Extends --n-cpu-moe to accept a fractional layer count. The integer part
offloads whole layers as before; the fractional part offloads a subset of the
boundary layer expert tensors (gate, then up), keeping down_proj resident.
This realizes ATSInfer tensor-granularity static placement, giving sub-layer
control over expert VRAM residency. Placement-only, lossless.
Assisted-by: Claude
Add the flash-attn mask_opt change (auto-on for GCN large head sizes,
lossless, +12% pp @ 32k on the RX 580) and correct the earlier pinning
advice: pinning helps in isolated llama-bench but fails in a long-running
server on RADV, so --no-mmap is the serving path. Add a recommended
Polaris serving command.
Assisted-by: Claude
mask_opt was disabled on AMD GCN for head sizes <= 256, but it is
beneficial there in high-context prefill: on fully-visible causal
blocks it skips the per-block mask load+add, and it skips fully-masked
blocks entirely. The attention op on GCN is compute-bound on the
softmax path (no matrix cores), so this cuts real work.
Verified lossless (perplexity bit-identical with it on vs off) and a
measured prefill win on Qwen3.5-35B-A3B (head_dim 256) on an RX 580:
pp2048 unchanged at short context, +8% @ 16k, +12% @ 32k, growing with
depth. Enable for GCN when HSK/HSV >= 256; the existing large-mask
conditions keep it off for decode.
Assisted-by: Claude
Pinning helps on Vulkan too, but GGML_SCHED_PREFETCH_EXPERTS regresses
there (second backend shares one device queue, no overlap). Add an RX 580
Polaris benchmark and the context-dependent --n-cpu-moe guidance.
Assisted-by: Claude
Pinned async host uploads can record into compute_ctx before graph
compute, so the perf logger's opening timestamp must append after them
instead of asserting the context is expired. Without this, profiling
(GGML_VK_PERF_LOGGER) crashes whenever host-register pinning is on.
Assisted-by: Claude
VK_EXT_external_memory_host import fails on RADV for file-backed mmap pages,
so register_host_buffer no-op'd and every MoE-expert upload paid a slow
single-threaded pageable memcpy into the staging buffer each eval (~3 GB/s
effective on the 35B, ~25% of PCIe bandwidth).
When the import fails, fall back to a one-time copy of the region into a
host-visible Vulkan buffer registered in device->pinned_memory under the
mmap's address range. The existing pinned fast path then DMAs straight from
that buffer each eval at full PCIe bandwidth - no per-eval CPU memcpy and no
blocking sync. Costs one-time host-visible memory equal to the CPU-resident
weight region.
Assisted-by: opencode
The generic concat shader reads a transposed source (nb[1]==type_size) with an
uncoalesced stride, which is catastrophically slow on discrete GPUs (~5.6ms for
a 16MB concat on the RX 580 vs ~130us of memory bandwidth), a ~9% prefill
hotspot on Qwen3.5-4B (delta-net state concat).
Add a fast path for concat along dim 0 where one source is stored transposed and
the other source + dst are contiguous along dim 0: copy the contiguous source
with copy.comp and transpose the other source into the matching dst sub-region
with the existing tiled copy_transpose shader (shared-memory 32x32 transpose,
coalesced read+write). Reuses pipeline_cpy_* / pipeline_cpy_transpose_* with
custom push constants + doffset, no new shader. Falls back to the generic path
otherwise (gated on type/shape/contiguity + 16-bit doffset bound).
Assisted-by: opencode
Export register_host_buffer/unregister via the backend reg so the existing
GGML_CUDA_REGISTER_HOST path in llama-model-loader pins mmap'd expert
weights on Vulkan. Imports host pages through VK_EXT_external_memory_host
into device->pinned_memory, letting the existing pinned fast path in
ggml_vk_buffer_write_2d_async DMA straight from system RAM instead of
bouncing through the staging buffer + blocking host memcpy.
A single Vulkan buffer cannot cover a multi-GB mmap (capped at
device->max_buffer_size), so the region is imported in page-aligned
chunks; a bound check makes tensors straddling a chunk boundary fall back
to staging instead of reading out of bounds. Opt-in via
GGML_CUDA_REGISTER_HOST=1 or GGML_VK_REGISTER_HOST=1, no-op otherwise.
Assisted-by: opencode
Profiling showed the 2-slot rotation stalls ~5.5ms per layer waiting for
the down_exps upload: 3 tensors per MoE layer need 3 slots for a full
layer of lookahead. Default is now 3 (one layer), configurable via the
env var value, degrading to however many slots fit on OOM.
Also fixes a use-after-free: on a failed slot regrow the repointed
staging tensors kept dangling pointers into freed device memory, which
graph reuse carried into later evals. Staging repoints are now restored
right after kernel launch, slots are sized once from the graph max, and
allocation happens before freeing.
Qwen3.6-35B-A3B pp2048 on RTX 3060: 1643 -> 1880 t/s (mainline: 1143).
At large batch sizes virtually every expert is used, so the per-layer
routing-ids readback that mainline waits on buys nothing while forcing a
full device sync per expert tensor (3x per MoE layer). Above a batch
threshold, upload the full expert tensors through a second backend
instance on the same device (own stream) with two event-ordered staging
slots, so uploads for tensor N+1 overlap compute of tensor N.
Qwen3.6-35B-A3B pp2048 on RTX 3060 (-ncmoe 26, ub 2048): 1383 -> 1663 t/s.
Generation output verified token-identical; decode path unaffected.
Opt-in via GGML_SCHED_PREFETCH_EXPERTS=1.
Wire the existing GGML_CUDA_REGISTER_HOST path back up: after model load,
cudaHostRegister the mmap pages backing weights kept in system memory.
Recovers pageable-copy losses when MoE experts are streamed to the GPU
during prefill (n-cpu-moe): Qwen3.6-35B-A3B pp2048 1144 -> 1385 t/s on
RTX 3060. Opt-in via GGML_CUDA_REGISTER_HOST=1, unchanged otherwise.
* add common/subproc.h|cpp
* add compile flag LLAMA_SUBPROCESS
* disabled by default on android and ios
* test-jinja: use common subproc
* mtmd: disable video if subproc is not set
* disable subproc on wasm
* make is_created atomic
* migrate server-mcp
* Add preliminary MiniMax-M3 support
Text-only port that re-uses existing components: MiniMax-M2 style GQA with
per-head QK-norm and partial rotary, DeepSeek-V3 style leading-dense and
routed/shared experts, and swigluoai activation. Sparse attention is not
yet supported (dense fallback); vision tower and MTP heads are dropped.
* MiniMax-M3 vision tower (mmproj + clip graph)
* Delete m3_vision_ref.py
* Update clip.cpp
* MSA
* Update constants.py
* Update minimax.py
* Cache creation. Working withotu flash attention
* Added flash attention for sparse layers
* Decomposed slow cpu OP into GPU + CPU ops. Massive speedup over long ctx
* Rewrote indexer op to be cuda native. Modified flash attention to match per group block picking
* Implement sparse attention calc out of stock ops.
* Fix a cache allocation and cont issue
* Fixed -fa auto crash, flagged debug spots
* Delete vocab.json
* Delete model.safetensors.index.json
* Delete generation_config.json
* Delete Minimax directory
* Handled multi stream case to fall back on Dense Attention
* Development scaffolding cleanup. No functional change to the decode or
4-way paths. Full debug harness remains at <8136a9c68ed7a5eb009aa67bba3fda8062f4648f> for reproducing the
selection-parity validation.
* Remove redundant comment from minimax-m3.cpp
* Changed 3 Gelu Ops for vision into Gelu_erf ops
* Assert that n_kv is multiple of 128
* Rename MSA index tensors to indexer convention
Note: All GGUFs generated before this change will need to be regenerated.
* Fix incorrect Assert
* Review driven changes (#3)
* Remove comment from conversion minimax.py
Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com>
* Remove whitespaces from constants.py
Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com>
* Tighten comment in minimax.py
Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com>
* inherit MiniMax-M3 from MiniMax-M2
* drop dead text_config fallbacks
* Add indexer writer methods
* Reuse LLM_FFN_SWIGLU_OAI_MOE
* Remove duplicate indexer setters, add only block_size/local_blocks, follow value naming convention
* Fix conversion error /gguf_writer.py
Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com>
* Update gguf-py/gguf/gguf_writer.py
Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com>
* Update gguf-py/gguf/tensor_mapping.py
Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com>
* Update conversion/minimax.py
Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com>
* Update conversion/minimax.py
Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com>
* Remove whitespace in src/llama-kv-cache.cpp
Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com>
* Remove Whitespace in Update src/llama-model.h
Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com>
* Remove whitespace in src/llama-hparams.h
Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com>
* remove multimodal code upon maintainer request. Will be made as a separate PR
* Whitespace clean in tensor_mapping.py
* Log cache size on launch, block ctx shift, support prompt caching
Log indexer cache size on launch
Disallow ctx shift
Support prompt caching
* Update minimax-m3.cpp
* Optimize implementation, add multi stream support.
Fully rewrote minimax-m3.cpp for speed and buffer size gains:
Unified the 4-way + decode, 1 FA call per layer instead of 4, with the groups mapped onto ne[3]
Custom CPU op now emits block-level mask, expanded on GPU, which causes CPU to GPU transfer to shrinks at prefill
Decode: ~25 nodes/layer vs ~50, no per-group concats/conts
Unified selection semantics, so both regimes rank bs + local bias (position-anchored local force), which means prefill/decode can no longer disagree on selection
can_reuse on the MSA bias input. Graph reuse at decode restored (was rebuilding the full graph every token)
In-place mask adds, shrinking compute buffer ~6.8 to ~4.2 GiB at ub2048/62k
Multi-stream: MSA now runs with -np N when kv_unified=false. Decode stays batched across streams (still 1 FA call), prefill loops per stream. dense fallback only for --kv-unified + multi-seq
Measured effect on expert offload bound setup: decode 6.2(4WAY)–7.15(MSA_decode) -> 7.7~7.8 t/s, flat from 5k to 60k+. prefill around 10% faster. buffer about 20% smaller, multi-user support.
* set default cache type to F32
* Fix potential DSA double indexer cache allocation bug, only allocate in-cache k_idx for archs that opt in
* remove F16 downcasts in MSA attention, force F32 indexer score accum
* Add Minimax eos to llama vocab
* Guard edge case where idx cache can become stale after a tail trim
* Update llama-kv-cache.h
* Update llama-kv-cache.cpp
* Update llama-kv-cache.cpp
* Update llama-kv-cache.h
* Update llama-kv-cache.cpp
* Review driven changes
* style fix
* indexer hparams are required
* fix tests
* fix lint
---------
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com>
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
The context gauge card starts monitoring like the dial does, because
its own processing state instance only follows the live stream while
its monitoring flag is set. It also gets back the text-sm and ring
classes the removed hover card wrapper used to inject, which restores
its layout. Routing to a conversation now lands at the bottom
instantly and keeps the pin one frame at a time until the page height
settles, since content-visibility size realizations and syntax
highlight passes grow the page without DOM mutations.
* Fix emscripten compilation with openmp
* Separate wasm job to its own workflow
* Add flags necessary for newer emsdk
* Just disable openmp
* Update triggers
* move server_pipe to common
* init impl
* vendor: update subprocess.h
* add server_mcp_stdio
* stderr drain
* server_mcp_transport
* server_mcp_stdio is now framing-only, no json
* internal/mcp-stdio: integration + tests + fixes (#26075)
* server-mcp: harden transport and wire up the tool integration
Builds on the transport/manager architecture (server_mcp_transport + server_pipe)
with the hardening and integration the draft did not yet have.
Hardening:
* Reader and stderr pumps are polled (running-aware) instead of blocking on a read
that only ends at EOF. subprocess_terminate() SIGKILLs only the direct child, so a
grandchild the MCP server spawned that inherited the pipe would otherwise keep the
write end open and hang teardown (both warmup shutdown at startup and process
shutdown). The writer is likewise non-blocking + polled.
* Windows: resolve the command through PATHEXT so "npx" (npm ships npx.cmd, never
npx.exe) spawns, matching POSIX's PATH search; and enumerate the parent environment
as UTF-8 (GetEnvironmentStringsW) instead of the active code page.
* server_pipe gains an opt-in max_size (default unbounded, so the router's streaming
use is unchanged); the MCP reply queue uses it so a server that streams unsolicited
notifications between requests cannot grow it without bound.
Integration:
* --mcp-servers-config / --mcp-servers-json flags; enabling MCP restricts default CORS
to localhost, same as --tools.
* MCP tools are exposed through /tools (and chat-completions) as <server>_<tool>,
skipping names that collide with a built-in or another MCP tool.
* Manager lifecycle wired into llama_server(): warmup at start, shutdown() from the
signal handler before the HTTP server drains, blocking teardown in clean_up().
* SIGPIPE ignored so a child dying mid-write yields EPIPE rather than killing us.
Assisted-By: Claude Opus 4.8 <noreply@anthropic.com>
* server-mcp: add MCP test suite with grandchild deadlock regression test
21 tests over the /tools endpoint: tool discovery/invocation, timeouts, crash
recovery and respawn cooldown, warmup partial failure, malformed and batched
notification+response output, tool-definition shape, and prompt shutdown during a
slow call.
The last test spawns an MCP server that leaves a grandchild inheriting its
stdout/stderr and asserts the server both starts and stops promptly. Verified it
fails (5s SIGKILL fallback on a deadlocked reader-join) when the pump is made to
ignore the running flag, and passes with the polled reader.
Assisted-By: Claude Opus 4.8 <noreply@anthropic.com>
* clean up
* clean up 2
* even stricter life cycle
* nits
* nits 2
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* fix some edge cases
* fix last_error data race
* fix response schema + docs
* server: fix MCP zombie leak and timeout-induced transport teardown
join_pumps() never reaped the child, leaking one zombie per spawn:
call subprocess_join() before subprocess_destroy().
A per-call timeout permanently closed from_server and got a healthy
transport evicted: add close_on_stop to server_pipe::read() and pass
false from send_rpc(), where should_stop is a per-request deadline
and a late reply is already skipped on id mismatch.
Also drop the unreachable disconnect cancellation in
server_mcp_tool::invoke(): support_stream is false, st is always null.
(cherry picked from commit e6de1ec043174fd0570b1e60d47f06c7c19d620d)
Assisted-by: Claude Opus 4.8
* server: make MCP test fixtures JSON-RPC 2.0 compliant
Add the missing notification guard to mcp_malformed_server.py and
mcp_burst_server.py (the latter treated id 0 as a notification and
replied to unknown ones; its notification table is now unused).
Return -32602 instead of -32601 for unknown tools: tools/call is a
valid method, the tool name is the invalid parameter.
Also fix the test module docstring: tools are named <server>_<tool>.
(cherry picked from commit 74a08e8c311dabf3b49d06cc6d754b0097ae7a38)
Assisted-by: Claude Opus 4.8
---------
Co-authored-by: Piotr Wilkin (ilintar) <piotr.wilkin@syndatis.com>
Co-authored-by: Pascal <admin@serveurperso.com>
The INI parser creates an implicit default section for top-level metadata.
After reserved keys such as `version` are skipped, that section can have no
model options but was still added and exposed in router mode.
Skip only the empty implicit default while preserving real default presets,
named presets, and the global `[*]` settings.
Signed-off-by: JS van Dijk <267467744+hogeheer499-commits@users.noreply.github.com>
Co-authored-by: JS van Dijk <267467744+hogeheer499-commits@users.noreply.github.com>
These two parameters were overlooked when task parameters were being JSONized
within `generation_settings` and have been added. A regression test has been
added to prevent the problem from recurring and it passes.
Fixes#25803
* common : extract trie/ac to a separate file
* common : support multiple token sequences in the reasoning budget sampler
* common/trie : return matched word index
* common/trie : rename "word" to "pattern"
* common/reasoning-budget : expose matched end sequence
* common/sampling : replay end sequence when reasoning budget is done
* cont : update to use multiple end sequences
* cont : clean up
* performance harness - the empirical root
Assisted-by: Claude Opus 4.8
* 210.36ms -> 2.67ms per streamed token
Assisted-by: Claude Opus 4.8
* 11.58ms -> 0.62ms per streamed token
Assisted-by: Claude Opus 4.8
* 22.02ms -> 3.33ms per streamed token
Assisted-by: Claude Opus 4.8
* 3.07ms -> 1.36ms per streamed token at 40 messages
Assisted-by: Claude Opus 4.8
---------
Co-authored-by: Zach Winter <dmtommy@icloud.com>
* ui: remove viewport fade in and smooth autoscroll bottom snap
fadeInView mounted every message and markdown block at opacity 0 and
relied on an IntersectionObserver to reveal it. When the observer never
fires (blocks mounted offscreen during long agentic loops) the content
stays invisible forever while still present in the DOM. Remove the
action, its orphaned isElementInViewport util and all call sites:
blocks now render visible immediately.
AutoScrollController.scrollToBottom defaulted to behavior smooth and is
invoked every 100 ms while streaming. Each tick restarts an easing
animation toward a moving scrollHeight, producing a random elastic bump
of a few pixels when the user reaches the bottom and autoscroll
reengages. Default to instant scrolling; the user facing scroll down
button keeps its smooth behavior.
* ui: skip rendering of offscreen chat messages via content-visibility
Apply content-visibility auto with contain-intrinsic-size to chat
messages so the browser skips layout and paint for messages outside
the viewport. The DOM stays complete: component state, find-in-page,
text selection, and the mutation based autoscroll are unaffected, and
browsers without support simply ignore the properties.
* ui: remove conversation switch fade
Switching conversations faded the message list out and in over 500 ms
plus a 300 ms route delay, deferring the message refresh behind two
requestAnimationFrame calls. Remove the fade, its navigation hooks and
dead state, and refresh messages directly so switching is only bound
by actual render time.
* ui: describe present behavior in comments and drop unused parameter
* ui: anchor the context gauge popup to the form with plain CSS
The stats card was portaled to body and repositioned in script on
every ancestor scroll event, trailing the page by one frame while
streaming. Render it as an absolutely positioned sibling of the input
box inside the already relative form, so nothing runs during scroll.
The card sits just above the dial, centered on it and overlapping the
textarea, from a single measurement of the dial center and top taken
when it opens; the dial and the card share the same positioning frame,
so the values stay exact while the card is open. Mouse pointers open
on hover with a grace delay to reach the card, touch pointers toggle
on tap, any press outside the card and the dial closes it, and Enter
and Space toggle from the keyboard. The card lives outside the input
box because its overflow-hidden and backdrop-filter would clip any
positioned descendant.
* ui: extract context gauge popup constants and relocate its state store
Move the placement values and the close grace delay to
lib/constants/context-gauge-popup.ts, matching the auto-scroll
constants layout, and move the popup state module from the component
folder to lib/stores where runes modules live in this codebase.
* ui: declare the context gauge popup card ref as $state
* Start building graph - reuse deepseek32
* Enable kv cache and rotation for glm_dsa architecture
Just follow Deepseek 3.2 for now.
* Reuse prev_top_k for "shared" indexer layers
* GLM 5.2 uses LLAMA_ROPE_TYPE_NORM for the indexer.
This is transformers' `apply_rotary_pos_emb_interleave`
* Default indexer types to GLM pattern
Previous converted GGUFs like https://huggingface.co/unsloth/GLM-5.2-GGUF write indexer weights to _all_ layers, even if they are only required for "full" types. This PR relies on a new key "%s.attention.indexer.types"; if absent, it will use the default GLM 5.2 schedule as defined in https://huggingface.co/zai-org/GLM-5.2/blob/main/config.json#L26.
Note that conversion is not saving this key yet.
* Save indexer types to gguf, restore on load
* Use ggml_lightning_indexer when cparams.fused_lid
Co-authored-by: fairydreaming <166155368+fairydreaming@users.noreply.github.com>
* GLM 5 and 5.1 use full indexers
Co-authored-by: fairydreaming <166155368+fairydreaming@users.noreply.github.com>
* Fix indentation
* Ensure array is zero-filled
* Prefer explicit std::fill
* Assert prev_top_k exists for shared indexer
---------
Co-authored-by: fairydreaming <166155368+fairydreaming@users.noreply.github.com>
* ui: fix MCP server display name conflicts in tools lists
Tool groups were keyed by display label so two servers reporting
the same name broke the keyed each blocks and only one was visible.
Key rendering, expand state and toggles by the stable server id
instead, and suffix duplicate labels with a counter in config order.
* ui: customizable MCP server display name with autofill
Add a display name field to the MCP server form, add and edit alike.
The custom name takes precedence over the server-reported one, so two
servers reporting the same name can be told apart; clearing the field
returns to the automatic label. In the add dialog a debounced preview
handshake prefills the field with the server-reported name: a manual
edit freezes the autofill, stale responses are discarded, failures
stay silent, and an unedited prefill is not persisted so the label
keeps following the server.
* ui: fix recursive fetch passthrough in the client test setup
The original fetch was captured inside beforeEach, where it is the
previous test's spy since vi.spyOn returns the existing one, so the
default passthrough recursed on itself for any URL outside the
mocked set. Capture the real fetch once at module load.
* support "reasoning_effort": "none" in OAI API
* handle reasoning.effort: "none" in OAI responses API
* clarify non-"none" values of reasoning_effort have no effect
* use json_value instead of body.at
* hex-l2: use dirty ranges for flushing
* hex-l2: simplify range based flush logic
* hex-l2: optimize dirty range scans
* hex-hvx: support for reduce_max_i32
* hex-mm: optimize fused MUL_MAT+ADD to use vtcm for bias when it fits
* hex-mmid: optimize mmid row-mapping generation
* hex-mmid: optimize mmid row-mapping generation
* hex-mmid: optimize mmid row-mapping generation (round2)
* hmx-mm: optimize output proc by tiling (col-chunking)
* hex-fa: start the next q dmas a bit earlier
* hex-fa: prefetch Q even earlier
* hvx-fa: optimize softmax to keep things in hvx registers
* hex-fa: hoist const register init in softmax loop
* hmx-fa: kick off next-qkv DMAs before o-proc
* hmx-fa: hoist various checks out of the inner loop
* hmx-fa: adjust the cost model to better balance softmax work across hvx threads
* hmx-fa: overlap diag rescale build with last HMX task
* hmx-fa: optimize idx update in output proc
* hmx-fa: unroll the softmax loops for improved perf
* hmx-fa: overlap qk-dot with softmax, double-buffer p and s tiles
* hex-trace: double the default number of trace entries
* hex-trace: add trace events for opbatch and buffer mgmt
* hex-trace: overhaul tracing to simplify runtime event handling and support opbatch stats
* hex-trace: replace ascii timeline diagram with pipeline bubbles detector
* hex-trace: handle missing start/stop events
* hex-dma: always log stop/start trace events even for dummy dmas
* hex-scripts: fix flake warnings
* ui: add Default reasoning option that defers to the server
The webui always injected enable_thinking, overriding the chat template
default and the --reasoning flag, breaking models that reason
unconditionally (e.g. Gemma 4 E4B) on a fresh client.
Default sends nothing so the server decides, Off and effort levels
force the value as before. All choices are remembered.
Also remove the boolean thinking API from the conversations store and
drop ChatFormReasoningEffortSubmenu.svelte (dead code).
* ui: close the whole menu tree on reasoning level selection
The reasoning levels were raw buttons inside the SubContent, so
selecting one only closed the submenu via manual state while the root
dropdown stayed open. DropdownMenu.Item closes the full tree on select
like the sibling entries and brings native keyboard navigation.
* ui: prevent the add menu tooltip from flashing when the dropdown closes
Lumpiasty
Anirban KarandLumpiasty