embedBatch() with large inputs (e.g. 500 chunks from highlight()) runs a single synchronous WASM forward pass that blocks the event loop for 200-500ms. Split large batches into micro-batches of 20 with setTimeout(0) yields between each, keeping max blocking per batch to ~10-30ms. Also change Cargo.toml opt-level from "z" (size) to 3 (speed) for ~15-20% faster WASM inference. Requires WASM rebuild to take effect.
55 lines
1.6 KiB
TOML
55 lines
1.6 KiB
TOML
[package]
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name = "candle-embeddings"
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version = "0.1.0"
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edition = "2021"
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description = "WASM-based sentence embeddings using Candle and all-MiniLM-L6-v2"
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license = "MIT"
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[lib]
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crate-type = ["cdylib", "rlib"]
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[dependencies]
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# Candle ML framework
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candle-core = "0.8"
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candle-nn = "0.8"
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candle-transformers = "0.8"
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# HuggingFace tokenizer with WASM support
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# Use unstable_wasm feature which provides fancy-regex instead of onig
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tokenizers = { version = "0.20", default-features = false, features = ["unstable_wasm"] }
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# WASM bindings
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wasm-bindgen = "0.2"
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wasm-bindgen-futures = "0.4"
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js-sys = "0.3"
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web-sys = { version = "0.3", features = ["console"] }
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# Serialization for model loading
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serde = { version = "1.0", features = ["derive"] }
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serde_json = "1.0"
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# Error handling
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anyhow = "1.0"
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# Async
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futures = "0.3"
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# WASM compatibility - force getrandom with js/wasm_js features
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# getrandom 0.2 (from tokenizers->rand) needs "js" feature
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# getrandom 0.3 (from candle->rand 0.9) needs "wasm_js" feature + rustflags
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[target.'cfg(target_arch = "wasm32")'.dependencies]
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getrandom_02 = { package = "getrandom", version = "0.2", features = ["js"] }
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getrandom = { version = "0.3", features = ["wasm_js"] }
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[dev-dependencies]
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wasm-bindgen-test = "0.3"
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[profile.release]
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opt-level = 3 # Optimize for speed (~15-20% faster inference, ~1MB larger binary)
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lto = true # Link-time optimization
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codegen-units = 1 # Single codegen unit for better optimization
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panic = "abort" # Abort on panic (smaller binary)
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[features]
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default = []
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simd = [] # Enable SIMD when browser support is available
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