various improvements ton ngram-map + docs
spec : fix the check-rate logic of ngram-simple
common : add common_speculative_is_compat()
spec : simplify time measurement using common_time_meas
refactor common_sampler_init
refactor common_token_to_piece
refactor and fix cur_p bug
clean up
* spec : remove check rate
* spec: show warnings instead of abort
---------
Co-authored-by: firecoperana <firecoperana>
Co-authored-by: Sascha Rogmann <59577610+srogmann@users.noreply.github.com>
* adaptive_p: fix history update + use current probability for high temp
* adaptive_p: fix history update bug, update with current probability if temp is high
* replace temp-as-signal with server argument
* adaptive_p: rename ema_w_cur_p to updt_w_cur
* delete test code
* WIP - not working
* WIP - not working
* WIP - GPT-OSS working
However, extremely stupid. The only way I could correctly repack the
up/gate experts is to copy up and gate into host buffers, repack
into another host buffer, copy back into the ffn_up_gate_exps tensor.
This is going to be very slow for giant 500 GB models.
My attempts to do this via a compute graph on the backend holding
the tensors was unsuccessful.
For GPT-OSS-20B I see ~6-7% better PP when using the original
ik_llama.cpp fused_up_gate CUDA implementation, and ~10% when
using the small batch size implementation.
Other models are not working yet on CUDA as I need to fix the
fused mul-unary implementation.
* WIP
* WIP - Qwen3-MoE (and hopefully all others) working
But when I say here and in the previous commit "working",
I mean PP is working. TG is still broken.
* WIP: TG seems to be working
* Minor
* Add command line option to merge experts up/gate
* Add merge up/gate command line parameter to llama-bench
* Turn off merge_up_gate_exps if split mode graph
It is not yet implemented
* When no bias, allow merging up/gate with tensor overrides
* Arghh, we need to increase the context size again
* Cleanup
* server: improve speed of speculative decoding
change logs
rpc: add recompute
spec dec fix
* Fix n_batch_size not set to context size for draft model
---------
Co-authored-by: firecoperana <firecoperana>
* WIP: absorb adding input into std_attn and std_ffn
* WIP: NCCL infra
* WIP: add reduce and fake_cpy ops
* WIP
* WIP: graph appears to work, layer is broken
* WIP: Qwen3-MoE works with graph, layer still broken
* WIP: GLM-4.5 graph works
* WIP: fix sm layer (dense)
* WIP: fix sm layer (MoE)
* WIP: fast PP with bespoke 4-GPU NCCL
I guess, I'm not using NCCL the right way as PP is very
low with a single communicator group for 3 or more GPUs.
But if I create 4 communicator groups for pairs of GPUs
(0,1, 2,3, 0,2, 1,3) and use that, PP is fast: I'm hitting
1500 t/s for L3-70B on the 4x3090 system, which is
~20% better than the previous sm graph without NCCL.
But that cannot be the solution (I cannot be creating pairwise
communicators and associated logic for every possible number of GPUs).
* WIP: Cohere2
* Explicitely set device
* Bespoke 3-GPU case
* WIP
* Do not repeat get_rows multiple times
* Fix 3 GPUs
* OK, let's leave it in
* Simple async
* This sync seems enough
* Only do async for 4 or more backends
With 2 GPUs (so, 3 backends) not using async is slightly faster
* Scheduler changes
* Use OpenMP if available
Surprisingly (at least to me), this is quite a bit faster than
std::thread and std::barrier. GLM-4.5-AIR with 4 GPUs is now
at 105 t/s at zero context!
* Do not use OpenMP if there are tensor overrides
* Set omp max active levels
* Be more careful with having set the device before using a stream
* Command line option to turn on async. Set to false by defualt for now
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Use -smgs or --split-mode-graph-scheduling in CLI to bypass the disabling of split mode graph scheduling when tensor overrides is used.
Co-authored-by: Kawrakow <iwankawrakow@gmail.com>
* This should do the trick for PP
* Command line option to set max. extra VRAM that the scheduler can use
* Fix bug and cleanup
* Looks like with this change it is working with tensor overrides
* Nah, it is not working
* OK, this seems to be working
* Disable split scheduling with tensor overrides
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
