Raise the threshold for minimum buffer size from 1 GiB to 4 GiB, based
on real-world experiments of overcommitting device memory with model
weights larger than available VRAM, for example Qwen3.5-35B-A3B-Q8
running on a B70.
Also add a debug message to better track USM system allocations.
Signed-off-by: Francois Dugast <francois.dugast@intel.com>
* [SYCL] F16 (default) Flash Attention with XMX engine via oneDNN graph API; Qwen3.6-27b-Q8_0 prefill speed up x1.21 at p=512 and x4.26 at p=80k
* [SYCL] Address review on FA oneDNN path. Result: llama-bench---pp512; 32% increase with fa1; llama-perplexity---0.11% difference; tested model: mradermacher/Meta-Llama-3.1-8B-Instruct-Q8_0.gguf
* PR-25222 revision v2: addressed audits
* [SYCL] flash-attn oneDNN SDPA KV F16 rev 3.0: add BMG gate + multi-device sync. Narrow the scrope of this PR to Battlemage only (bmg; Xe2). Other archs (e.g., alchemist) fall back to existing FA kernel. When device_count >1, apply stream -> wait_and_throw(), validated working path for multi-gpu sync fix by @maxious.
Co-authored-by: maxious <81432+maxious@users.noreply.github.com>
* updated comment on bmg gate, noted the issue
---------
Co-authored-by: scientist3 <scientist.3@users.noreply.github.com>
Co-authored-by: hmscider <hmscider@users.noreply.github.com>
Co-authored-by: maxious <81432+maxious@users.noreply.github.com>
The f16 GEMV kernels take a vectorized path for ne00 >= 128 that casts the row
pointers to half4 or float4. When the row stride is not aligned, the wide load
becomes misaligned. On devices that require natural alignment for vector loads,
the kernel reads garbage. This is the case Intel GPUs and the kernels produce
incorrect results there. Adreno happpens to be byte addressable and the kernels
happen to work.
* server : clear checkpoints upon prompt clear
* server : move the prompt state data to the server_prompt_cache
Assisted-by: pi:llama.cpp/Qwen3.6-27B
* server : handle batched slot being cleared
* opencl: do not fail backend init on devices without cl_khr_integer_dot_product
* opencl: do not call dp4 kernels when dp is unavailable
---------
Co-authored-by: Li He <lih@qti.qualcomm.com>
* ui: fix MCP panel regressions after settings rework
Restore the llama-server proxy switch in the Add New Server dialog.
The dialog never passed useProxy/onUseProxyChange to McpServerForm,
which only renders the proxy switch when the handler is provided.
The flag is now wired, persisted on addServer, and reset on close.
Bound the MCP connection handshake with the configured timeout.
handshakeTimeoutMs was set in the server config but never consumed.
The SDK timeout only covers the initialize request, not
transport.start(), which can hang forever on an unreachable host.
The whole handshake now races against the timeout and closes the
transport on expiry so the underlying fetch or socket is aborted.
Keep disabled MCP servers visible in management and chat-add UIs.
Collapsing mcpDefaultServerOverrides into mcpServers[i].enabled turned
the visibleMcpServers enabled filter into a visibility trap: toggling
a server off outside a conversation hid it from every surface with no
way to re-enable it. The filter is dropped, tools derived from health
checks still skip disabled servers, and the settings page and server
card render the real card instead of a skeleton for disabled servers
that never receive a startup health check.
* ui: clarify MCP server list semantics and add regression test
Remove the visibleMcpServers getter, a filterless alias of getServers
whose name invites the next refactor to put a filter back. Call sites
read getServers directly, the duplicate list in the chat submenu is
merged, and the misleading local variable in the sheet is renamed.
A parser unit test pins the invariant: enabled is an on/off state,
never a visibility filter, so disabled servers stay listed and
toggleable.
* ui: apply the MCP request timeout setting live to all servers
The per-server requestTimeoutSeconds field was never editable in any
UI and froze the global setting at server creation time, so changing
the timeout in Settings was a no-op for existing servers. The field
is removed from the data model and parsers, the timeout is read live
from the global setting wherever a request config is built, and the
misleading "Can be overridden per server" help text is dropped. A
parser unit test guards against reintroducing the stored field.
* ui: move the MCP request timeout into the Agentic settings section
The MCP section held a single setting. The timeout is a global tool
execution parameter like the other Agentic entries, so it moves there
and the section is removed. Same settings key, no migration needed.
* ui: remove the dead tool preview lines setting
The agenticMaxToolPreviewLines setting was read into AgenticConfig
and consumed by nothing: the agentic loop only uses enabled and
maxTurns. Its help text described a previous architecture where only
truncated previews and the final response survived the loop; tool
results and intermediate turns now persist as full DB messages, so
the setting had no effect at any value. Stale keys in localStorage
or a server ui-config are ignored.
* ui: resolve absent MCP per-chat overrides to the server enabled flag
New conversations started with every MCP server off: the settings
rework stopped seeding a per-conversation override list, assuming
the enabled check would fall back to mcpServers[i].enabled, but it
fell back to false, and the send path passed the raw stored list
with no fallback at all. The per-conversation list is now sparse by
contract, holding only explicit toggles, and every access point
resolves a missing entry to the server's own enabled flag: the
toggle display, the resolved list handed to the agentic flow, and
the enabled check itself.
* vulkan/cpu: Support f16 as SET_ROWS src.
This adds full support for f16 SET_ROWS (equivalent to f32) to vulkan and CPU
backends, and adds more backend tests.
* Set DenormPreserve 16 when supported, to try to fix failures on Intel
* tune error threshold
* update metal supports_op
Migrate the tokenize tool to common_params_parse, replacing its
hand-rolled argv parsing, Windows UTF-8 handling and file reading
with the shared common helpers.
Expose the model-sourcing flags (-m, -mu, -dr, -hf, -hff, --offline,
HF_TOKEN) to LLAMA_EXAMPLE_TOKENIZE, and register --ids, --stdin,
--no-bos, --no-parse-special and --show-count as common args.
parse_special defaults to true for TOKENIZE to preserve the old
behavior. Errors now go through LOG_ERR instead of fprintf(stderr).
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
* ggml: uniformize im2col dst_type for all conv ops
* Update ggml/src/ggml.c
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* ggml : uniformize im2col casting logic across all conv ops
* fix : allow im2col_f16 to accept any kernel type
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
The mobile "+" sheet was missing the reasoning effort section present
in the desktop dropdown, so thinking could not be toggled on touch.
Extract the shared derivation and selection logic into useReasoningMenu
and consume it from both the desktop submenu and the mobile sheet,
keeping a single source of truth and preserving each surface idiom.
- Add a supports_mtp_export capability to ModelBase so architectures can opt
into --mtp and --no-mtp without extending a central class allowlist.
- Enable the capability for the existing Qwen3.5/3.6 and Step3.5/3.7
implementations, and for HY V3, whose converter already supports
filtering the appended MTP layers.
Under certain conditions, it's possible for messages emitted via LOG()
to get lost before exit, apparently because they are emitted by another
thread. common_params_print_usage() uses printf directly, and is not
affected.
Flushing the log before exit seems to resolve this.
* model: add Hy3 (hy_v3) architecture support
Adds Tencent Hunyuan 3 (HF architecture HYV3ForCausalLM, GGUF arch
hy_v3): a MoE decoder stack with per-head Q/K RMSNorm, a sigmoid
router with expert selection bias, an always-active ungated shared
expert, and leading dense block(s) (first_k_dense_replace).
The base implementation is ported from charlie12345's fork
(https://github.com/charlie12345/ROCmFPX, src/models/hyv3.cpp),
adapted to current mainline APIs (hparams.n_layer(), build_qkv,
build_moe_ffn with fused gate_up + scale tensors, output_s).
Note: blk.N.exp_probs_b is stored without a .bias suffix for
compatibility with existing hy_v3 GGUFs produced by that fork.
Co-Authored-By: charlie12345 <charlie12345@users.noreply.github.com>
Co-authored-by: Piotr Wilkin <ilintar@gmail.com>
Assisted-by: Claude Fable 5
This uses the new VK_EXT_shader_ocp_microscaling_types extension to do fp4 type
promotions, and also uses the float8 extension to do ue4m3 promotions for
nvfp4. It's reasonable to assume that an implementation that supports fp4 will
also support fp8, so we don't need to handle all possible combinations of
support.
* tests: Harmonize the use of private ggml includes
* tests: In test-backend-ops, use quoted includes
As with all other tests. This is to ensure that the build uses shipped
headers over possibly system-installed ones.
* chat : fix reasoning leak with force-opened bare <think> templates
The reasoning start tag inferred from prior turns can carry trailing
whitespace (e.g. <think>\n) while a force-open template prefills a bare
<think>. Trim the tag used for the prefix split so the bare prefill is
matched instead of being swallowed into content.
* chat : fix Nemotron Nano v2 regression
---------
Co-authored-by: Alde Rojas <hello@alde.dev>
* fix: drop MCP recommendations auto-popup and silent preloads
* feat: Add consent-driven MCP recommendations inside Add New Server dialog
* refactor: Drop mcpDefaultServerOverrides for mcpServers[i].enabled
* feat: Center the empty state on the MCP settings page
* fix: keep existing MCP cards intact when adding a new server
* fix: keep MCP cards stable when a new server is added
* refactor: keep MCP server list in config insertion order
* feat: shrink the recommended-MCP cards to two tools each and fit them in one row
* feat: make recommended MCP cards click-to-fill and tighten copy
* feat: highlight the selected MCP recommendation and stop auto-focus on dialog open
* feat: derive MCP recommendation selection from the form URL
* fix: make recommendation MCP cards fully non-focusable
* fix: redirect focus from first card to the URL input on consent
* chore: Formatting
* refactor: Remove Recommended MCP Servers completely
* fix: Preserve legacy mcpDefaultServerOverrides key after merge migration for downgrade compatibility
* server: honour per-request reasoning_budget_tokens in chat completions
The reasoning-budget block in oaicompat_chat_params_parse read only the
server-level default (opt.reasoning_budget, typically -1) and the
Anthropic-style alias thinking_budget_tokens, but never the canonical
reasoning_budget_tokens field from the request body. Because the key
was then written into llama_params before the generic body-copy loop
ran, the copy loop found the key already present and silently skipped
the caller-supplied value. Any per-request override (e.g. 0 to
suppress thinking entirely) was therefore discarded.
Fix: read reasoning_budget_tokens from the request body first, so the
value that reaches the sampling layer is the one the caller intended.
Add a unit test in test-chat.cpp that exercises this path via
oaicompat_chat_params_parse with a Qwen3 template (which the autoparser
detects as a thinking-capable model) and asserts the returned
llama_params carries reasoning_budget_tokens == 0.
* server: honour per-request reasoning_budget_message in chat completions
The reasoning-budget block in oaicompat_chat_params_parse wrote
reasoning_budget_message into llama_params straight from the server-level
default (opt.reasoning_budget_message) and never read the canonical
reasoning_budget_message field from the request body. Because the key
was written before the generic body-copy loop ran, that loop found the
key already present and silently skipped the caller-supplied value. Any
per-request override of the message injected before the end tag when the
budget is exhausted was therefore discarded, even though server-task.cpp
already reads reasoning_budget_message from that data.
This mirrors the reasoning_budget_tokens bug fixed in the previous commit.
Fix: read reasoning_budget_message from the request body first, falling
back to the server default, so the value that reaches the sampling layer
is the one the caller intended.
While here, collapse the adjacent reasoning_budget_tokens override to a
single json_value() call; json_value already falls back to the default on
a missing/null/wrong-type key, so the explicit body.contains() guard was
redundant. No behavioral change.
Add a unit test in test-chat.cpp that exercises this path via
oaicompat_chat_params_parse with a Qwen3 template (which the autoparser
detects as a thinking-capable model) and asserts the returned
llama_params carries the per-request reasoning_budget_message rather than
the server default.
* cleanup
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
If mmproj is explicitly disabled via the model preset or command-line
parameters then the model won't be able to handle image/audio inputs and
this shouldn't be declared as supported input modality on the /v1/models
endpoint.
* mtmd: fix silent prompt truncation on embedded NUL
mtmd_input_text carried the prompt as a bare const char* with no
length, so a NUL byte in message content cut the prompt at the
tokenizer boundary and dropped every later message plus the assistant
marker, with no log. Add an explicit text_len and thread it through,
matching llama_tokenize and the text only path.
* cleanup
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* server : fix image blocks in tool_result being dropped during Anthropic→OpenAI conversion
server_chat_convert_anthropic_to_oai() silently discarded image blocks
inside Anthropic tool_result content. This broke multimodal tool outputs
(e.g. a tool that returns an image) because the model never received the
image.
When tool_result contains image blocks, convert them to OpenAI
multimodal content parts (text + image_url array). Plain-text results
remain simple strings for backwards compatibility.
* server : add test for image blocks in Anthropic tool_result conversion
If a Cuda device has no or limited available memory, the actual call
to cudaMemGetInfo() itself can cause a fatal crash due to a cuda out
of memory error (there is not enough memory to actually query memory)
This causes an issue because we query memory for all devices at
startup even if the user isn't trying to use the device for inference.
Fix this by making the error non-fatal and assigning zero total/free
memory to the device. This will have the downstream effect of the fit
algorithm not trying to put any layers on it, which is desired outcome
vs hard crashing.
this also prevents crashes in cuda enabled builds when user explicitly
passes '-dev none'
