Files
LocalAI/backend/go/vllm-cpp/chat.go
mudler's LocalAI [bot] 4baa36ddd8 feat(backend): vllm-cpp - text-generation backend for vllm.cpp with llama.cpp-parity tool calling (#11100)
* 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>
2026-07-26 23:04:48 +02:00

290 lines
8.4 KiB
Go

package main
// The rich chat path (AIModelRich): rides the ENGINE's serving pipeline via
// the ABI v3 chat entry points, exactly like the llama-cpp autoparser flow.
// The engine applies the model's chat template, decides when a tool call
// engages (tool_choice auto lowers to a LAZY structural-tag decode
// constraint), parses tool calls with its streaming-stateful Hermes-style
// parser, and hands back chat.completion.chunk JSON that this file maps 1:1
// onto pb.Reply ChatDelta / ToolCallDelta.
import (
"encoding/json"
"fmt"
"sync"
"unsafe"
"github.com/ebitengine/purego"
pb "github.com/mudler/LocalAI/pkg/grpc/proto"
"github.com/mudler/xlog"
)
// useChatPath reports whether the request should go through the engine-side
// chat pipeline: the model config asked for backend-side templating and the
// host handed us structured messages.
func useChatPath(opts *pb.PredictOptions) bool {
return opts.UseTokenizerTemplate && len(opts.Messages) > 0
}
// chatRequestJSON lowers PredictOptions into one OpenAI chat-completions
// request object for the ABI (the engine ignores `model`/`stream`).
func chatRequestJSON(opts *pb.PredictOptions, stream bool) (string, error) {
messages := make([]map[string]any, 0, len(opts.Messages))
for _, m := range opts.Messages {
msg := map[string]any{"role": m.Role, "content": m.Content}
if m.ToolCalls != "" {
var toolCalls any
if err := json.Unmarshal([]byte(m.ToolCalls), &toolCalls); err == nil {
msg["tool_calls"] = toolCalls
}
}
// Multi-turn tool identity + prior reasoning: a role="tool" reply
// carries the id (and optionally the name) of the assistant call it
// answers, and assistant history may carry its reasoning span. The
// engine's template context needs all three or a second turn after
// tool execution is malformed.
if m.ToolCallId != "" {
msg["tool_call_id"] = m.ToolCallId
}
if m.Name != "" {
msg["name"] = m.Name
}
if m.ReasoningContent != "" {
msg["reasoning"] = m.ReasoningContent
}
messages = append(messages, msg)
}
req := map[string]any{"messages": messages}
if opts.Tools != "" {
var tools any
if err := json.Unmarshal([]byte(opts.Tools), &tools); err != nil {
return "", fmt.Errorf("vllm-cpp: tools is not valid JSON: %w", err)
}
req["tools"] = tools
}
if opts.ToolChoice != "" {
var choice any
// ToolChoice arrives either as a bare string ("auto"/"required"/"none")
// or as the OpenAI named-function JSON object.
if err := json.Unmarshal([]byte(opts.ToolChoice), &choice); err == nil {
req["tool_choice"] = choice
} else {
req["tool_choice"] = opts.ToolChoice
}
}
req["temperature"] = opts.Temperature
if opts.TopP > 0 {
req["top_p"] = opts.TopP
}
if opts.TopK > 0 {
req["top_k"] = opts.TopK
}
if opts.Tokens > 0 {
req["max_tokens"] = opts.Tokens
}
if opts.Seed > 0 {
req["seed"] = opts.Seed
}
if len(opts.StopPrompts) > 0 {
req["stop"] = opts.StopPrompts
}
if opts.PresencePenalty != 0 {
req["presence_penalty"] = opts.PresencePenalty
}
if opts.FrequencyPenalty != 0 {
req["frequency_penalty"] = opts.FrequencyPenalty
}
if stream {
// The engine's request parser validates stream_options against the
// stream flag at parse time (before the ABI entry point forces it),
// so state the intent explicitly.
req["stream"] = true
req["stream_options"] = map[string]any{"include_usage": true}
}
b, err := json.Marshal(req)
if err != nil {
return "", err
}
return string(b), nil
}
// chatChunk is the subset of an OpenAI chat.completion(.chunk) object the
// backend consumes.
type chatChunk struct {
Object string `json:"object"`
Choices []struct {
Delta *chatDelta `json:"delta"` // streaming chunks
Message *chatDelta `json:"message"` // non-stream response
FinishReason string `json:"finish_reason"`
} `json:"choices"`
Usage *struct {
PromptTokens int32 `json:"prompt_tokens"`
CompletionTokens int32 `json:"completion_tokens"`
} `json:"usage"`
}
type chatDelta struct {
Content string `json:"content"`
ReasoningContent string `json:"reasoning"`
ToolCalls []struct {
Index int32 `json:"index"`
ID string `json:"id"`
Function struct {
Name string `json:"name"`
Arguments string `json:"arguments"`
} `json:"function"`
} `json:"tool_calls"`
}
// toReply maps one parsed chunk onto a pb.Reply carrying the content bytes
// plus the structured ChatDelta (the host prefers ChatDeltas when present).
func (c *chatChunk) toReply() *pb.Reply {
reply := &pb.Reply{}
if c.Usage != nil {
reply.PromptTokens = c.Usage.PromptTokens
reply.Tokens = c.Usage.CompletionTokens
}
if len(c.Choices) == 0 {
return reply
}
d := c.Choices[0].Delta
if d == nil {
d = c.Choices[0].Message
}
if d == nil {
return reply
}
delta := &pb.ChatDelta{
Content: d.Content,
ReasoningContent: d.ReasoningContent,
}
for _, tc := range d.ToolCalls {
delta.ToolCalls = append(delta.ToolCalls, &pb.ToolCallDelta{
Index: tc.Index,
Id: tc.ID,
Name: tc.Function.Name,
Arguments: tc.Function.Arguments,
})
}
reply.Message = []byte(d.Content)
if delta.Content != "" || delta.ReasoningContent != "" ||
len(delta.ToolCalls) > 0 {
reply.ChatDeltas = []*pb.ChatDelta{delta}
}
return reply
}
// Chat-stream registry: chunk JSON arrives on the engine's delivery thread
// through one shared C callback; the integer handle in user_data selects the
// destination channel (never a Go pointer across the ABI).
var (
chatStreamsMu sync.Mutex
chatStreams = map[uintptr]chan<- *pb.Reply{}
chatStreamNext uintptr
chatCbOnce sync.Once
chatCbPtr uintptr
)
func chatCallback(delta uintptr, finished uintptr, userData uintptr) uintptr {
chatStreamsMu.Lock()
results := chatStreams[userData]
chatStreamsMu.Unlock()
if results == nil {
return 0
}
_ = finished // the terminal call carries an empty delta; nothing to emit.
payload := goString(delta)
if payload == "" {
return 1
}
var chunk chatChunk
if err := json.Unmarshal([]byte(payload), &chunk); err != nil {
xlog.Error("[vllm-cpp] unparseable chat chunk", "error", err)
return 1
}
results <- chunk.toReply()
return 1
}
func registerChatStream(results chan<- *pb.Reply) uintptr {
chatStreamsMu.Lock()
defer chatStreamsMu.Unlock()
chatStreamNext++
chatStreams[chatStreamNext] = results
return chatStreamNext
}
func unregisterChatStream(h uintptr) {
chatStreamsMu.Lock()
defer chatStreamsMu.Unlock()
delete(chatStreams, h)
}
// PredictRich implements the non-streaming rich path. Without structured
// messages it falls back to the plain Predict flow (LocalAI-side templating,
// optional grammar constraint).
func (v *VllmCpp) PredictRich(opts *pb.PredictOptions) (*pb.Reply, error) {
if !useChatPath(opts) {
text, err := v.Predict(opts)
if err != nil {
return nil, err
}
return &pb.Reply{Message: []byte(text)}, nil
}
if v.engine == 0 {
return nil, fmt.Errorf("vllm-cpp: model not loaded")
}
request, err := chatRequestJSON(opts, false)
if err != nil {
return nil, err
}
var out uintptr
rc := vllmChat(v.engine, request, unsafe.Pointer(&out)) // #nosec G103 -- char** out-param
if rc != vllmOK {
return nil, fmt.Errorf("vllm-cpp: chat failed: %s", vllmLastError())
}
payload := goString(out)
vllmStringFree(out)
var response chatChunk
if err := json.Unmarshal([]byte(payload), &response); err != nil {
return nil, fmt.Errorf("vllm-cpp: unparseable chat response: %w", err)
}
return response.toReply(), nil
}
// PredictStreamRich implements the streaming rich path. Contract: send into
// the channel and return when finished; the host closes the channel.
func (v *VllmCpp) PredictStreamRich(opts *pb.PredictOptions, results chan<- *pb.Reply) error {
if !useChatPath(opts) {
// Legacy bridge: run the plain stream and wrap deltas.
plain := make(chan string)
if err := v.PredictStream(opts, plain); err != nil {
return err
}
for delta := range plain {
results <- &pb.Reply{Message: []byte(delta)}
}
return nil
}
if v.engine == 0 {
return fmt.Errorf("vllm-cpp: model not loaded")
}
request, err := chatRequestJSON(opts, true)
if err != nil {
return err
}
chatCbOnce.Do(func() {
chatCbPtr = purego.NewCallback(chatCallback)
})
handle := registerChatStream(results)
defer unregisterChatStream(handle)
rc := vllmChatStream(v.engine, request, chatCbPtr, handle)
if rc != vllmOK {
return fmt.Errorf("vllm-cpp: chat stream failed: %s", vllmLastError())
}
return nil
}