rob thijssen
84f5662df1
Stage 4 of the candle-native pivot. /v1/chat/completions now switches
to text/event-stream when the request sets stream: true, emitting one
chat.completion.chunk per generated token followed by the OpenAI
[DONE] terminator.
Pipeline:
- chat_completion_stream creates a bounded mpsc::channel<ChatCompletionChunk>(32),
sends the leading role chunk, then spawns a blocking task that
acquires the per-model arch lock and runs the streaming generation
loop.
- run_inference_streaming tracks a cumulative decoded prefix so each
chunk's delta.content is the substring added since the last chunk —
safe across BPE byte-fallback boundaries that would otherwise split
multi-byte UTF-8 chars.
- The blocking task aborts cleanly if blocking_send fails (client
disconnected), so generation stops when the SSE consumer hangs up.
- Final chunk carries finish_reason ("stop" on EOS, "length" on
max_tokens). The handler appends data: [DONE] after the channel
closes.
The Stage 3 streaming 501 placeholder test is repurposed: with the
streaming path live, an unloaded model now hits the same 404 surface
as the non-streaming path (the model lookup happens first).
cortex-gateway's existing proxy is unchanged — it already forwards
SSE bytes verbatim from Phase 2 work, so the candle SSE format passes
through unmodified.
Neuron Cargo.toml gains futures + tokio-stream (both already in
workspace deps) for ReceiverStream and stream combinators.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
69 lines
1.7 KiB
TOML
69 lines
1.7 KiB
TOML
[package]
|
|
name = "neuron"
|
|
version.workspace = true
|
|
edition.workspace = true
|
|
license.workspace = true
|
|
|
|
[lib]
|
|
name = "neuron"
|
|
path = "src/lib.rs"
|
|
|
|
[[bin]]
|
|
name = "neuron"
|
|
path = "src/main.rs"
|
|
|
|
[features]
|
|
default = []
|
|
# Enables CUDA acceleration in candle. Without this feature, candle
|
|
# compiles for CPU only and Device::new_cuda calls fall back to CPU.
|
|
cuda = [
|
|
"candle-core/cuda",
|
|
"candle-nn/cuda",
|
|
"candle-transformers/cuda",
|
|
]
|
|
# Use cuDNN for convolution / attention kernels. Requires CUDA.
|
|
cudnn = [
|
|
"cuda",
|
|
"candle-core/cudnn",
|
|
"candle-nn/cudnn",
|
|
"candle-transformers/cudnn",
|
|
]
|
|
# FlashAttention kernels. Requires CUDA.
|
|
flash-attn = [
|
|
"cuda",
|
|
"candle-transformers/flash-attn",
|
|
]
|
|
# Reserved for GPU-only integration tests in later stages.
|
|
cuda-integration = ["cuda"]
|
|
|
|
[dependencies]
|
|
cortex-core.workspace = true
|
|
tokio.workspace = true
|
|
axum.workspace = true
|
|
serde.workspace = true
|
|
serde_json.workspace = true
|
|
reqwest.workspace = true
|
|
tracing.workspace = true
|
|
tracing-subscriber.workspace = true
|
|
anyhow.workspace = true
|
|
async-trait.workspace = true
|
|
clap.workspace = true
|
|
thiserror.workspace = true
|
|
futures.workspace = true
|
|
tokio-stream.workspace = true
|
|
figment.workspace = true
|
|
toml.workspace = true
|
|
|
|
# candle for in-process inference. CUDA support is gated behind the
|
|
# crate's `cuda` feature (default off) so the workspace builds on
|
|
# non-CUDA hosts and CI runners.
|
|
candle-core = "0.10.2"
|
|
candle-nn = "0.10.2"
|
|
candle-transformers = "0.10.2"
|
|
tokenizers = { version = "0.22", default-features = false, features = ["onig"] }
|
|
hf-hub = { version = "0.4", features = ["tokio"] }
|
|
|
|
[dev-dependencies]
|
|
tokio = { workspace = true, features = ["test-util"] }
|
|
reqwest.workspace = true
|