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* feat(backend): add vllm-cpp text-generation backend (vllm.cpp) Wrap https://github.com/mudler/vllm.cpp - the LocalAI-team from-scratch C++20 port of vLLM (paged KV cache, continuous batching, prefix caching, safetensors + GGUF loading, no Python at inference) - as a Go gRPC backend over its stable C ABI (ABI v2) via purego. Backend (backend/go/vllm-cpp): - Load -> vllm_engine_load: accepts a .gguf file or a config.json model dir (anything else is refused, satisfying the greedy-probe rule); context_size maps to max_model_len, options block_size/num_blocks/max_num_seqs size the KV cache and scheduler admission. - Predict -> vllm_complete (blocking); PredictStream -> vllm_complete_stream with the per-delta C callback bridged into the gRPC stream. The backend embeds base.Base (not SingleThread): concurrent requests batch continuously in the engine's shared AsyncLLM scheduler. - PredictOptions.Grammar -> the ABI's structured_grammar (GBNF), giving grammar-constrained tool calling at parity with llama-cpp; the ABI also exposes JSON-schema/regex/choice constraints. - Hand-mirrored POD structs with layout locked by unit tests (unsafe.Offsetof vs the C offsets) and a runtime vllm_abi_version gate. - One portable library per platform (vllm.cpp uses per-file SIMD tiers with runtime dispatch), so no avx/avx2/avx512 variant builds. Wiring: - backend-matrix: CPU amd64+arm64 (per-arch + manifest merge), CUDA 12/13 amd64 (120a;121a Blackwell fat binary), L4T arm64 (121a, GB10/DGX Spark - the runtime-proven GPU target), Vulkan amd64, and Darwin arm64 Metal. - backend/index.yaml meta + 12 image entries (latest/development x cpu, cuda12, cuda13, l4t, vulkan, metal); bump_deps registration for the VLLM_CPP_VERSION pin; root Makefile registration; test-extra runs the unit specs (pure Go, no engine build). - Importers: preference-only swaps - llama-cpp (GGUF) and vllm (safetensors) advertise vllm-cpp via AdditionalBackends and emit backend: vllm-cpp without tokenizer templating (the C ABI takes the FINAL prompt; templating and tool parsing stay LocalAI-side). No auto-detect importer. - Docs: backends list, top-level README maintained-engines table, compatibility table. Verified: 20/20 Ginkgo specs against the real pinned engine and Qwen3.5-2B-UD-Q8_K_XL.gguf on CPU - blocking + streaming parity, greedy determinism, stop words, GBNF-constrained generation, and 4 concurrent streams; plus a dlopen/ABI-gate smoke of the built gRPC server binary. Upstream ABI v2 + production structured-output wiring landed as mudler/vllm.cpp@86013f3. Assisted-by: Claude Code:claude-fable-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vllm-cpp): ride the autoparser code path - engine-side chat templating and tool engagement (ABI v3) The backend now implements AIModelRich (PredictRich / PredictStreamRich) over vllm.cpp's ABI v3 chat entry points, so chat and tool calling ride the SAME code path as the llama.cpp autoparser: the ENGINE renders the model's chat template, decides when a tool call engages, and parses it - LocalAI receives pre-parsed ChatDelta / ToolCallDelta protos exactly as it does from llama-cpp. - With use_tokenizer_template + structured Messages, PredictOptions lowers to ONE OpenAI chat request JSON (messages, tools, tool_choice, sampling, stream_options.include_usage) for vllm_chat / vllm_chat_stream. tool_choice auto lowers engine-side to a LAZY structural-tag decode constraint - free text until the model emits the tool trigger, then the call is grammar-constrained; required/named force a call. Tool output is parsed by the engine's streaming Hermes-style parser; each chat.completion.chunk maps onto ChatDeltas (content / reasoning_content / tool_calls) which the host already prefers over Go-side tag extraction. Without structured messages the plain path (LocalAI templating + optional GBNF grammar) applies unchanged. - The engine resolves the chat template from the GGUF tokenizer.chat_template metadata (or tokenizer_config.json); templates beyond its minja subset - e.g. the full Qwen3.5 namespace()/macro template - degrade engine-side to a Hermes-aware fallback prompt (tools schemas + <tool_call> instruction) with a stderr witness, so structural-tag engagement keeps working. - Importers now emit the same config shape as llama-cpp for vllm-cpp (use_tokenizer_template: true, no-grammar autoparser flow); only the llama-cpp-specific use_jinja option and the vllm-python parser options are dropped. - Pin bumped to mudler/vllm.cpp@aaed7ec (ABI v3 + chat-prompt resolution). Verified against the real engine and Qwen3.5-2B-UD-Q8_K_XL.gguf on CPU: full suite green - blocking chat, streaming deltas concatenating byte-equal to the blocking answer, a REQUIRED tool call returning schema-valid arguments JSON, and an AUTO run where the engine itself engages get_weather and streams parsed tool deltas; plus unit specs for the request lowering, chunk->ChatDelta mapping, and the C struct mirrors (ABI gate now v3). Assisted-by: Claude Code:claude-fable-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vllm-cpp): ABI v5 - engine-side parser selection for 30 tool dialects + reasoning Bump the vllm.cpp pin to the autoparser-parity engine: 30 tool-call dialects (every pure-text parser in the pinned vLLM registry, each ported 1:1 with its upstream tests), 7 reasoning parsers, google/minja as the template renderer (the full Qwen3.5 template now renders engine-side), per-family structural tags (tool_choice required/named compiles the model's NATIVE syntax where expressible), and template auto-detection for both parser axes. Backend changes: - cModelParams mirrors ABI v5 (tool_parser + reasoning_parser fields, layout-locked by the offset tests; ABI gate now v5). - New model options tool_parser:<name> / reasoning_parser:<name> pass through to the engine; unset means template auto-detection (18-row tool marker table; [THINK]->mistral, <think>->think_auto for reasoning); "none" disables the reasoning split; unknown names fail the first chat call. - Chat chunks parse the `reasoning` field (the pin renamed reasoning_content), flowing into ChatDelta.ReasoningContent which the host already prefers. Live e2e against Qwen3.5-2B-UD-Q8_K_XL.gguf on CPU, full suite green: the real chat template renders (no more fallback), reasoning auto-detection picks think_auto so markerless answers stay pure content (the live run caught the deepseek_r1 content-swallow upstream and drove the think_auto fix), required tool_choice returns schema-valid arguments, auto tool_choice engages engine-side and streams parsed deltas, and blocking/streaming stay byte-identical. Turn latency also dropped (proper template EOS behavior). Upstream program landed as mudler/vllm.cpp 86013f3..5fffe7e (ABI v2-v5, minja, parser waves B1/B2/B4, reasoning seam, structural-tag registry, think_auto). Assisted-by: Claude Code:claude-fable-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore(vllm-cpp): bump the engine pin to the ENG-wave close-out mudler/vllm.cpp@df8909b: the six engine-backed vLLM tool-parser families (qwen3-coder/xml/mimo, kimi_k2, glm45/47, minimax_m2, gemma4, seed_oss) text-reimplemented from their wire formats and held to the upstream test suites - 39 registered dialects; the pinned vLLM registry is now covered except the three Rust/Harmony-backed families, descoped by decision. kimi_k2 also gains a full native structural-tag builder; four new template auto-detection rows land with test-pinned ordering. Full backend e2e re-run green against Qwen3.5-2B-UD-Q8_K_XL.gguf on CPU. Assisted-by: Claude Code:claude-fable-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(vllm-cpp): add the vllm-cpp-development gallery meta The gallery grew the twelve latest/development image entries but was missing the separate vllm-cpp-development meta (own capabilities map targeting the -development image names), which every backend ships so the development gallery resolves per-platform. Validated: all capability targets in both metas resolve to existing entries, and every image URI's tag suffix matches a backend-matrix build. Also full-stack verified in this change's context (single-node local-ai from this branch, locally-built backend under --backends-path, Qwen3.5-2B GGUF): /v1/chat/completions non-stream (clean content + usage), streaming (SSE deltas), tool_choice auto engaging get_weather engine-side with schema-valid arguments and finish_reason=tool_calls, and streamed tool-call deltas in the standard name-first cadence. Assisted-by: Claude Code:claude-fable-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(vllm-cpp): repair the CI backend builds - gcc-14 -Werror + fat-arch Triton Two distinct failures took down all five vllm-cpp backend builds on the PR: 1. gcc-14 (ubuntu:24.04 CI images; the local toolchain is gcc-13) fails the engine build with -Werror=maybe-uninitialized in InputBatch::condense - a false positive through a staging std::optional's raw storage. Fixed upstream (mudler/vllm.cpp@61f3e85) by moving slot-to-slot directly; verified BOTH ways under dockerized g++-14.2 (unfixed reproduces CI's two diagnostics exactly, fixed compiles clean) with the engine's behavior suites green. Pin bumped to that sha. 2. The amd64 CUDA builds died at CMake configure: the vendored Triton-AOT cubin trees are per-arch and the engine refuses -DVLLM_CPP_TRITON=ON on a multi-arch (120a;121a) fat build unless pinned to one tree, which would be unsound for the other arch. Triton is now enabled only on the single-arch arm64/GB10 build (where the cubins matter); the fat amd64 binary uses the engine's non-AOT GDN path. Backend e2e re-run green at the new pin (Qwen3.5-2B on CPU, full suite). Assisted-by: Claude Code:claude-fable-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(vllm-cpp): cuda-12 images cannot compile compute_121a - target 120a only The second CI round surfaced a CUDA-version constraint: the cuda-12 (12.8) image's nvcc rejects 'compute_121a' (GB10 arch support landed with CUDA 13), killing the amd64 cuda-12 build at nvcc. Gate the architecture list on CUDA_MAJOR_VERSION (exported by Dockerfile.golang): cuda-12 builds consumer Blackwell 120a only, cuda-13 keeps the 120a;121a fat binary, arm64/l4t (cuda-13) keeps single-arch 121a with the Triton cubins. GB10 is arm64, so the amd64 cuda-12 image never served it - no capability change. Verified by Makefile dry-run variable dumps for all three combinations (cuda12 -> 120a; cuda13 -> 120a;121a; cpu -> CUDA off). Assisted-by: Claude Code:claude-fable-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(vllm-cpp): drop the cuda-12 variant - the engine needs the CUDA 13 toolchain Third CI round, third layer: with the arch list already narrowed to 120a, the cuda-12 (12.8) build still dies in ptxas compiling the sm_120a NVFP4 MMA kernels ("Vector type too large, exceeds 128 bit limit") - the Blackwell fp4 path genuinely requires the CUDA 13 toolchain, and vllm.cpp supports Blackwell-family GPUs only. Shipping a cuda-12 image without the fp4 kernels would be a crippled build of an engine whose whole GPU story is fp4, so the variant is dropped instead: - backend-matrix: cuda-12 vllm-cpp entry removed (cuda-13 amd64, l4t arm64, cpu, vulkan, metal remain). - gallery: cuda12 image entries removed; the nvidia capability now resolves to the cuda13 image in both metas; the nvidia-cuda-12 key is dropped so older-driver hosts fall back to the CPU image instead of an unrunnable one. - backend Makefile: BUILD_TYPE=cublas under CUDA_MAJOR_VERSION=12 now fails fast with a clear message; cuda-13 keeps the 120a;121a fat binary and arm64/l4t keeps 121a with the Triton cubins. Verified: Makefile branch dumps for all four combinations (cuda12 loud error, cuda13 fat, arm64 121a+Triton, cpu off), YAML parses, matrix filter tests green, gallery capability targets all resolve. Assisted-by: Claude Code:claude-fable-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(vllm-cpp): forward multi-turn tool identity and reasoning to the engine chatRequestJSON dropped Message.ToolCallId and Message.Name on role="tool" replies and Message.ReasoningContent on assistant history, so a second turn after tool execution reached the engine's chat template without the fields that bind a tool result to the call it answers. Forward all three (present-only, matching the OpenAI wire shape) and pin vllm.cpp to 6a0bd3e7, where ChatMessage parses/round-trips tool_calls, tool_call_id, name and reasoning and the minja adapter exposes them to the template context. Adds the round-trip request-lowering spec (user -> assistant tool_call -> tool reply -> lowered request) and re-ran the gated e2e suite against the new engine pin with a real Qwen3.5 GGUF: chat, reasoning split, streaming parity, required-tool and auto-tool cases all green. Assisted-by: Claude Code:claude-fable-5 [Bash] [Edit] [Read] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(vllm-cpp): bump vllm.cpp for the darwin arm64 i8mm build fix The darwin-metal CI job was the first build to compile the engine's arm CPU-quant files on macOS and hit their Linux-only <asm/hwcap.h> / <sys/auxv.h> includes. vllm.cpp 9e1c9025 detects i8mm per-OS (auxv on Linux, sysctl on Apple Silicon) with kernels untouched. Gated e2e suite re-run green against the new pin with a real Qwen3.5 GGUF. Assisted-by: Claude Code:claude-fable-5 [Bash] [Read] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(vllm-cpp): darwin build - bound cmake parallelism when nproc is absent The macOS runners have no nproc, so JOBS evaluated empty and `cmake --build -j$(JOBS)` became bare `-j`: unlimited clang jobs on a 3-core/7GB Mac, which swap-thrashed until the 6h GHA timeout (the log shows "nproc: Command not found" and 7+ concurrent clang processes being reaped at the cutoff). Use the same portable fallback chain as the other darwin backends: nproc, then sysctl hw.ncpu, then 4. Assisted-by: Claude Code:claude-fable-5 [Bash] [Edit] [Read] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
55 lines
1.5 KiB
Go
55 lines
1.5 KiB
Go
package main
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// Engine-sizing knobs carried through the model config's free-form
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// `options:` list ("key:value" entries), mirroring how the other in-house
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// backends pass engine-specific settings that have no proto field.
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import (
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"strconv"
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"strings"
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pb "github.com/mudler/LocalAI/pkg/grpc/proto"
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)
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type loadOptions struct {
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blockSize int32 // KV block size (tokens/block); engine default 32.
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numBlocks int32 // KV blocks to allocate; engine default 256.
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maxNumSeqs int32 // max concurrent sequences; engine default 8.
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// Engine-side parser selection (ABI v4/v5). Empty = the engine
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// auto-detects from the chat template; "none" disables the reasoning
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// split; unknown names fail the first chat call.
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toolParser string
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reasoningParser string
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}
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func parseOptions(opts *pb.ModelOptions) loadOptions {
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lo := loadOptions{}
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for _, o := range opts.GetOptions() {
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k, v, found := strings.Cut(o, ":")
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if !found {
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continue
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}
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switch strings.TrimSpace(k) {
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case "block_size":
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lo.blockSize = parseInt32(v, lo.blockSize)
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case "num_blocks":
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lo.numBlocks = parseInt32(v, lo.numBlocks)
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case "max_num_seqs":
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lo.maxNumSeqs = parseInt32(v, lo.maxNumSeqs)
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case "tool_parser":
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lo.toolParser = strings.TrimSpace(v)
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case "reasoning_parser":
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lo.reasoningParser = strings.TrimSpace(v)
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}
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}
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return lo
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}
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func parseInt32(s string, fallback int32) int32 {
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n, err := strconv.ParseInt(strings.TrimSpace(s), 10, 32)
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if err != nil || n <= 0 {
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return fallback
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}
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return int32(n)
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}
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