From f0cddd5d5da0c0e5c198c7e1fed5a462321be49d Mon Sep 17 00:00:00 2001 From: wangjiawen2013 Date: Tue, 28 Jan 2025 12:05:44 +0800 Subject: [PATCH 01/14] modern lstm --- Cargo.lock | 934 ++++++++++++++++++++++---- examples/modern-lstm/Cargo.toml | 29 + examples/modern-lstm/README.md | 22 + examples/modern-lstm/src/cli.rs | 42 ++ examples/modern-lstm/src/dataset.rs | 111 +++ examples/modern-lstm/src/inference.rs | 46 ++ examples/modern-lstm/src/lib.rs | 5 + examples/modern-lstm/src/main.rs | 170 +++++ examples/modern-lstm/src/model.rs | 336 +++++++++ examples/modern-lstm/src/training.rs | 125 ++++ 10 files changed, 1673 insertions(+), 147 deletions(-) create mode 100644 examples/modern-lstm/Cargo.toml create mode 100644 examples/modern-lstm/README.md create mode 100644 examples/modern-lstm/src/cli.rs create mode 100644 examples/modern-lstm/src/dataset.rs create mode 100644 examples/modern-lstm/src/inference.rs create mode 100644 examples/modern-lstm/src/lib.rs create mode 100644 examples/modern-lstm/src/main.rs create mode 100644 examples/modern-lstm/src/model.rs create mode 100644 examples/modern-lstm/src/training.rs diff --git a/Cargo.lock b/Cargo.lock index 5f5b948a86..c19478d646 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -402,12 +402,12 @@ name = "backend-comparison" version = "0.17.0" dependencies = [ "arboard", - 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"cubecl-common", - "cubecl-core", - "cubecl-cpp", - "cubecl-runtime", + "cubecl-common 0.5.0", + "cubecl-core 0.5.0", + "cubecl-cpp 0.5.0", + "cubecl-runtime 0.5.0", "cudarc", "derive-new 0.6.0", "half", "log", ] +[[package]] +name = "cubecl-hip" +version = "0.4.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "976e150315f9d7d6bb84c51cb13c19221ea5d185bb6d61347a3c392dd29720de" +dependencies = [ + "bytemuck", + "cubecl-common 0.4.0", + "cubecl-core 0.4.0", + "cubecl-cpp 0.4.0", + "cubecl-hip-sys", + "cubecl-runtime 0.4.0", + "derive-new 0.6.0", + "half", + "log", + "paste", +] + [[package]] name = "cubecl-hip" version = "0.5.0" source = "git+https://github.com/tracel-ai/cubecl?rev=ff34667accfe077d4a1cd48ae419868e142acfd6#ff34667accfe077d4a1cd48ae419868e142acfd6" dependencies = [ "bytemuck", - "cubecl-common", - "cubecl-core", - "cubecl-cpp", + "cubecl-common 0.5.0", + "cubecl-core 0.5.0", + "cubecl-cpp 0.5.0", "cubecl-hip-sys", - "cubecl-runtime", + "cubecl-runtime 0.5.0", "derive-new 0.6.0", "half", "log", @@ -1690,7 +2097,7 @@ name = "cubecl-ir" version = "0.5.0" source = "git+https://github.com/tracel-ai/cubecl?rev=ff34667accfe077d4a1cd48ae419868e142acfd6#ff34667accfe077d4a1cd48ae419868e142acfd6" dependencies = [ - "cubecl-common", + "cubecl-common 0.5.0", "cubecl-macros-internal", "derive_more 1.0.0", "float-ord", @@ -1700,24 +2107,53 @@ dependencies = [ "type_hash", ] +[[package]] +name = "cubecl-linalg" +version = "0.4.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "640c379e225fecb1336f963affd3b8f1ff66b9320a972dfe92d8158dca8b6382" +dependencies = [ + "bytemuck", + "cubecl-core 0.4.0", + "cubecl-runtime 0.4.0", + "half", + "serde", +] + [[package]] name = "cubecl-linalg" version = "0.5.0" source = "git+https://github.com/tracel-ai/cubecl?rev=ff34667accfe077d4a1cd48ae419868e142acfd6#ff34667accfe077d4a1cd48ae419868e142acfd6" dependencies = [ "bytemuck", - "cubecl-core", - "cubecl-runtime", + "cubecl-core 0.5.0", + "cubecl-runtime 0.5.0", "half", "serde", ] +[[package]] +name = "cubecl-macros" +version = "0.4.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "f05d95f3be436814f909a3ac97209159f63076d3d2b254914bc02db2ac7faefb" +dependencies = [ + "cubecl-common 0.4.0", + "darling", + "derive-new 0.6.0", + "ident_case", + "prettyplease", + "proc-macro2", + "quote", + "syn 2.0.96", +] + [[package]] name = "cubecl-macros" version = "0.5.0" source = "git+https://github.com/tracel-ai/cubecl?rev=ff34667accfe077d4a1cd48ae419868e142acfd6#ff34667accfe077d4a1cd48ae419868e142acfd6" dependencies = [ - "cubecl-common", + "cubecl-common 0.5.0", "darling", "derive-new 0.6.0", "ident_case", @@ -1743,7 +2179,7 @@ name = "cubecl-opt" version = "0.5.0" source = "git+https://github.com/tracel-ai/cubecl?rev=ff34667accfe077d4a1cd48ae419868e142acfd6#ff34667accfe077d4a1cd48ae419868e142acfd6" dependencies = [ - "cubecl-common", + "cubecl-common 0.5.0", "cubecl-ir", "float-ord", "log", @@ -1754,16 +2190,49 @@ dependencies = [ "type-map", ] +[[package]] +name = "cubecl-reduce" +version = "0.4.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "0912890b52cc6f9636e0070320ff93dec27af15d57453789081b9a8bdb49786d" +dependencies = [ + "cubecl-core 0.4.0", + "cubecl-runtime 0.4.0", + "num-traits", +] + [[package]] name = "cubecl-reduce" version = "0.5.0" source = "git+https://github.com/tracel-ai/cubecl?rev=ff34667accfe077d4a1cd48ae419868e142acfd6#ff34667accfe077d4a1cd48ae419868e142acfd6" dependencies = [ - "cubecl-core", - "cubecl-runtime", + "cubecl-core 0.5.0", + "cubecl-runtime 0.5.0", "num-traits", ] +[[package]] +name = "cubecl-runtime" +version = "0.4.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "75e84f4ae5a096e4d0c410db01d18b673d6efcd6eea1724d1a001ab60484df87" +dependencies = [ + "async-channel", + "async-lock", + "cfg_aliases 0.2.1", + "cubecl-common 0.4.0", + "derive-new 0.6.0", + "dirs", + "hashbrown 0.14.5", + "log", + "md5", + "sanitize-filename 0.5.0", + "serde", + "serde_json", + "spin", + "wasm-bindgen-futures", +] + [[package]] name = "cubecl-runtime" version = "0.5.0" @@ -1771,8 +2240,8 @@ source = "git+https://github.com/tracel-ai/cubecl?rev=ff34667accfe077d4a1cd48ae4 dependencies = [ "async-channel", "async-lock", - "cfg_aliases", - "cubecl-common", + "cfg_aliases 0.2.1", + "cubecl-common 0.5.0", "derive-new 0.6.0", "dirs", "hashbrown 0.14.5", @@ -1792,15 +2261,35 @@ version = "0.5.0" source = "git+https://github.com/tracel-ai/cubecl?rev=ff34667accfe077d4a1cd48ae419868e142acfd6#ff34667accfe077d4a1cd48ae419868e142acfd6" dependencies = [ "bitflags 2.8.0", - "cubecl-common", - "cubecl-core", + "cubecl-common 0.5.0", + "cubecl-core 0.5.0", "cubecl-opt", - "cubecl-runtime", + "cubecl-runtime 0.5.0", "half", "hashbrown 0.14.5", "rspirv", ] +[[package]] +name = "cubecl-wgpu" +version = "0.4.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "3cf8105d01ef4cd103d4e31bee9ae583fabc807253234923fb08218b28db7d15" +dependencies = [ + "async-channel", + "bytemuck", + "cfg-if", + "cfg_aliases 0.2.1", + "cubecl-common 0.4.0", + "cubecl-core 0.4.0", + "cubecl-runtime 0.4.0", + "derive-new 0.6.0", + "hashbrown 0.14.5", + "log", + "web-time", + "wgpu 23.0.1", +] + [[package]] name = "cubecl-wgpu" version = "0.5.0" @@ -1810,16 +2299,16 @@ dependencies = [ "async-channel", "bytemuck", "cfg-if", - "cfg_aliases", - "cubecl-common", - "cubecl-core", - "cubecl-runtime", + "cfg_aliases 0.2.1", + "cubecl-common 0.5.0", + "cubecl-core 0.5.0", + "cubecl-runtime 0.5.0", "cubecl-spirv", "derive-new 0.6.0", "hashbrown 0.14.5", "log", "web-time", - "wgpu", + "wgpu 24.0.1", ] [[package]] @@ -1836,7 +2325,7 @@ dependencies = [ name = "custom-csv-dataset" version = "0.17.0" dependencies = [ - "burn", + "burn 0.17.0", "csv", "reqwest", "serde", @@ -1846,10 +2335,10 @@ dependencies = [ name = "custom-cubecl-kernel" version = "0.17.0" dependencies = [ - "burn", - "burn-jit", + "burn 0.17.0", + "burn-jit 0.17.0", "bytemuck", - "cubecl", + "cubecl 0.5.0", "derive-new 0.7.0", "log", "serde", @@ -1859,7 +2348,7 @@ dependencies = [ name = "custom-image-dataset" version = "0.17.0" dependencies = [ - "burn", + "burn 0.17.0", "flate2", "tar", ] @@ -1868,7 +2357,7 @@ dependencies = [ name = "custom-renderer" version = "0.17.0" dependencies = [ - "burn", + "burn 0.17.0", "bytemuck", "derive-new 0.7.0", "guide", @@ -1880,7 +2369,7 @@ dependencies = [ name = "custom-training-loop" version = "0.17.0" dependencies = [ - "burn", + "burn 0.17.0", "bytemuck", "derive-new 0.7.0", "guide", @@ -1892,9 +2381,9 @@ dependencies = [ name = "custom-wgpu-kernel" version = "0.17.0" dependencies = [ - "burn", + "burn 0.17.0", "bytemuck", - "cubecl", + "cubecl 0.5.0", "derive-new 0.7.0", "log", "serde", @@ -2858,6 +3347,18 @@ dependencies = [ "walkdir", ] +[[package]] +name = "glow" +version = "0.14.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "d51fa363f025f5c111e03f13eda21162faeacb6911fe8caa0c0349f9cf0c4483" +dependencies = [ + "js-sys", + "slotmap", + "wasm-bindgen", + "web-sys", +] + [[package]] name = "glow" version = "0.16.0" @@ -2934,7 +3435,7 @@ dependencies = [ name = "guide" version = "0.17.0" dependencies = [ - "burn", + "burn 0.17.0", "log", "serde", ] @@ -3434,8 +3935,8 @@ dependencies = [ name = "image-classification-web" version = "0.17.0" dependencies = [ - "burn", - "burn-candle", + "burn 0.17.0", + "burn-candle 0.17.0", "burn-import", "console_error_panic_hook", "js-sys", @@ -3760,6 +4261,18 @@ dependencies = [ "hashbrown 0.15.2", ] +[[package]] +name = "lstm" +version = "0.1.0" +dependencies = [ + "burn 0.16.0", + "clap", + "polars", + "rand", + "rand_distr", + "serde", +] + [[package]] name = "lz4" version = "1.28.1" @@ -3985,7 +4498,7 @@ dependencies = [ name = "mnist" version = "0.17.0" dependencies = [ - "burn", + "burn 0.17.0", "log", "serde", ] @@ -3994,7 +4507,7 @@ dependencies = [ name = "mnist-inference-web" version = "0.17.0" dependencies = [ - "burn", + "burn 0.17.0", "console_error_panic_hook", "js-sys", "serde", @@ -4006,7 +4519,7 @@ dependencies = [ name = "model" version = "0.6.0" dependencies = [ - "burn", + "burn 0.17.0", "burn-import", ] @@ -4053,6 +4566,27 @@ dependencies = [ "target-features", ] +[[package]] +name = "naga" +version = "23.1.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "364f94bc34f61332abebe8cad6f6cd82a5b65cff22c828d05d0968911462ca4f" +dependencies = [ + "arrayvec", + "bit-set", + "bitflags 2.8.0", + "cfg_aliases 0.1.1", + "codespan-reporting", + "hexf-parse", + "indexmap", + "log", + "rustc-hash 1.1.0", + "spirv 0.3.0+sdk-1.3.268.0", + "termcolor", + "thiserror 1.0.69", + "unicode-xid", +] + [[package]] name = "naga" version = "24.0.0" @@ -4062,7 +4596,7 @@ dependencies = [ "arrayvec", "bit-set", "bitflags 2.8.0", - "cfg_aliases", + "cfg_aliases 0.2.1", "codespan-reporting", "hexf-parse", "indexmap", @@ -4079,7 +4613,7 @@ dependencies = [ name = "named-tensor" version = "0.17.0" dependencies = [ - "burn", + "burn 0.17.0", "serde", ] @@ -4555,7 +5089,7 @@ dependencies = [ name = "onnx-inference" version = "0.17.0" dependencies = [ - "burn", + "burn 0.17.0", "burn-import", "serde", ] @@ -4581,9 +5115,9 @@ dependencies = [ name = "onnx-tests" version = "0.17.0" dependencies = [ - "burn", + "burn 0.17.0", "burn-import", - "burn-ndarray", + "burn-ndarray 0.17.0", "float-cmp", "serde", ] @@ -5651,7 +6185,7 @@ dependencies = [ name = "pytorch-import" version = "0.17.0" dependencies = [ - "burn", + "burn 0.17.0", "burn-import", "model", ] @@ -5660,10 +6194,10 @@ dependencies = [ name = "pytorch-tests" version = "0.17.0" dependencies = [ - "burn", - "burn-autodiff", + "burn 0.17.0", + "burn-autodiff 0.17.0", "burn-import", - "burn-ndarray", + "burn-ndarray 0.17.0", "float-cmp", "serde", ] @@ -5737,7 +6271,7 @@ version = "0.5.9" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "1c40286217b4ba3a71d644d752e6a0b71f13f1b6a2c5311acfcbe0c2418ed904" dependencies = [ - "cfg_aliases", + "cfg_aliases 0.2.1", "libc", "once_cell", "socket2", @@ -6625,7 +7159,7 @@ dependencies = [ name = "server" version = "0.17.0" dependencies = [ - "burn", + "burn 0.17.0", "cfg-if", ] @@ -6738,7 +7272,7 @@ checksum = "e3a9fe34e3e7a50316060351f37187a3f546bce95496156754b601a5fa71b76e" name = "simple-regression" version = "0.17.0" dependencies = [ - "burn", + "burn 0.17.0", "log", "rgb", "serde", @@ -7149,7 +7683,7 @@ dependencies = [ name = "text-classification" version = "0.17.0" dependencies = [ - "burn", + "burn 0.17.0", "derive-new 0.7.0", "serde", "tokenizers", @@ -7159,7 +7693,7 @@ dependencies = [ name = "text-generation" version = "0.17.0" dependencies = [ - "burn", + "burn 0.17.0", "derive-new 0.7.0", "log", "serde", @@ -8095,10 +8629,35 @@ checksum = "53a85b86a771b1c87058196170769dd264f66c0782acf1ae6cc51bfd64b39082" name = "wgan" version = "0.1.0" dependencies = [ - "burn", + "burn 0.17.0", "image", ] +[[package]] +name = "wgpu" +version = "23.0.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "80f70000db37c469ea9d67defdc13024ddf9a5f1b89cb2941b812ad7cde1735a" +dependencies = [ + "arrayvec", + "cfg_aliases 0.1.1", + "document-features", + "js-sys", + "log", + "naga 23.1.0", + "parking_lot 0.12.3", + "profiling", + "raw-window-handle", + "smallvec", + "static_assertions", + "wasm-bindgen", + "wasm-bindgen-futures", + "web-sys", + "wgpu-core 23.0.1", + "wgpu-hal 23.0.1", + "wgpu-types 23.0.0", +] + [[package]] name = "wgpu" version = "24.0.1" @@ -8107,11 +8666,11 @@ checksum = "47f55718f85c2fa756edffa0e7f0e0a60aba463d1362b57e23123c58f035e4b6" dependencies = [ "arrayvec", "bitflags 2.8.0", - "cfg_aliases", + "cfg_aliases 0.2.1", "document-features", "js-sys", "log", - "naga", + "naga 24.0.0", "parking_lot 0.12.3", "profiling", "raw-window-handle", @@ -8120,9 +8679,34 @@ dependencies = [ "wasm-bindgen", "wasm-bindgen-futures", "web-sys", - "wgpu-core", - "wgpu-hal", - "wgpu-types", + "wgpu-core 24.0.0", + "wgpu-hal 24.0.0", + "wgpu-types 24.0.0", +] + +[[package]] +name = "wgpu-core" +version = "23.0.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "d63c3c478de8e7e01786479919c8769f62a22eec16788d8c2ac77ce2c132778a" +dependencies = [ + "arrayvec", + "bit-vec", + "bitflags 2.8.0", + "cfg_aliases 0.1.1", + "document-features", + "indexmap", + "log", + "naga 23.1.0", + "once_cell", + "parking_lot 0.12.3", + "profiling", + "raw-window-handle", + "rustc-hash 1.1.0", + "smallvec", + "thiserror 1.0.69", + "wgpu-hal 23.0.1", + "wgpu-types 23.0.0", ] [[package]] @@ -8134,11 +8718,11 @@ dependencies = [ "arrayvec", "bit-vec", "bitflags 2.8.0", - "cfg_aliases", + "cfg_aliases 0.2.1", "document-features", "indexmap", "log", - "naga", + "naga 24.0.0", "once_cell", "parking_lot 0.12.3", "profiling", @@ -8146,8 +8730,53 @@ dependencies = [ "rustc-hash 1.1.0", "smallvec", "thiserror 2.0.11", - "wgpu-hal", - "wgpu-types", + "wgpu-hal 24.0.0", + "wgpu-types 24.0.0", +] + +[[package]] +name = "wgpu-hal" +version = "23.0.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "89364b8a0b211adc7b16aeaf1bd5ad4a919c1154b44c9ce27838213ba05fd821" +dependencies = [ + "android_system_properties", + "arrayvec", + "ash", + "bit-set", + "bitflags 2.8.0", + "block", + "bytemuck", + "cfg_aliases 0.1.1", + "core-graphics-types", + "glow 0.14.2", + "glutin_wgl_sys", + "gpu-alloc", + "gpu-allocator", + "gpu-descriptor", + "js-sys", + "khronos-egl", + "libc", + "libloading", + "log", + "metal 0.29.0", + "naga 23.1.0", + "ndk-sys", + "objc", + "once_cell", + "parking_lot 0.12.3", + "profiling", + "range-alloc", + "raw-window-handle", + "renderdoc-sys", + "rustc-hash 1.1.0", + "smallvec", + "thiserror 1.0.69", + "wasm-bindgen", + "web-sys", + "wgpu-types 23.0.0", + "windows 0.58.0", + "windows-core 0.58.0", ] [[package]] @@ -8163,9 +8792,9 @@ dependencies = [ "bitflags 2.8.0", "block", "bytemuck", - "cfg_aliases", + "cfg_aliases 0.2.1", "core-graphics-types", - "glow", + "glow 0.16.0", "glutin_wgl_sys", "gpu-alloc", "gpu-allocator", @@ -8176,7 +8805,7 @@ dependencies = [ "libloading", "log", "metal 0.31.0", - "naga", + "naga 24.0.0", "ndk-sys", "objc", "once_cell", @@ -8191,11 +8820,22 @@ dependencies = [ "thiserror 2.0.11", "wasm-bindgen", "web-sys", - "wgpu-types", + "wgpu-types 24.0.0", "windows 0.58.0", "windows-core 0.58.0", ] +[[package]] +name = "wgpu-types" +version = "23.0.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "610f6ff27778148c31093f3b03abc4840f9636d58d597ca2f5977433acfe0068" +dependencies = [ + "bitflags 2.8.0", + "js-sys", + "web-sys", +] + [[package]] name = "wgpu-types" version = "24.0.0" diff --git a/examples/modern-lstm/Cargo.toml b/examples/modern-lstm/Cargo.toml new file mode 100644 index 0000000000..a37f651d09 --- /dev/null +++ b/examples/modern-lstm/Cargo.toml @@ -0,0 +1,29 @@ +[package] +name = "lstm" +version = "0.1.0" +edition = "2021" + +[features] +ndarray = ["burn/ndarray"] +ndarray-blas-accelerate = ["burn/ndarray", "burn/accelerate"] +ndarray-blas-netlib = ["burn/ndarray", "burn/blas-netlib"] +ndarray-blas-openblas = ["burn/ndarray", "burn/openblas"] +tch-cpu = ["burn/tch"] +tch-gpu = ["burn/tch"] +wgpu = ["burn/wgpu"] + +[dependencies] +burn = { version="0.16.0", features=["train"] } + +# Random number generator +rand = { version="0.8.5" } +rand_distr = { version="0.4.3" } + +# Serialization +serde = { version="1.0.210", features=["std", "derive"] } + +# Organise the results in dataframe +polars = { version="0.44.1" } + +# Command line parser +clap = { version="4.5.21", features=["derive"] } \ No newline at end of file diff --git a/examples/modern-lstm/README.md b/examples/modern-lstm/README.md new file mode 100644 index 0000000000..5a41f078b1 --- /dev/null +++ b/examples/modern-lstm/README.md @@ -0,0 +1,22 @@ +# Advanced LSTM Implementation with Burn +A sophisticated implementation of Long Short-Term Memory (LSTM) networks in Burn, featuring state-of-the-art architectural enhancements and optimizations. This implementation includes bidirectional processing capabilities and advanced regularization techniques, making it suitable for both research and production environments. More details can be found at the [PyTorch implementation](https://github.com/shiv08/Advanced-LSTM-Implementation-with-PyTorch). + +`LstmNetwork` is the top-level module with bidirectional support and output projection. It can support multiple LSTM variants by setting appropriate `bidirectional` and `num_layers`： +* LSTM: `num_layers = 1` and `bidirectional = false` +* Stacked LSTM: `num_layers > 1` and `bidirectional = false` +* Birectional LSTM: `num_layers = 1` and `bidirectional = true` +* Birectional Stacked LSTM: `num_layers > 1` and `birectional = true` + +This implementation is complementary to Burn's official LSTM, users can choose either one depends on the project's specific needs. + +## Example Usage + +### Training +```sh +cargo run --release --features ndarray -- train --artifact-dir /home/wangjw/data/work/projects/lstm/output --num-epochs 30 --batch-size 32 --num-workers 2 --lr 0.001 +``` + +### Inference +```sh +cargo run --release --features ndarray -- infer --artifact-dir /home/wangjw/data/work/projects/lstm/output +``` diff --git a/examples/modern-lstm/src/cli.rs b/examples/modern-lstm/src/cli.rs new file mode 100644 index 0000000000..8d3f4a9582 --- /dev/null +++ b/examples/modern-lstm/src/cli.rs @@ -0,0 +1,42 @@ +use clap::{Parser, Subcommand}; + +/// A CLI for long short-term memory network +#[derive(Parser, Debug)] +#[command(version, author, about, long_about=None)] +pub struct Cli { + #[command(subcommand)] + pub command: Commands, +} + +#[derive(Subcommand, Debug)] +pub enum Commands { + /// Train a model + Train { + /// Path to save trained model + #[arg(long)] + artifact_dir: String, + + /// Number of epochs of training + #[arg(long, default_value="200")] + num_epochs: usize, + + /// Size of the batches + #[arg(long, default_value="64")] + batch_size: usize, + + /// Number of cpu threads to use during batch generation + #[arg(long, default_value="8")] + num_workers: usize, + + /// Learning rate + #[arg(long, default_value="0.00005")] + lr: f64, + }, + + /// Inference + Infer { + /// Path to the trained model + #[arg(long)] + artifact_dir: String, + } +} \ No newline at end of file diff --git a/examples/modern-lstm/src/dataset.rs b/examples/modern-lstm/src/dataset.rs new file mode 100644 index 0000000000..7845159b1d --- /dev/null +++ b/examples/modern-lstm/src/dataset.rs @@ -0,0 +1,111 @@ +use burn::{ + data::{ + dataloader::batcher::Batcher, + dataset::{Dataset, InMemDataset}, + }, + prelude::*, +}; +use rand::Rng; +use rand_distr::{Normal, Distribution}; +use serde::{Deserialize, Serialize}; + +// Dataset parameters +pub const NUM_SEQUENCES: usize = 1000; +pub const SEQ_LENGTH: usize = 10; +pub const NOISE_LEVEL: f32 = 0.1; +pub const RANDOM_SEED: u64 = 5; + +// Generate a sequence where each number is the sum of previous two numbers plus noise +#[derive(Clone, Debug, Serialize, Deserialize)] +pub struct SequenceDatasetItem { + pub sequence: Vec, + pub target: f32, +} + +impl SequenceDatasetItem { + pub fn new(seq_length: usize, noise_level: f32) -> Self { + // Start with two random numbers between 0 and 1 + let mut seq = vec![rand::thread_rng().gen(), rand::thread_rng().gen()]; + + // Generate sequence + for _i in 0..seq_length { + // Next number is sum of previous two plus noise + let normal = Normal::new(0.0, noise_level).unwrap(); + let next_val = seq[seq.len()-2] + seq[seq.len()-1] + normal.sample(&mut rand::thread_rng()); + seq.push(next_val); + } + + Self { + // Convert to sequence and target + sequence: seq[0..seq.len()-1].to_vec(), // All but last + target: seq[seq.len()-1], // Last value + } + } +} + +// Custom Dataset for Sequence Data +pub struct SequenceDataset { + dataset: InMemDataset, +} + +impl SequenceDataset { + pub fn new(num_sequences: usize, seq_length: usize, noise_level: f32) -> Self { + let mut items = vec![]; + for _i in 0..num_sequences { + items.push(SequenceDatasetItem::new(seq_length, noise_level)); + } + let dataset = InMemDataset::new(items); + + Self { dataset } + } +} + +impl Dataset for SequenceDataset { + fn get(&self, index: usize) -> Option { + self.dataset.get(index) + } + + fn len(&self) -> usize { + self.dataset.len() + } +} + +#[derive(Clone, Debug)] +pub struct SequenceBatcher { + device: B::Device, +} + +#[derive(Clone, Debug)] +pub struct SequenceBatch { + pub sequences: Tensor, // [batch_size, seq_length, input_size] + pub targets: Tensor, // [batch_size, 1] +} + +impl SequenceBatcher { + pub fn new(device: B::Device) -> Self { + Self { + device, + } + } +} + +impl Batcher> for SequenceBatcher { + fn batch(&self, items: Vec) -> SequenceBatch { + let mut sequences: Vec> = Vec::new(); + + for item in items.iter() { + let seq_tensor = Tensor::::from_floats(item.sequence.as_slice(), &self.device); + // Add feature dimension, the input_size is 1 implicitly. We can change the input_size here with some operations + sequences.push(seq_tensor.unsqueeze_dims(&[-1])); + } + let sequences = Tensor::stack(sequences, 0); + + let targets = items + .iter() + .map(|item| Tensor::::from_floats([item.target], &self.device)) + .collect(); + let targets = Tensor::stack(targets, 0); + + SequenceBatch { sequences, targets } + } +} \ No newline at end of file diff --git a/examples/modern-lstm/src/inference.rs b/examples/modern-lstm/src/inference.rs new file mode 100644 index 0000000000..3cd23c75a0 --- /dev/null +++ b/examples/modern-lstm/src/inference.rs @@ -0,0 +1,46 @@ +use crate::{ + dataset::{ + SequenceBatcher, + SequenceDataset, + SequenceDatasetItem, + NUM_SEQUENCES, SEQ_LENGTH, NOISE_LEVEL, + }, + training::TrainingConfig, + model::LstmNetwork, +}; +use polars::prelude::*; +use burn::{ + data::{dataset::Dataset, dataloader::batcher::Batcher}, + prelude::*, + record::{CompactRecorder, Recorder}, +}; + +pub fn infer(artifact_dir: &str, device: B::Device) { + // Loading model + let config = TrainingConfig::load(format!("{artifact_dir}/config.json")) + .expect("Config should exist for the model; run train first"); + let record = CompactRecorder::new() + .load(format!("{artifact_dir}/model").into(), &device) + .expect("Trained model should exist; run train first"); + + let model: LstmNetwork = config.model.init(&device).load_record(record); + + let dataset = SequenceDataset::new(NUM_SEQUENCES / 5, SEQ_LENGTH, NOISE_LEVEL); + let items: Vec = dataset.iter().collect(); + + let batcher = SequenceBatcher::new(device); + // Put all items in one batch + let batch = batcher.batch(items); + let predicted = model.forward(batch.sequences, None); + let targets = batch.targets; + + let predicted = predicted.squeeze::<1>(1).into_data(); + let expected = targets.squeeze::<1>(1).into_data(); + + // Display the predicted vs expected values + let results = df![ + "predicted" => &predicted.to_vec::().unwrap(), + "expected" => &expected.to_vec::().unwrap(), + ].unwrap(); + println!("{}", &results.head(Some(10))); +} \ No newline at end of file diff --git a/examples/modern-lstm/src/lib.rs b/examples/modern-lstm/src/lib.rs new file mode 100644 index 0000000000..d355a91bc2 --- /dev/null +++ b/examples/modern-lstm/src/lib.rs @@ -0,0 +1,5 @@ +pub mod dataset; +pub mod model; +pub mod training; +pub mod inference; +pub mod cli; diff --git a/examples/modern-lstm/src/main.rs b/examples/modern-lstm/src/main.rs new file mode 100644 index 0000000000..a803578c32 --- /dev/null +++ b/examples/modern-lstm/src/main.rs @@ -0,0 +1,170 @@ +#[cfg(any( + feature = "ndarray", + feature = "ndarray-blas-netlib", + feature = "ndarray-blas-openblas", + feature = "ndarray-blas-accelerate", +))] +mod ndarray { + use burn::{ + backend::{ndarray::{NdArray, NdArrayDevice}, Autodiff}, + grad_clipping::GradientClippingConfig, + optim::AdamConfig, + }; + use lstm::{ + cli::{Cli, Commands}, + inference::infer, + model::LstmNetworkConfig, + training::{TrainingConfig, train}, + }; + + pub fn run(cli: Cli) { + let device = NdArrayDevice::Cpu; + + match cli.command { + Commands::Train {artifact_dir, num_epochs, batch_size, num_workers, lr} => { + let config = TrainingConfig::new( + LstmNetworkConfig::new(), + // Gradient clipping via optimizer config + AdamConfig::new().with_grad_clipping(Some(GradientClippingConfig::Norm(1.0))), + num_epochs, + batch_size, + num_workers, + lr); + train::>(&artifact_dir, config, device); + }, + Commands::Infer {artifact_dir} => { + infer::(&artifact_dir, device); + }, + } + } +} + +#[cfg(feature = "tch-cpu")] +mod tch_cpu { + use burn::{ + backend::{libtorch::{LibTorch, LibTorchDevice}, Autodiff}, + grad_clipping::GradientClippingConfig, + optim::AdamConfig + }; + use lstm::{ + cli::{Cli, Commands}, + inference::infer, + model::LstmNetworkConfig, + training::{TrainingConfig, train}, + }; + + pub fn run(cli: Cli) { + let device = LibTorchDevice::Cpu; + + match cli.command { + Commands::Train {artifact_dir, num_epochs, batch_size, num_workers, lr} => { + let config = TrainingConfig::new( + LstmNetworkConfig::new(), + // Gradient clipping via optimizer config + AdamConfig::new().with_grad_clipping(Some(GradientClippingConfig::Norm(1.0))), + num_epochs, + batch_size, + num_workers, + lr); + train::>(&artifact_dir, config, device); + }, + Commands::Infer {artifact_dir} => { + infer::(&artifact_dir, device); + }, + } + } +} + +#[cfg(feature = "tch-gpu")] +mod tch_gpu { + use burn::{ + backend::{libtorch::{LibTorch, LibTorchDevice}, Autodiff}, + grad_clipping::GradientClippingConfig, + optim::AdamConfig + }; + use lstm::{ + cli::{Cli, Commands}, + inference::infer, + model::LstmNetworkConfig, + training::{TrainingConfig, train}, + }; + + pub fn run(cli: Cli) { + #[cfg(not(target_os = "macos"))] + let device = LibTorchDevice::Cuda(0); + #[cfg(target_os = "macos")] + let device = LibTorchDevice::Mps; + + match cli.command { + Commands::Train {artifact_dir, num_epochs, batch_size, num_workers, lr} => { + let config = TrainingConfig::new( + LstmNetworkConfig::new(), + // Gradient clipping via optimizer config + AdamConfig::new().with_grad_clipping(Some(GradientClippingConfig::Norm(1.0))), + num_epochs, + batch_size, + num_workers, + lr); + train::>(&artifact_dir, config, device); + }, + Commands::Infer {artifact_dir} => { + infer::(&artifact_dir, device); + }, + } + } +} + +#[cfg(feature = "wgpu")] +mod wgpu { + use burn::{ + backend::{wgpu::{Wgpu, WgpuDevice}, Autodiff}, + grad_clipping::GradientClippingConfig, + optim::AdamConfig + }; + use lstm::{ + cli::{Cli, Commands}, + inference::infer, + model::LstmNetworkConfig, + training::{TrainingConfig, train}, + }; + + pub fn run(cli: Cli) { + let device = WgpuDevice::default(); + + match cli.command { + Commands::Train {artifact_dir, num_epochs, batch_size, num_workers, lr} => { + let config = TrainingConfig::new( + LstmNetworkConfig::new(), + // Gradient clipping via optimizer config + AdamConfig::new().with_grad_clipping(Some(GradientClippingConfig::Norm(1.0))), + num_epochs, + batch_size, + num_workers, + lr); + train::>(&artifact_dir, config, device); + }, + Commands::Infer {artifact_dir} => { + infer::(&artifact_dir, device); + }, + } + } +} + +use clap::Parser; +use lstm::cli::Cli; +fn main() { + let cli = Cli::parse(); + #[cfg(any( + feature = "ndarray", + feature = "ndarray-blas-netlib", + feature = "ndarray-blas-openblas", + feature = "ndarray-blas-accelerate", + ))] + ndarray::run(cli); + #[cfg(feature = "tch-cpu")] + tch_cpu::run(cli); + #[cfg(feature = "tch-gpu")] + tch_gpu::run(cli); + #[cfg(feature = "wgpu")] + wgpu::run(cli); +} diff --git a/examples/modern-lstm/src/model.rs b/examples/modern-lstm/src/model.rs new file mode 100644 index 0000000000..f580554b18 --- /dev/null +++ b/examples/modern-lstm/src/model.rs @@ -0,0 +1,336 @@ +use burn::{ + nn::{LayerNorm, LayerNormConfig, Dropout, DropoutConfig, Initializer, Linear, LinearConfig, LstmState, Sigmoid, Tanh}, + prelude::*, +}; + +// LSTM Cell implementation with layer normalization. +// +// Mathematical formulation of LSTM: +// f_t = σ(W_f · [h_{t-1}, x_t] + b_f) # Forget gate +// i_t = σ(W_i · [h_{t-1}, x_t] + b_i] # Input gate +// g_t = tanh(W_g · [h_{t-1}, x_t] + b_g] # Candidate cell state +// o_t = σ(W_o · [h_{t-1}, x_t] + b_o) # Output gate +// +// c_t = f_t ⊙ c_{t-1} + i_t ⊙ g_t # New cell state +// h_t = o_t ⊙ tanh(c_t) # New hidden state +// +// where: +// - σ is the sigmoid function +// - ⊙ is the element-wise multiplication +// - [h_{t-1}, x_t] represents concatenation + +#[derive(Module, Debug)] +pub struct LstmCell { + pub hidden_size: usize, + // Combined weight matrices for efficiency + // weight_ih layer uses combined weights for [i_t, f_t, g_t, o_t] for input x_t + // weight_hh layer uses combined weights for [i_t, f_t, g_t, o_t] for hidden state h_{t-1} + pub weight_ih: Linear, + pub weight_hh: Linear, + // Layer Normalization for better training stability. Don't use BatchNorm because the input distribution is always changing for LSTM. + pub norm_x: LayerNorm, // Normalize gate pre-activations + pub norm_h: LayerNorm, // Normalize hidden state + pub norm_c: LayerNorm, // Normalize cell state + pub dropout: Dropout, +} + +// Configuration to create a Lstm module using the init function. +#[derive(Config, Debug)] +pub struct LstmCellConfig { + // The size of the input features + pub input_size: usize, + // The size of the hidden state + pub hidden_size: usize, + // The number of hidden layers + pub dropout: f64, +} + +impl LstmCellConfig { + // Initialize parameters using best practices: + // 1. Orthogonal initialization for better gradient flow (here we use Xavier because of the lack of Orthogonal in burn) + // 2. Initialize forget gate bias to 1.0 to prevent forgetting at start of training + pub fn init(&self, device: &B::Device) -> LstmCell { + let initializer = Initializer::XavierNormal{gain:1.0}; + let init_bias = Tensor::::ones([self.hidden_size], &device); + + let mut weight_ih = LinearConfig::new(self.input_size, 4 * self.hidden_size) + .with_initializer(initializer.clone()) + .init(device); + // Set forget gate bias to 1.0 (helps with learning long sequences) + let bias = weight_ih.bias.clone().unwrap().val().slice_assign([self.hidden_size..2*self.hidden_size], init_bias.clone()); + weight_ih.bias = weight_ih.bias.map(|p| p.map(|_t| bias)); + + let mut weight_hh = LinearConfig::new(self.hidden_size, 4 * self.hidden_size) + .with_initializer(initializer) + .init(device); + let bias = weight_hh.bias.clone().unwrap().val().slice_assign([self.hidden_size..2*self.hidden_size], init_bias); + weight_hh.bias = weight_hh.bias.map(|p| p.map(|_t| bias)); + + LstmCell { + hidden_size: self.hidden_size, + weight_ih, + weight_hh, + norm_x: LayerNormConfig::new(4 * self.hidden_size).init(device), + norm_h: LayerNormConfig::new(self.hidden_size).init(device), + norm_c: LayerNormConfig::new(self.hidden_size).init(device), + dropout: DropoutConfig::new(self.dropout).init(), + } + } +} + +impl LstmCell { + // Forward pass of LSTM cell. + // Args: + // x: Input tensor of shape (batch_size, input_size) + // state: Tuple of (h_{t-1}, c_{t-1}) each of shape (batch_size, hidden_size) + // Returns: + // Tuple of (h_t, c_t) representing new hidden and cell states + pub fn forward( + &self, + x: Tensor, + state: LstmState, + ) -> LstmState { + let (h_prev, c_prev) = (state.hidden, state.cell); + + // Combined matrix multiplication for all gates + // Shape: (batch_size, 4 * hidden_size) + let gates_x = self.weight_ih.forward(x); // Transform input + let gates_h = self.weight_hh.forward(h_prev); // Transform previous hidden state + + // Apply layer normalization + let gates_x = self.norm_x.forward(gates_x); + // Combined gate pre-activations + let gates = gates_x + gates_h; + + // Split into individual gates + // Each gate shape: (batch_size, hidden_size) + let gates = gates.chunk(4, 1); + let i_gate = gates[0].clone(); + let f_gate = gates[1].clone(); + let g_gate = gates[2].clone(); + let o_gate = gates[3].clone(); + + // Apply gate non-linearities + let i_t = Sigmoid::new().forward(i_gate); + let f_t = Sigmoid::new().forward(f_gate); + let g_t = Tanh::new().forward(g_gate); + let o_t = Sigmoid::new().forward(o_gate); + + // Update cell state: c_t = f_t ⊙ c_{t-1} + i_t ⊙ g_t + let c_t = f_t * c_prev + i_t * g_t; + let c_t = self.norm_c.forward(c_t); + + // Update cell state: h_t = o_t ⊙ tanh(c_t) + let h_t = o_t * Tanh::new().forward(c_t.clone()); + let h_t = self.norm_h.forward(h_t); + + let h_t = self.dropout.forward(h_t); + + LstmState::new(h_t, c_t) + } + + // Initialize cell state and hidden state if provided or with zeros + pub fn init_state( + &self, + batch_size: usize, + device: &B::Device, + ) -> LstmState { + let cell = Tensor::zeros([batch_size, self.hidden_size], device); + let hidden = Tensor::zeros([batch_size, self.hidden_size], device); + + LstmState::new(cell, hidden) + } +} + +// Stacked LSTM implementation supporting multiple layers +// Each layer processes the output of the previous layer +#[derive(Module, Debug)] +pub struct StackedLstm { + pub layers: Vec>, +} + +#[derive(Config, Debug)] +pub struct StackedLstmConfig { + pub input_size: usize, + pub hidden_size: usize, + pub num_layers: usize, + pub dropout: f64, +} + +impl StackedLstmConfig { + pub fn init(&self, device: &B::Device) -> StackedLstm { + let mut layers: Vec> = vec![]; + // Create list of LSTM cells, one for each layer + for i in 0..self.num_layers { + if i == 0 { + if i < self.num_layers - 1 { + layers.push(LstmCellConfig::new(self.input_size, self.hidden_size, self.dropout).init(device)); + } else { // No dropout on last layer + layers.push(LstmCellConfig::new(self.input_size, self.hidden_size, 0.0).init(device)); + } + } else { + if i < self.num_layers -1 { + layers.push(LstmCellConfig::new(self.hidden_size, self.hidden_size, self.dropout).init(device)); + } else { // No dropout on last layer + layers.push(LstmCellConfig::new(self.hidden_size, self.hidden_size, 0.0).init(device)); + } + } + } + StackedLstm { layers } + } +} + +impl StackedLstm { + // Process input sequence through stacked LSTM layers. + // + // Args: + // x: Input tensor of shape (batch_size, seq_length, input_size) + // states: Optional initial states for each layer + // + // Returns: + // Tuple of (output, states) where output has shape (batch_size, seq_length, hidden_size) + // and states is a vector of length num_layers, both cell and hidden state in each element have shape (batch_size, hidden_size) + pub fn forward( + &self, + x: Tensor, + states: Option>>, + ) -> (Tensor::, Vec>) { + let [batch_size, seq_length, _] = x.dims(); + let device = x.device(); + + let mut states = match states { + None => { + let mut temp: Vec> = vec![]; + for layer in self.layers.iter() { + temp.push(layer.init_state(batch_size, &device)); + } + temp + }, + _ => states.unwrap(), + }; + + let mut layer_outputs = vec![]; + for t in 0..seq_length { + let mut input_t = x.clone().slice([None, Some((t as i64, t as i64 + 1)), None]).squeeze::<2>(1); + for (i, lstm_cell) in self.layers.iter().enumerate() { + let mut state: LstmState = LstmState::new(states[i].cell.clone(), states[i].hidden.clone()); + state = lstm_cell.forward(input_t, state); + input_t = state.hidden.clone(); + states[i] = state; + } + layer_outputs.push(input_t); + } + + // Stack output along sequence dimension + let output = Tensor::stack(layer_outputs, 1); + + (output, states) + } +} + +// Complete LSTM network with bidirectional support. +// +// In bidirectional mode: +// - Forward LSTM processes sequence from left to right +// - Backward LSTM processes sequence from right to left +// - Outputs are concatenated for final prediction +#[derive(Module, Debug)] +pub struct LstmNetwork { + // Forward direction LSTM + pub stacked_lstm: StackedLstm, + // Optional backward direction LSTM for bidirectional processing + pub reverse_lstm: Option>, + pub dropout: Dropout, + pub fc: Linear, +} + +#[derive(Config, Debug)] +pub struct LstmNetworkConfig { + #[config(default = 1)] + pub input_size: usize, // Single feature (number sequence) + #[config(default = 32)] + pub hidden_size: usize, // Size of LSTM hidden state + #[config(default = 2)] + pub num_layers: usize, // Number of LSTM layers + #[config(default = 1)] + pub output_size: usize, // Predict one number + #[config(default = 0.1)] + pub dropout: f64, + #[config(default = true)] + pub bidirectional: bool, // Use bidirectional LSTM +} + +impl LstmNetworkConfig { + pub fn init(&self, device: &B::Device) -> LstmNetwork { + // Forward direction LSTM + let stacked_lstm = StackedLstmConfig::new(self.input_size, self.hidden_size, self.num_layers, self.dropout) + .init(device); + + // Optional backward direction LSTM for bidirectional processing + let (reverse_lstm, hidden_size) = if self.bidirectional { + let lstm = StackedLstmConfig::new(self.input_size, self.hidden_size, self.num_layers, self.dropout) + .init(device); + (Some(lstm), 2*self.hidden_size) + } else { + (None, self.hidden_size) + }; + + let fc = LinearConfig::new(hidden_size, self.output_size).init(device); + let dropout = DropoutConfig::new(self.dropout).init(); + + LstmNetwork { + stacked_lstm, + reverse_lstm, + dropout, + fc, + } + } +} + +impl LstmNetwork { + // Forward pass of the network. + // + // For bidirectional processing: + // 1. Process sequence normally with forward LSTM + // 2. Process reversed sequence with backward LSTM + // 3. Concatenate both outputs + // 4. Apply final linear transformation + // + // Args: + // x: Input tensor of shape (batch_size, seq_length, input_size) + // states: Optional initial states + // + // Returns: + // Output tensor of shape (batch_size, output_size) + pub fn forward( + &self, + x: Tensor, + states: Option>>, + ) -> Tensor { + let seq_length = x.dims()[1] as i64; + // Forward direction + let (mut output, _states) = self.stacked_lstm.forward(x.clone(), states); + + output = match &self.reverse_lstm { + Some(reverse_lstm) => { + //Process sequence in reverse direction + let (mut reverse_output, _states) = reverse_lstm.forward(x.flip([1]), None); + // Flip back to align with forward sequence + reverse_output = reverse_output.flip([1]); + // Concatenate forward and backward outputs along the feature dimension + output = Tensor::cat(vec![output, reverse_output], 2); + output + }, + None => output + }; + + // Apply dropout before final layer + output = self.dropout.forward(output); + // Use final timestep output for prediction + let final_output = self.fc.forward( + output.slice([None, Some((seq_length-1, seq_length)), None]).squeeze::<2>(1) + ); + + final_output + } +} \ No newline at end of file diff --git a/examples/modern-lstm/src/training.rs b/examples/modern-lstm/src/training.rs new file mode 100644 index 0000000000..5da43e4355 --- /dev/null +++ b/examples/modern-lstm/src/training.rs @@ -0,0 +1,125 @@ +use burn::{ + data::dataloader::DataLoaderBuilder, + module::AutodiffModule, + nn::loss::{MseLoss, Reduction::Mean}, + optim::{AdamConfig, GradientsParams, Optimizer}, + prelude::*, + record::CompactRecorder, + tensor::backend::AutodiffBackend, +}; + +use crate::dataset::{ + SequenceBatcher, + SequenceDataset, + NUM_SEQUENCES, SEQ_LENGTH, NOISE_LEVEL, RANDOM_SEED, +}; +use crate::model::{LstmNetwork, LstmNetworkConfig}; + +#[derive(Config)] +pub struct TrainingConfig { + pub model: LstmNetworkConfig, + pub optimizer: AdamConfig, + pub num_epochs: usize, + pub batch_size: usize, + pub num_workers: usize, + pub lr: f64, +} + +// Create the directory to save the model and model config +fn create_artifact_dir(artifact_dir: &str) { + // Remove existing artifacts + std::fs::remove_dir_all(artifact_dir).ok(); + std::fs::create_dir_all(artifact_dir).ok(); +} + +pub fn train(artifact_dir: &str, config: TrainingConfig, device: B::Device) { + create_artifact_dir(artifact_dir); + + // Save training config + config + .save(format!("{artifact_dir}/config.json")) + .expect("Config should be saved successfully"); + B::seed(RANDOM_SEED); + + // Create the model and optimizer + let mut model = config.model.init::(&device); + let mut optim = config.optimizer.init::>(); + + // Create the batcher + let batcher_train = SequenceBatcher::::new(device.clone()); + let batcher_valid = SequenceBatcher::::new(device.clone()); + + // Create the dataloaders + let dataloader_train = DataLoaderBuilder::new(batcher_train) + .batch_size(config.batch_size) + .shuffle(RANDOM_SEED) + .num_workers(config.num_workers) + .build(SequenceDataset::new(NUM_SEQUENCES, SEQ_LENGTH, NOISE_LEVEL)); + + let dataloader_valid = DataLoaderBuilder::new(batcher_valid) + .batch_size(config.batch_size) + .shuffle(RANDOM_SEED) + .num_workers(config.num_workers) + // 20% size of training + .build(SequenceDataset::new(NUM_SEQUENCES / 5, SEQ_LENGTH, NOISE_LEVEL)); + + let train_num_items = dataloader_train.num_items(); + let valid_num_items = dataloader_valid.num_items(); + + println!("Starting training..."); + // Iterate over our training for X epochs + for epoch in 1..config.num_epochs + 1 { + // Initialize the training and validation metrics at the start of each epoch + let mut train_losses = vec![]; + let mut train_loss = 0.0; + let mut valid_losses = vec![]; + let mut valid_loss = 0.0; + + // Implement our training loop + for (_iteration, batch) in dataloader_train.iter().enumerate() { + let output = model.forward(batch.sequences, None); + let loss = MseLoss::new().forward(output, batch.targets.clone(), Mean); + train_loss += loss.clone().into_scalar().elem::() * batch.targets.dims()[0] as f32; + + // Gradients for the current backward pass + let grads = loss.backward(); + // Gradients linked to each parameter of the model + let grads = GradientsParams::from_grads(grads, &model); + // Update the model using the optimizer + model = optim.step(config.lr, model, grads); + } + + // The averaged train loss per epoch + let avg_train_loss = train_loss / train_num_items as f32; + train_losses.push(avg_train_loss); + + // Get the model without autodiff + let valid_model = model.valid(); + + // Implement our validation loop + for (_iteration, batch) in dataloader_valid.iter().enumerate() { + let output = valid_model.forward(batch.sequences, None); + let loss = MseLoss::new().forward(output, batch.targets.clone(), Mean); + valid_loss += loss.clone().into_scalar().elem::() * batch.targets.dims()[0] as f32; + } + // The averaged train loss per epoch + let avg_valid_loss = valid_loss / valid_num_items as f32; + valid_losses.push(avg_valid_loss); + + // Display the averaged training and validataion metrics every 10 epochs + if (epoch + 1) % 5 == 0 { + println!( + "Epoch {}/{}, Avg Loss {:.4}, Avg Val Loss: {:.4}", + epoch + 1, + config.num_epochs, + avg_train_loss, + avg_valid_loss, + ); + } + } + + // Save the trained model + model + .save_file(format!("{artifact_dir}/model"), &CompactRecorder::new()) + .expect("Trained model should be saved successfully"); +} From 31477f2c5245e5f19c4b633f92560c4a24287c5a Mon Sep 17 00:00:00 2001 From: wangjiawen2013 Date: Tue, 28 Jan 2025 12:45:23 +0800 Subject: [PATCH 02/14] format --- examples/modern-lstm/src/cli.rs | 10 +-- examples/modern-lstm/src/dataset.rs | 17 +++-- examples/modern-lstm/src/inference.rs | 15 ++-- examples/modern-lstm/src/lib.rs | 4 +- examples/modern-lstm/src/main.rs | 100 ++++++++++++++++++-------- examples/modern-lstm/src/model.rs | 99 +++++++++++++++---------- examples/modern-lstm/src/training.rs | 10 +-- 7 files changed, 161 insertions(+), 94 deletions(-) diff --git a/examples/modern-lstm/src/cli.rs b/examples/modern-lstm/src/cli.rs index 8d3f4a9582..0f008e14a2 100644 --- a/examples/modern-lstm/src/cli.rs +++ b/examples/modern-lstm/src/cli.rs @@ -17,19 +17,19 @@ pub enum Commands { artifact_dir: String, /// Number of epochs of training - #[arg(long, default_value="200")] + #[arg(long, default_value = "200")] num_epochs: usize, /// Size of the batches - #[arg(long, default_value="64")] + #[arg(long, default_value = "64")] batch_size: usize, /// Number of cpu threads to use during batch generation - #[arg(long, default_value="8")] + #[arg(long, default_value = "8")] num_workers: usize, /// Learning rate - #[arg(long, default_value="0.00005")] + #[arg(long, default_value = "0.00005")] lr: f64, }, @@ -38,5 +38,5 @@ pub enum Commands { /// Path to the trained model #[arg(long)] artifact_dir: String, - } + }, } \ No newline at end of file diff --git a/examples/modern-lstm/src/dataset.rs b/examples/modern-lstm/src/dataset.rs index 7845159b1d..d9c61b7e5d 100644 --- a/examples/modern-lstm/src/dataset.rs +++ b/examples/modern-lstm/src/dataset.rs @@ -6,7 +6,7 @@ use burn::{ prelude::*, }; use rand::Rng; -use rand_distr::{Normal, Distribution}; +use rand_distr::{Distribution, Normal}; use serde::{Deserialize, Serialize}; // Dataset parameters @@ -31,14 +31,15 @@ impl SequenceDatasetItem { for _i in 0..seq_length { // Next number is sum of previous two plus noise let normal = Normal::new(0.0, noise_level).unwrap(); - let next_val = seq[seq.len()-2] + seq[seq.len()-1] + normal.sample(&mut rand::thread_rng()); + let next_val = + seq[seq.len()-2] + seq[seq.len()-1] + normal.sample(&mut rand::thread_rng()); seq.push(next_val); } Self { // Convert to sequence and target - sequence: seq[0..seq.len()-1].to_vec(), // All but last - target: seq[seq.len()-1], // Last value + sequence: seq[0..seq.len() - 1].to_vec(), // All but last + target: seq[seq.len() - 1], // Last value } } } @@ -78,14 +79,12 @@ pub struct SequenceBatcher { #[derive(Clone, Debug)] pub struct SequenceBatch { pub sequences: Tensor, // [batch_size, seq_length, input_size] - pub targets: Tensor, // [batch_size, 1] + pub targets: Tensor, // [batch_size, 1] } impl SequenceBatcher { pub fn new(device: B::Device) -> Self { - Self { - device, - } + Self { device } } } @@ -108,4 +107,4 @@ impl Batcher> for SequenceBatc SequenceBatch { sequences, targets } } -} \ No newline at end of file +} diff --git a/examples/modern-lstm/src/inference.rs b/examples/modern-lstm/src/inference.rs index 3cd23c75a0..cec68f2bd3 100644 --- a/examples/modern-lstm/src/inference.rs +++ b/examples/modern-lstm/src/inference.rs @@ -1,19 +1,17 @@ use crate::{ dataset::{ - SequenceBatcher, - SequenceDataset, - SequenceDatasetItem, - NUM_SEQUENCES, SEQ_LENGTH, NOISE_LEVEL, + SequenceBatcher, SequenceDataset, SequenceDatasetItem, NOISE_LEVEL, NUM_SEQUENCES, + SEQ_LENGTH, }, - training::TrainingConfig, model::LstmNetwork, + training::TrainingConfig, }; -use polars::prelude::*; use burn::{ - data::{dataset::Dataset, dataloader::batcher::Batcher}, + data::{dataloader::batcher::Batcher, dataset::Dataset}, prelude::*, record::{CompactRecorder, Recorder}, }; +use polars::prelude::*; pub fn infer(artifact_dir: &str, device: B::Device) { // Loading model @@ -41,6 +39,7 @@ pub fn infer(artifact_dir: &str, device: B::Device) { let results = df![ "predicted" => &predicted.to_vec::().unwrap(), "expected" => &expected.to_vec::().unwrap(), - ].unwrap(); + ] + .unwrap(); println!("{}", &results.head(Some(10))); } \ No newline at end of file diff --git a/examples/modern-lstm/src/lib.rs b/examples/modern-lstm/src/lib.rs index d355a91bc2..cf2fcd7afa 100644 --- a/examples/modern-lstm/src/lib.rs +++ b/examples/modern-lstm/src/lib.rs @@ -1,5 +1,5 @@ +pub mod cli; pub mod dataset; +pub mod inference; pub mod model; pub mod training; -pub mod inference; -pub mod cli; diff --git a/examples/modern-lstm/src/main.rs b/examples/modern-lstm/src/main.rs index a803578c32..9f8e9c048c 100644 --- a/examples/modern-lstm/src/main.rs +++ b/examples/modern-lstm/src/main.rs @@ -6,7 +6,10 @@ ))] mod ndarray { use burn::{ - backend::{ndarray::{NdArray, NdArrayDevice}, Autodiff}, + backend::{ + ndarray::{NdArray, NdArrayDevice}, + Autodiff, + }, grad_clipping::GradientClippingConfig, optim::AdamConfig, }; @@ -14,14 +17,20 @@ mod ndarray { cli::{Cli, Commands}, inference::infer, model::LstmNetworkConfig, - training::{TrainingConfig, train}, + training::{train, TrainingConfig}, }; pub fn run(cli: Cli) { let device = NdArrayDevice::Cpu; match cli.command { - Commands::Train {artifact_dir, num_epochs, batch_size, num_workers, lr} => { + Commands::Train { + artifact_dir, + num_epochs, + batch_size, + num_workers, + lr, + } => { let config = TrainingConfig::new( LstmNetworkConfig::new(), // Gradient clipping via optimizer config @@ -29,12 +38,13 @@ mod ndarray { num_epochs, batch_size, num_workers, - lr); + lr, + ); train::>(&artifact_dir, config, device); - }, - Commands::Infer {artifact_dir} => { + } + Commands::Infer { artifact_dir } => { infer::(&artifact_dir, device); - }, + } } } } @@ -42,22 +52,31 @@ mod ndarray { #[cfg(feature = "tch-cpu")] mod tch_cpu { use burn::{ - backend::{libtorch::{LibTorch, LibTorchDevice}, Autodiff}, + backend::{ + libtorch::{LibTorch, LibTorchDevice}, + Autodiff, + }, grad_clipping::GradientClippingConfig, - optim::AdamConfig + optim::AdamConfig, }; use lstm::{ cli::{Cli, Commands}, inference::infer, model::LstmNetworkConfig, - training::{TrainingConfig, train}, + training::{train, TrainingConfig}, }; pub fn run(cli: Cli) { let device = LibTorchDevice::Cpu; match cli.command { - Commands::Train {artifact_dir, num_epochs, batch_size, num_workers, lr} => { + Commands::Train { + artifact_dir, + num_epochs, + batch_size, + num_workers, + lr, + } => { let config = TrainingConfig::new( LstmNetworkConfig::new(), // Gradient clipping via optimizer config @@ -65,12 +84,13 @@ mod tch_cpu { num_epochs, batch_size, num_workers, - lr); + lr, + ); train::>(&artifact_dir, config, device); - }, - Commands::Infer {artifact_dir} => { + } + Commands::Infer { artifact_dir } => { infer::(&artifact_dir, device); - }, + } } } } @@ -78,15 +98,18 @@ mod tch_cpu { #[cfg(feature = "tch-gpu")] mod tch_gpu { use burn::{ - backend::{libtorch::{LibTorch, LibTorchDevice}, Autodiff}, + backend::{ + libtorch::{LibTorch, LibTorchDevice}, + Autodiff, + }, grad_clipping::GradientClippingConfig, - optim::AdamConfig + optim::AdamConfig, }; use lstm::{ cli::{Cli, Commands}, inference::infer, model::LstmNetworkConfig, - training::{TrainingConfig, train}, + training::{train, TrainingConfig}, }; pub fn run(cli: Cli) { @@ -96,7 +119,13 @@ mod tch_gpu { let device = LibTorchDevice::Mps; match cli.command { - Commands::Train {artifact_dir, num_epochs, batch_size, num_workers, lr} => { + Commands::Train { + artifact_dir, + num_epochs, + batch_size, + num_workers, + lr, + } => { let config = TrainingConfig::new( LstmNetworkConfig::new(), // Gradient clipping via optimizer config @@ -104,12 +133,13 @@ mod tch_gpu { num_epochs, batch_size, num_workers, - lr); + lr, + ); train::>(&artifact_dir, config, device); }, - Commands::Infer {artifact_dir} => { + Commands::Infer { artifact_dir } => { infer::(&artifact_dir, device); - }, + } } } } @@ -117,22 +147,31 @@ mod tch_gpu { #[cfg(feature = "wgpu")] mod wgpu { use burn::{ - backend::{wgpu::{Wgpu, WgpuDevice}, Autodiff}, + backend::{ + wgpu::{Wgpu, WgpuDevice}, + Autodiff, + }, grad_clipping::GradientClippingConfig, - optim::AdamConfig + optim::AdamConfig, }; use lstm::{ cli::{Cli, Commands}, inference::infer, model::LstmNetworkConfig, - training::{TrainingConfig, train}, + training::{train, TrainingConfig}, }; pub fn run(cli: Cli) { let device = WgpuDevice::default(); match cli.command { - Commands::Train {artifact_dir, num_epochs, batch_size, num_workers, lr} => { + Commands::Train { + artifact_dir, + num_epochs, + batch_size, + num_workers, + lr, + } => { let config = TrainingConfig::new( LstmNetworkConfig::new(), // Gradient clipping via optimizer config @@ -140,12 +179,13 @@ mod wgpu { num_epochs, batch_size, num_workers, - lr); + lr, + ); train::>(&artifact_dir, config, device); - }, - Commands::Infer {artifact_dir} => { + } + Commands::Infer { artifact_dir } => { infer::(&artifact_dir, device); - }, + } } } } diff --git a/examples/modern-lstm/src/model.rs b/examples/modern-lstm/src/model.rs index f580554b18..7617c01a12 100644 --- a/examples/modern-lstm/src/model.rs +++ b/examples/modern-lstm/src/model.rs @@ -1,5 +1,8 @@ use burn::{ - nn::{LayerNorm, LayerNormConfig, Dropout, DropoutConfig, Initializer, Linear, LinearConfig, LstmState, Sigmoid, Tanh}, + nn::{ + Dropout, DropoutConfig, Initializer, LayerNorm, LayerNormConfig, Linear, LinearConfig, + LstmState, Sigmoid, Tanh, + }, prelude::*, }; @@ -50,20 +53,30 @@ impl LstmCellConfig { // 1. Orthogonal initialization for better gradient flow (here we use Xavier because of the lack of Orthogonal in burn) // 2. Initialize forget gate bias to 1.0 to prevent forgetting at start of training pub fn init(&self, device: &B::Device) -> LstmCell { - let initializer = Initializer::XavierNormal{gain:1.0}; + let initializer = Initializer::XavierNormal { gain: 1.0 }; let init_bias = Tensor::::ones([self.hidden_size], &device); let mut weight_ih = LinearConfig::new(self.input_size, 4 * self.hidden_size) .with_initializer(initializer.clone()) .init(device); // Set forget gate bias to 1.0 (helps with learning long sequences) - let bias = weight_ih.bias.clone().unwrap().val().slice_assign([self.hidden_size..2*self.hidden_size], init_bias.clone()); + let bias = weight_ih + .bias + .clone() + .unwrap() + .val() + .slice_assign([self.hidden_size..2*self.hidden_size], init_bias.clone()); weight_ih.bias = weight_ih.bias.map(|p| p.map(|_t| bias)); let mut weight_hh = LinearConfig::new(self.hidden_size, 4 * self.hidden_size) .with_initializer(initializer) .init(device); - let bias = weight_hh.bias.clone().unwrap().val().slice_assign([self.hidden_size..2*self.hidden_size], init_bias); + let bias = weight_hh + .bias + .clone() + .unwrap() + .val() + .slice_assign([self.hidden_size..2*self.hidden_size], init_bias); weight_hh.bias = weight_hh.bias.map(|p| p.map(|_t| bias)); LstmCell { @@ -85,17 +98,13 @@ impl LstmCell { // state: Tuple of (h_{t-1}, c_{t-1}) each of shape (batch_size, hidden_size) // Returns: // Tuple of (h_t, c_t) representing new hidden and cell states - pub fn forward( - &self, - x: Tensor, - state: LstmState, - ) -> LstmState { + pub fn forward(&self, x: Tensor, state: LstmState) -> LstmState { let (h_prev, c_prev) = (state.hidden, state.cell); // Combined matrix multiplication for all gates // Shape: (batch_size, 4 * hidden_size) - let gates_x = self.weight_ih.forward(x); // Transform input - let gates_h = self.weight_hh.forward(h_prev); // Transform previous hidden state + let gates_x = self.weight_ih.forward(x); // Transform input + let gates_h = self.weight_hh.forward(h_prev); // Transform previous hidden state // Apply layer normalization let gates_x = self.norm_x.forward(gates_x); @@ -130,11 +139,7 @@ impl LstmCell { } // Initialize cell state and hidden state if provided or with zeros - pub fn init_state( - &self, - batch_size: usize, - device: &B::Device, - ) -> LstmState { + pub fn init_state(&self, batch_size: usize, device: &B::Device) -> LstmState { let cell = Tensor::zeros([batch_size, self.hidden_size], device); let hidden = Tensor::zeros([batch_size, self.hidden_size], device); @@ -164,15 +169,25 @@ impl StackedLstmConfig { for i in 0..self.num_layers { if i == 0 { if i < self.num_layers - 1 { - layers.push(LstmCellConfig::new(self.input_size, self.hidden_size, self.dropout).init(device)); + layers.push( + LstmCellConfig::new(self.input_size, self.hidden_size, self.dropout) + .init(device), + ); } else { // No dropout on last layer - layers.push(LstmCellConfig::new(self.input_size, self.hidden_size, 0.0).init(device)); + layers.push( + LstmCellConfig::new(self.input_size, self.hidden_size, 0.0).init(device), + ); } } else { if i < self.num_layers -1 { - layers.push(LstmCellConfig::new(self.hidden_size, self.hidden_size, self.dropout).init(device)); + layers.push( + LstmCellConfig::new(self.hidden_size, self.hidden_size, self.dropout) + .init(device), + ); } else { // No dropout on last layer - layers.push(LstmCellConfig::new(self.hidden_size, self.hidden_size, 0.0).init(device)); + layers.push( + LstmCellConfig::new(self.hidden_size, self.hidden_size, 0.0).init(device), + ); } } } @@ -194,7 +209,7 @@ impl StackedLstm { &self, x: Tensor, states: Option>>, - ) -> (Tensor::, Vec>) { + ) -> (Tensor, Vec>) { let [batch_size, seq_length, _] = x.dims(); let device = x.device(); @@ -205,15 +220,19 @@ impl StackedLstm { temp.push(layer.init_state(batch_size, &device)); } temp - }, + } _ => states.unwrap(), }; let mut layer_outputs = vec![]; for t in 0..seq_length { - let mut input_t = x.clone().slice([None, Some((t as i64, t as i64 + 1)), None]).squeeze::<2>(1); + let mut input_t = x + .clone() + .slice([None, Some((t as i64, t as i64 + 1)), None]) + .squeeze::<2>(1); for (i, lstm_cell) in self.layers.iter().enumerate() { - let mut state: LstmState = LstmState::new(states[i].cell.clone(), states[i].hidden.clone()); + let mut state: LstmState = + LstmState::new(states[i].cell.clone(), states[i].hidden.clone()); state = lstm_cell.forward(input_t, state); input_t = state.hidden.clone(); states[i] = state; @@ -263,13 +282,23 @@ pub struct LstmNetworkConfig { impl LstmNetworkConfig { pub fn init(&self, device: &B::Device) -> LstmNetwork { // Forward direction LSTM - let stacked_lstm = StackedLstmConfig::new(self.input_size, self.hidden_size, self.num_layers, self.dropout) - .init(device); + let stacked_lstm = StackedLstmConfig::new( + self.input_size, + self.hidden_size, + self.num_layers, + self.dropout, + ) + .init(device); // Optional backward direction LSTM for bidirectional processing let (reverse_lstm, hidden_size) = if self.bidirectional { - let lstm = StackedLstmConfig::new(self.input_size, self.hidden_size, self.num_layers, self.dropout) - .init(device); + let lstm = StackedLstmConfig::new( + self.input_size, + self.hidden_size, + self.num_layers, + self.dropout, + ) + .init(device); (Some(lstm), 2*self.hidden_size) } else { (None, self.hidden_size) @@ -302,11 +331,7 @@ impl LstmNetwork { // // Returns: // Output tensor of shape (batch_size, output_size) - pub fn forward( - &self, - x: Tensor, - states: Option>>, - ) -> Tensor { + pub fn forward(&self, x: Tensor, states: Option>>) -> Tensor { let seq_length = x.dims()[1] as i64; // Forward direction let (mut output, _states) = self.stacked_lstm.forward(x.clone(), states); @@ -320,15 +345,17 @@ impl LstmNetwork { // Concatenate forward and backward outputs along the feature dimension output = Tensor::cat(vec![output, reverse_output], 2); output - }, - None => output + } + None => output, }; // Apply dropout before final layer output = self.dropout.forward(output); // Use final timestep output for prediction let final_output = self.fc.forward( - output.slice([None, Some((seq_length-1, seq_length)), None]).squeeze::<2>(1) + output + .slice([None, Some((seq_length-1, seq_length)), None]) + .squeeze::<2>(1), ); final_output diff --git a/examples/modern-lstm/src/training.rs b/examples/modern-lstm/src/training.rs index 5da43e4355..199b48d1a6 100644 --- a/examples/modern-lstm/src/training.rs +++ b/examples/modern-lstm/src/training.rs @@ -9,9 +9,7 @@ use burn::{ }; use crate::dataset::{ - SequenceBatcher, - SequenceDataset, - NUM_SEQUENCES, SEQ_LENGTH, NOISE_LEVEL, RANDOM_SEED, + SequenceBatcher, SequenceDataset, NOISE_LEVEL, NUM_SEQUENCES, RANDOM_SEED, SEQ_LENGTH, }; use crate::model::{LstmNetwork, LstmNetworkConfig}; @@ -61,7 +59,11 @@ pub fn train(artifact_dir: &str, config: TrainingConfig, dev .shuffle(RANDOM_SEED) .num_workers(config.num_workers) // 20% size of training - .build(SequenceDataset::new(NUM_SEQUENCES / 5, SEQ_LENGTH, NOISE_LEVEL)); + .build(SequenceDataset::new( + NUM_SEQUENCES / 5, + SEQ_LENGTH, + NOISE_LEVEL, + )); let train_num_items = dataloader_train.num_items(); let valid_num_items = dataloader_valid.num_items(); From 28721693630eea194b2db9712de1f113090df3de Mon Sep 17 00:00:00 2001 From: wangjiawen2013 Date: Tue, 28 Jan 2025 15:22:19 +0800 Subject: [PATCH 03/14] formatting --- examples/modern-lstm/src/dataset.rs | 2 +- examples/modern-lstm/src/main.rs | 2 +- examples/modern-lstm/src/model.rs | 28 +++++++++++++++------------- examples/modern-lstm/src/training.rs | 4 ++-- 4 files changed, 19 insertions(+), 17 deletions(-) diff --git a/examples/modern-lstm/src/dataset.rs b/examples/modern-lstm/src/dataset.rs index d9c61b7e5d..5820e3a73b 100644 --- a/examples/modern-lstm/src/dataset.rs +++ b/examples/modern-lstm/src/dataset.rs @@ -32,7 +32,7 @@ impl SequenceDatasetItem { // Next number is sum of previous two plus noise let normal = Normal::new(0.0, noise_level).unwrap(); let next_val = - seq[seq.len()-2] + seq[seq.len()-1] + normal.sample(&mut rand::thread_rng()); + seq[seq.len() - 2] + seq[seq.len() - 1] + normal.sample(&mut rand::thread_rng()); seq.push(next_val); } diff --git a/examples/modern-lstm/src/main.rs b/examples/modern-lstm/src/main.rs index 9f8e9c048c..f37afbb321 100644 --- a/examples/modern-lstm/src/main.rs +++ b/examples/modern-lstm/src/main.rs @@ -136,7 +136,7 @@ mod tch_gpu { lr, ); train::>(&artifact_dir, config, device); - }, + } Commands::Infer { artifact_dir } => { infer::(&artifact_dir, device); } diff --git a/examples/modern-lstm/src/model.rs b/examples/modern-lstm/src/model.rs index 7617c01a12..3afbf797da 100644 --- a/examples/modern-lstm/src/model.rs +++ b/examples/modern-lstm/src/model.rs @@ -30,7 +30,7 @@ pub struct LstmCell { // weight_hh layer uses combined weights for [i_t, f_t, g_t, o_t] for hidden state h_{t-1} pub weight_ih: Linear, pub weight_hh: Linear, - // Layer Normalization for better training stability. Don't use BatchNorm because the input distribution is always changing for LSTM. + // Layer Normalization for better training stability. Don't use BatchNorm because the input distribution is always changing for LSTM. pub norm_x: LayerNorm, // Normalize gate pre-activations pub norm_h: LayerNorm, // Normalize hidden state pub norm_c: LayerNorm, // Normalize cell state @@ -55,7 +55,7 @@ impl LstmCellConfig { pub fn init(&self, device: &B::Device) -> LstmCell { let initializer = Initializer::XavierNormal { gain: 1.0 }; let init_bias = Tensor::::ones([self.hidden_size], &device); - + let mut weight_ih = LinearConfig::new(self.input_size, 4 * self.hidden_size) .with_initializer(initializer.clone()) .init(device); @@ -65,7 +65,7 @@ impl LstmCellConfig { .clone() .unwrap() .val() - .slice_assign([self.hidden_size..2*self.hidden_size], init_bias.clone()); + .slice_assign([self.hidden_size..2 * self.hidden_size], init_bias.clone()); weight_ih.bias = weight_ih.bias.map(|p| p.map(|_t| bias)); let mut weight_hh = LinearConfig::new(self.hidden_size, 4 * self.hidden_size) @@ -76,7 +76,7 @@ impl LstmCellConfig { .clone() .unwrap() .val() - .slice_assign([self.hidden_size..2*self.hidden_size], init_bias); + .slice_assign([self.hidden_size..2 * self.hidden_size], init_bias); weight_hh.bias = weight_hh.bias.map(|p| p.map(|_t| bias)); LstmCell { @@ -173,18 +173,20 @@ impl StackedLstmConfig { LstmCellConfig::new(self.input_size, self.hidden_size, self.dropout) .init(device), ); - } else { // No dropout on last layer + } else { + // No dropout on last layer layers.push( LstmCellConfig::new(self.input_size, self.hidden_size, 0.0).init(device), ); } } else { - if i < self.num_layers -1 { + if i < self.num_layers - 1 { layers.push( LstmCellConfig::new(self.hidden_size, self.hidden_size, self.dropout) .init(device), ); - } else { // No dropout on last layer + } else { + // No dropout on last layer layers.push( LstmCellConfig::new(self.hidden_size, self.hidden_size, 0.0).init(device), ); @@ -239,7 +241,7 @@ impl StackedLstm { } layer_outputs.push(input_t); } - + // Stack output along sequence dimension let output = Tensor::stack(layer_outputs, 1); @@ -299,11 +301,11 @@ impl LstmNetworkConfig { self.dropout, ) .init(device); - (Some(lstm), 2*self.hidden_size) + (Some(lstm), 2 * self.hidden_size) } else { (None, self.hidden_size) }; - + let fc = LinearConfig::new(hidden_size, self.output_size).init(device); let dropout = DropoutConfig::new(self.dropout).init(); @@ -335,7 +337,7 @@ impl LstmNetwork { let seq_length = x.dims()[1] as i64; // Forward direction let (mut output, _states) = self.stacked_lstm.forward(x.clone(), states); - + output = match &self.reverse_lstm { Some(reverse_lstm) => { //Process sequence in reverse direction @@ -354,10 +356,10 @@ impl LstmNetwork { // Use final timestep output for prediction let final_output = self.fc.forward( output - .slice([None, Some((seq_length-1, seq_length)), None]) + .slice([None, Some((seq_length - 1, seq_length)), None]) .squeeze::<2>(1), ); final_output } -} \ No newline at end of file +} diff --git a/examples/modern-lstm/src/training.rs b/examples/modern-lstm/src/training.rs index 199b48d1a6..beb2e875b6 100644 --- a/examples/modern-lstm/src/training.rs +++ b/examples/modern-lstm/src/training.rs @@ -53,7 +53,7 @@ pub fn train(artifact_dir: &str, config: TrainingConfig, dev .shuffle(RANDOM_SEED) .num_workers(config.num_workers) .build(SequenceDataset::new(NUM_SEQUENCES, SEQ_LENGTH, NOISE_LEVEL)); - + let dataloader_valid = DataLoaderBuilder::new(batcher_valid) .batch_size(config.batch_size) .shuffle(RANDOM_SEED) @@ -82,7 +82,7 @@ pub fn train(artifact_dir: &str, config: TrainingConfig, dev let output = model.forward(batch.sequences, None); let loss = MseLoss::new().forward(output, batch.targets.clone(), Mean); train_loss += loss.clone().into_scalar().elem::() * batch.targets.dims()[0] as f32; - + // Gradients for the current backward pass let grads = loss.backward(); // Gradients linked to each parameter of the model From a32e01f89006d8485fbbd3380258141f85aea47b Mon Sep 17 00:00:00 2001 From: wangjiawen2013 Date: Tue, 28 Jan 2025 15:28:28 +0800 Subject: [PATCH 04/14] formatting --- examples/modern-lstm/src/cli.rs | 2 +- examples/modern-lstm/src/dataset.rs | 4 ++-- examples/modern-lstm/src/inference.rs | 2 +- examples/modern-lstm/src/model.rs | 2 +- examples/modern-lstm/src/training.rs | 3 +-- 5 files changed, 6 insertions(+), 7 deletions(-) diff --git a/examples/modern-lstm/src/cli.rs b/examples/modern-lstm/src/cli.rs index 0f008e14a2..4e815ee7c0 100644 --- a/examples/modern-lstm/src/cli.rs +++ b/examples/modern-lstm/src/cli.rs @@ -39,4 +39,4 @@ pub enum Commands { #[arg(long)] artifact_dir: String, }, -} \ No newline at end of file +} diff --git a/examples/modern-lstm/src/dataset.rs b/examples/modern-lstm/src/dataset.rs index 5820e3a73b..b2d04d525f 100644 --- a/examples/modern-lstm/src/dataset.rs +++ b/examples/modern-lstm/src/dataset.rs @@ -8,7 +8,7 @@ use burn::{ use rand::Rng; use rand_distr::{Distribution, Normal}; use serde::{Deserialize, Serialize}; - + // Dataset parameters pub const NUM_SEQUENCES: usize = 1000; pub const SEQ_LENGTH: usize = 10; @@ -26,7 +26,7 @@ impl SequenceDatasetItem { pub fn new(seq_length: usize, noise_level: f32) -> Self { // Start with two random numbers between 0 and 1 let mut seq = vec![rand::thread_rng().gen(), rand::thread_rng().gen()]; - + // Generate sequence for _i in 0..seq_length { // Next number is sum of previous two plus noise diff --git a/examples/modern-lstm/src/inference.rs b/examples/modern-lstm/src/inference.rs index cec68f2bd3..bad0af2996 100644 --- a/examples/modern-lstm/src/inference.rs +++ b/examples/modern-lstm/src/inference.rs @@ -42,4 +42,4 @@ pub fn infer(artifact_dir: &str, device: B::Device) { ] .unwrap(); println!("{}", &results.head(Some(10))); -} \ No newline at end of file +} diff --git a/examples/modern-lstm/src/model.rs b/examples/modern-lstm/src/model.rs index 3afbf797da..9a659e7693 100644 --- a/examples/modern-lstm/src/model.rs +++ b/examples/modern-lstm/src/model.rs @@ -142,7 +142,7 @@ impl LstmCell { pub fn init_state(&self, batch_size: usize, device: &B::Device) -> LstmState { let cell = Tensor::zeros([batch_size, self.hidden_size], device); let hidden = Tensor::zeros([batch_size, self.hidden_size], device); - + LstmState::new(cell, hidden) } } diff --git a/examples/modern-lstm/src/training.rs b/examples/modern-lstm/src/training.rs index beb2e875b6..583c05ab0d 100644 --- a/examples/modern-lstm/src/training.rs +++ b/examples/modern-lstm/src/training.rs @@ -7,9 +7,8 @@ use burn::{ record::CompactRecorder, tensor::backend::AutodiffBackend, }; - use crate::dataset::{ - SequenceBatcher, SequenceDataset, NOISE_LEVEL, NUM_SEQUENCES, RANDOM_SEED, SEQ_LENGTH, + SequenceBatcher, SequenceDataset, NOISE_LEVEL, NUM_SEQUENCES, RANDOM_SEED, SEQ_LENGTH, }; use crate::model::{LstmNetwork, LstmNetworkConfig}; From 1cf29c190cada9896604c9340abacc32577ebdbe Mon Sep 17 00:00:00 2001 From: wangjiawen2013 Date: Tue, 28 Jan 2025 15:44:12 +0800 Subject: [PATCH 05/14] formatting --- examples/modern-lstm/src/training.rs | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/examples/modern-lstm/src/training.rs b/examples/modern-lstm/src/training.rs index 583c05ab0d..2da5f4c205 100644 --- a/examples/modern-lstm/src/training.rs +++ b/examples/modern-lstm/src/training.rs @@ -1,3 +1,7 @@ +use crate::dataset::{ + SequenceBatcher, SequenceDataset, NOISE_LEVEL, NUM_SEQUENCES, RANDOM_SEED, SEQ_LENGTH, +}; +use crate::model::{LstmNetwork, LstmNetworkConfig}; use burn::{ data::dataloader::DataLoaderBuilder, module::AutodiffModule, @@ -7,10 +11,6 @@ use burn::{ record::CompactRecorder, tensor::backend::AutodiffBackend, }; -use crate::dataset::{ - SequenceBatcher, SequenceDataset, NOISE_LEVEL, NUM_SEQUENCES, RANDOM_SEED, SEQ_LENGTH, -}; -use crate::model::{LstmNetwork, LstmNetworkConfig}; #[derive(Config)] pub struct TrainingConfig { From 1717f71d885b5719fb451754a892105f5f7aaf19 Mon Sep 17 00:00:00 2001 From: wangjiawen2013 Date: Tue, 28 Jan 2025 15:55:56 +0800 Subject: [PATCH 06/14] formatting --- examples/modern-lstm/src/model.rs | 22 ++++++++++------------ 1 file changed, 10 insertions(+), 12 deletions(-) diff --git a/examples/modern-lstm/src/model.rs b/examples/modern-lstm/src/model.rs index 9a659e7693..fc06b341b1 100644 --- a/examples/modern-lstm/src/model.rs +++ b/examples/modern-lstm/src/model.rs @@ -54,7 +54,7 @@ impl LstmCellConfig { // 2. Initialize forget gate bias to 1.0 to prevent forgetting at start of training pub fn init(&self, device: &B::Device) -> LstmCell { let initializer = Initializer::XavierNormal { gain: 1.0 }; - let init_bias = Tensor::::ones([self.hidden_size], &device); + let init_bias = Tensor::::ones([self.hidden_size], device); let mut weight_ih = LinearConfig::new(self.input_size, 4 * self.hidden_size) .with_initializer(initializer.clone()) @@ -179,18 +179,16 @@ impl StackedLstmConfig { LstmCellConfig::new(self.input_size, self.hidden_size, 0.0).init(device), ); } + } else if i < self.num_layers - 1 { + layers.push( + LstmCellConfig::new(self.hidden_size, self.hidden_size, self.dropout) + .init(device), + ); } else { - if i < self.num_layers - 1 { - layers.push( - LstmCellConfig::new(self.hidden_size, self.hidden_size, self.dropout) - .init(device), - ); - } else { - // No dropout on last layer - layers.push( - LstmCellConfig::new(self.hidden_size, self.hidden_size, 0.0).init(device), - ); - } + // No dropout on last layer + layers.push( + LstmCellConfig::new(self.hidden_size, self.hidden_size, 0.0).init(device), + ); } } StackedLstm { layers } From dbf7639595dc48a8e5da9d2b59a8329a7a868ab0 Mon Sep 17 00:00:00 2001 From: jiawen wang Date: Tue, 28 Jan 2025 16:31:04 +0800 Subject: [PATCH 07/14] fix a typo --- examples/modern-lstm/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/modern-lstm/README.md b/examples/modern-lstm/README.md index 5a41f078b1..b71a14a491 100644 --- a/examples/modern-lstm/README.md +++ b/examples/modern-lstm/README.md @@ -4,8 +4,8 @@ A sophisticated implementation of Long Short-Term Memory (LSTM) networks in Burn `LstmNetwork` is the top-level module with bidirectional support and output projection. It can support multiple LSTM variants by setting appropriate `bidirectional` and `num_layers`： * LSTM: `num_layers = 1` and `bidirectional = false` * Stacked LSTM: `num_layers > 1` and `bidirectional = false` -* Birectional LSTM: `num_layers = 1` and `bidirectional = true` -* Birectional Stacked LSTM: `num_layers > 1` and `birectional = true` +* Bidirectional LSTM: `num_layers = 1` and `bidirectional = true` +* Bidirectional Stacked LSTM: `num_layers > 1` and `bidirectional = true` This implementation is complementary to Burn's official LSTM, users can choose either one depends on the project's specific needs. From 64f10aba9e357b72bf950ba194d2e4e1bae31759 Mon Sep 17 00:00:00 2001 From: jiawen wang Date: Thu, 30 Jan 2025 10:13:00 +0800 Subject: [PATCH 08/14] Update examples/modern-lstm/Cargo.toml Co-authored-by: Guillaume Lagrange --- examples/modern-lstm/Cargo.toml | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/examples/modern-lstm/Cargo.toml b/examples/modern-lstm/Cargo.toml index a37f651d09..4c7ecf2e89 100644 --- a/examples/modern-lstm/Cargo.toml +++ b/examples/modern-lstm/Cargo.toml @@ -13,17 +13,17 @@ tch-gpu = ["burn/tch"] wgpu = ["burn/wgpu"] [dependencies] -burn = { version="0.16.0", features=["train"] } +burn = { path = "../../crates/burn", features=["train"] } # Random number generator -rand = { version="0.8.5" } -rand_distr = { version="0.4.3" } +rand = { workspace = true } +rand_distr = { workspace = true } # Serialization -serde = { version="1.0.210", features=["std", "derive"] } +serde = {workspace = true, features = ["std", "derive"]} # Organise the results in dataframe -polars = { version="0.44.1" } +polars = { workspace = true } # Command line parser -clap = { version="4.5.21", features=["derive"] } \ No newline at end of file +clap = { workspace = true } \ No newline at end of file From 2690895bdb44252b34573871198615ff5ff4eeea Mon Sep 17 00:00:00 2001 From: wangjiawen2013 Date: Fri, 31 Jan 2025 00:22:31 +0800 Subject: [PATCH 09/14] use generic backend --- examples/modern-lstm/Cargo.toml | 6 +- examples/modern-lstm/README.md | 28 +++- examples/modern-lstm/examples/infer.rs | 95 +++++++++++ examples/modern-lstm/examples/train.rs | 106 ++++++++++++ examples/modern-lstm/output/config.json | 23 +++ examples/modern-lstm/output/model.mpk | Bin 0 -> 58001 bytes examples/modern-lstm/src/cli.rs | 42 ----- examples/modern-lstm/src/lib.rs | 1 - examples/modern-lstm/src/main.rs | 210 ------------------------ examples/modern-lstm/src/model.rs | 106 ++++++------ examples/modern-lstm/src/training.rs | 5 + 11 files changed, 308 insertions(+), 314 deletions(-) create mode 100644 examples/modern-lstm/examples/infer.rs create mode 100644 examples/modern-lstm/examples/train.rs create mode 100644 examples/modern-lstm/output/config.json create mode 100644 examples/modern-lstm/output/model.mpk delete mode 100644 examples/modern-lstm/src/cli.rs delete mode 100644 examples/modern-lstm/src/main.rs diff --git a/examples/modern-lstm/Cargo.toml b/examples/modern-lstm/Cargo.toml index 4c7ecf2e89..74f0e62015 100644 --- a/examples/modern-lstm/Cargo.toml +++ b/examples/modern-lstm/Cargo.toml @@ -1,5 +1,5 @@ [package] -name = "lstm" +name = "modern-lstm" version = "0.1.0" edition = "2021" @@ -11,6 +11,7 @@ ndarray-blas-openblas = ["burn/ndarray", "burn/openblas"] tch-cpu = ["burn/tch"] tch-gpu = ["burn/tch"] wgpu = ["burn/wgpu"] +cuda-jit = ["burn/cuda-jit"] [dependencies] burn = { path = "../../crates/burn", features=["train"] } @@ -24,6 +25,3 @@ serde = {workspace = true, features = ["std", "derive"]} # Organise the results in dataframe polars = { workspace = true } - -# Command line parser -clap = { workspace = true } \ No newline at end of file diff --git a/examples/modern-lstm/README.md b/examples/modern-lstm/README.md index b71a14a491..8a5f75ba95 100644 --- a/examples/modern-lstm/README.md +++ b/examples/modern-lstm/README.md @@ -9,14 +9,34 @@ A sophisticated implementation of Long Short-Term Memory (LSTM) networks in Burn This implementation is complementary to Burn's official LSTM, users can choose either one depends on the project's specific needs. -## Example Usage +## Usage + + +## Training -### Training ```sh -cargo run --release --features ndarray -- train --artifact-dir /home/wangjw/data/work/projects/lstm/output --num-epochs 30 --batch-size 32 --num-workers 2 --lr 0.001 +# Cuda backend +cargo run --example train --release --features cuda-jit + +# Wgpu backend +cargo run --example train --release --features wgpu + +# Tch GPU backend +export TORCH_CUDA_VERSION=cu121 # Set the cuda version +cargo run --example train --release --features tch-gpu + +# Tch CPU backend +cargo run --example train --release --features tch-cpu + +# NdArray backend (CPU) +cargo run --example train --release --features ndarray # f32 - single thread +cargo run --example train --release --features ndarray-blas-openblas # f32 - blas with openblas +cargo run --example train --release --features ndarray-blas-netlib # f32 - blas with netlib ``` + ### Inference + ```sh -cargo run --release --features ndarray -- infer --artifact-dir /home/wangjw/data/work/projects/lstm/output +cargo run --example infer --release --features cuda-jit ``` diff --git a/examples/modern-lstm/examples/infer.rs b/examples/modern-lstm/examples/infer.rs new file mode 100644 index 0000000000..50cd80b541 --- /dev/null +++ b/examples/modern-lstm/examples/infer.rs @@ -0,0 +1,95 @@ +use burn::tensor::backend::Backend; + +pub fn launch(device: B::Device) { + modern_lstm::inference::infer::("/tmp/modern-lstm", device); +} + +#[cfg(any( + feature = "ndarray", + feature = "ndarray-blas-netlib", + feature = "ndarray-blas-openblas", + feature = "ndarray-blas-accelerate", +))] +mod ndarray { + use burn::backend::{ + ndarray::{NdArray, NdArrayDevice}, + Autodiff, + }; + + use crate::launch; + + pub fn run() { + launch::>(NdArrayDevice::Cpu); + } +} + +#[cfg(feature = "tch-gpu")] +mod tch_gpu { + use burn::backend::{ + libtorch::{LibTorch, LibTorchDevice}, + Autodiff, + }; + + use crate::launch; + + pub fn run() { + #[cfg(not(target_os = "macos"))] + let device = LibTorchDevice::Cuda(0); + #[cfg(target_os = "macos")] + let device = LibTorchDevice::Mps; + + launch::>(device); + } +} + +#[cfg(feature = "tch-cpu")] +mod tch_cpu { + use burn::backend::{ + libtorch::{LibTorch, LibTorchDevice}, + Autodiff, + }; + + use crate::launch; + + pub fn run() { + launch::>(LibTorchDevice::Cpu); + } +} + +#[cfg(feature = "wgpu")] +mod wgpu { + use crate::launch; + use burn::backend::{wgpu::Wgpu, Autodiff}; + + pub fn run() { + launch::>(Default::default()); + } +} + +#[cfg(feature = "cuda-jit")] +mod cuda_jit { + use crate::launch; + use burn::backend::{Autodiff, CudaJit}; + + pub fn run() { + launch::>(Default::default()); + } +} + +fn main() { + #[cfg(any( + feature = "ndarray", + feature = "ndarray-blas-netlib", + feature = "ndarray-blas-openblas", + feature = "ndarray-blas-accelerate", + ))] + ndarray::run(); + #[cfg(feature = "tch-gpu")] + tch_gpu::run(); + #[cfg(feature = "tch-cpu")] + tch_cpu::run(); + #[cfg(feature = "wgpu")] + wgpu::run(); + #[cfg(feature = "cuda-jit")] + cuda_jit::run(); +} diff --git a/examples/modern-lstm/examples/train.rs b/examples/modern-lstm/examples/train.rs new file mode 100644 index 0000000000..a4288564b5 --- /dev/null +++ b/examples/modern-lstm/examples/train.rs @@ -0,0 +1,106 @@ +use burn::{ + grad_clipping::GradientClippingConfig, + optim::AdamConfig, + tensor::backend::AutodiffBackend +}; +use modern_lstm::{model::LstmNetworkConfig, training::TrainingConfig}; + +pub fn launch(device: B::Device) { + let config = TrainingConfig::new( + LstmNetworkConfig::new(), + // Gradient clipping via optimizer config + AdamConfig::new().with_grad_clipping(Some(GradientClippingConfig::Norm(1.0))) + ); + + modern_lstm::training::train::("/tmp/modern-lstm", config, device); +} + +#[cfg(any( + feature = "ndarray", + feature = "ndarray-blas-netlib", + feature = "ndarray-blas-openblas", + feature = "ndarray-blas-accelerate", +))] +mod ndarray { + use burn::backend::{ + ndarray::{NdArray, NdArrayDevice}, + Autodiff, + }; + + use crate::launch; + + pub fn run() { + launch::>(NdArrayDevice::Cpu); + } +} + +#[cfg(feature = "tch-gpu")] +mod tch_gpu { + use burn::backend::{ + libtorch::{LibTorch, LibTorchDevice}, + Autodiff, + }; + + use crate::launch; + + pub fn run() { + #[cfg(not(target_os = "macos"))] + let device = LibTorchDevice::Cuda(0); + #[cfg(target_os = "macos")] + let device = LibTorchDevice::Mps; + + launch::>(device); + } +} + +#[cfg(feature = "tch-cpu")] +mod tch_cpu { + use burn::backend::{ + libtorch::{LibTorch, LibTorchDevice}, + Autodiff, + }; + + use crate::launch; + + pub fn run() { + launch::>(LibTorchDevice::Cpu); + } +} + +#[cfg(feature = "wgpu")] +mod wgpu { + use crate::launch; + use burn::backend::{wgpu::Wgpu, Autodiff}; + + pub fn run() { + launch::>(Default::default()); + } +} + +#[cfg(feature = "cuda-jit")] +mod cuda_jit { + use crate::launch; + use burn::backend::{cuda_jit::CudaDevice, Autodiff, CudaJit}; + + pub fn run() { + launch::>(CudaDevice::default()); + } +} + +fn main() { + #[cfg(any( + feature = "ndarray", + feature = "ndarray-blas-netlib", + feature = "ndarray-blas-openblas", + feature = "ndarray-blas-accelerate", + ))] + ndarray::run(); + #[cfg(feature = "tch-gpu")] + tch_gpu::run(); + #[cfg(feature = "tch-cpu")] + tch_cpu::run(); + #[cfg(feature = "wgpu")] + wgpu::run(); + #[cfg(feature = "cuda-jit")] + cuda_jit::run(); +} diff --git a/examples/modern-lstm/output/config.json 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zg1|$|S^r9lFK{c6YHm856Brtp?XNwY7Px20w3G(cSe67_{#d`)|GFjb$Xtsu@LvPE zJ2}WX-?plxpdp4QP*5ZGBvwmLQu73d0 Chd=@V literal 0 HcmV?d00001 diff --git a/examples/modern-lstm/src/cli.rs b/examples/modern-lstm/src/cli.rs deleted file mode 100644 index 4e815ee7c0..0000000000 --- a/examples/modern-lstm/src/cli.rs +++ /dev/null @@ -1,42 +0,0 @@ -use clap::{Parser, Subcommand}; - -/// A CLI for long short-term memory network -#[derive(Parser, Debug)] -#[command(version, author, about, long_about=None)] -pub struct Cli { - #[command(subcommand)] - pub command: Commands, -} - -#[derive(Subcommand, Debug)] -pub enum Commands { - /// Train a model - Train { - /// Path to save trained model - #[arg(long)] - artifact_dir: String, - - /// Number of epochs of training - #[arg(long, default_value = "200")] - num_epochs: usize, - - /// Size of the batches - #[arg(long, default_value = "64")] - batch_size: usize, - - /// Number of cpu threads to use during batch generation - #[arg(long, default_value = "8")] - num_workers: usize, - - /// Learning rate - #[arg(long, default_value = "0.00005")] - lr: f64, - }, - - /// Inference - Infer { - /// Path to the trained model - #[arg(long)] - artifact_dir: String, - }, -} diff --git a/examples/modern-lstm/src/lib.rs b/examples/modern-lstm/src/lib.rs index cf2fcd7afa..1a167ffd75 100644 --- a/examples/modern-lstm/src/lib.rs +++ b/examples/modern-lstm/src/lib.rs @@ -1,4 +1,3 @@ -pub mod cli; pub mod dataset; pub mod inference; pub mod model; diff --git a/examples/modern-lstm/src/main.rs b/examples/modern-lstm/src/main.rs deleted file mode 100644 index f37afbb321..0000000000 --- a/examples/modern-lstm/src/main.rs +++ /dev/null @@ -1,210 +0,0 @@ -#[cfg(any( - feature = "ndarray", - feature = "ndarray-blas-netlib", - feature = "ndarray-blas-openblas", - feature = "ndarray-blas-accelerate", -))] -mod ndarray { - use burn::{ - backend::{ - ndarray::{NdArray, NdArrayDevice}, - Autodiff, - }, - grad_clipping::GradientClippingConfig, - optim::AdamConfig, - }; - use lstm::{ - cli::{Cli, Commands}, - inference::infer, - model::LstmNetworkConfig, - training::{train, TrainingConfig}, - }; - - pub fn run(cli: Cli) { - let device = NdArrayDevice::Cpu; - - match cli.command { - Commands::Train { - artifact_dir, - num_epochs, - batch_size, - num_workers, - lr, - } => { - let config = TrainingConfig::new( - LstmNetworkConfig::new(), - // Gradient clipping via optimizer config - AdamConfig::new().with_grad_clipping(Some(GradientClippingConfig::Norm(1.0))), - num_epochs, - batch_size, - num_workers, - lr, - ); - train::>(&artifact_dir, config, device); - } - Commands::Infer { artifact_dir } => { - infer::(&artifact_dir, device); - } - } - } -} - -#[cfg(feature = "tch-cpu")] -mod tch_cpu { - use burn::{ - backend::{ - libtorch::{LibTorch, LibTorchDevice}, - Autodiff, - }, - grad_clipping::GradientClippingConfig, - optim::AdamConfig, - }; - use lstm::{ - cli::{Cli, Commands}, - inference::infer, - model::LstmNetworkConfig, - training::{train, TrainingConfig}, - }; - - pub fn run(cli: Cli) { - let device = LibTorchDevice::Cpu; - - match cli.command { - Commands::Train { - artifact_dir, - num_epochs, - batch_size, - num_workers, - lr, - } => { - let config = TrainingConfig::new( - LstmNetworkConfig::new(), - // Gradient clipping via optimizer config - AdamConfig::new().with_grad_clipping(Some(GradientClippingConfig::Norm(1.0))), - num_epochs, - batch_size, - num_workers, - lr, - ); - train::>(&artifact_dir, config, device); - } - Commands::Infer { artifact_dir } => { - infer::(&artifact_dir, device); - } - } - } -} - -#[cfg(feature = "tch-gpu")] -mod tch_gpu { - use burn::{ - backend::{ - libtorch::{LibTorch, LibTorchDevice}, - Autodiff, - }, - grad_clipping::GradientClippingConfig, - optim::AdamConfig, - }; - use lstm::{ - cli::{Cli, Commands}, - inference::infer, - model::LstmNetworkConfig, - training::{train, TrainingConfig}, - }; - - pub fn run(cli: Cli) { - #[cfg(not(target_os = "macos"))] - let device = LibTorchDevice::Cuda(0); - #[cfg(target_os = "macos")] - let device = LibTorchDevice::Mps; - - match cli.command { - Commands::Train { - artifact_dir, - num_epochs, - batch_size, - num_workers, - lr, - } => { - let config = TrainingConfig::new( - LstmNetworkConfig::new(), - // Gradient clipping via optimizer config - AdamConfig::new().with_grad_clipping(Some(GradientClippingConfig::Norm(1.0))), - num_epochs, - batch_size, - num_workers, - lr, - ); - train::>(&artifact_dir, config, device); - } - Commands::Infer { artifact_dir } => { - infer::(&artifact_dir, device); - } - } - } -} - -#[cfg(feature = "wgpu")] -mod wgpu { - use burn::{ - backend::{ - wgpu::{Wgpu, WgpuDevice}, - Autodiff, - }, - grad_clipping::GradientClippingConfig, - optim::AdamConfig, - }; - use lstm::{ - cli::{Cli, Commands}, - inference::infer, - model::LstmNetworkConfig, - training::{train, TrainingConfig}, - }; - - pub fn run(cli: Cli) { - let device = WgpuDevice::default(); - - match cli.command { - Commands::Train { - artifact_dir, - num_epochs, - batch_size, - num_workers, - lr, - } => { - let config = TrainingConfig::new( - LstmNetworkConfig::new(), - // Gradient clipping via optimizer config - AdamConfig::new().with_grad_clipping(Some(GradientClippingConfig::Norm(1.0))), - num_epochs, - batch_size, - num_workers, - lr, - ); - train::>(&artifact_dir, config, device); - } - Commands::Infer { artifact_dir } => { - infer::(&artifact_dir, device); - } - } - } -} - -use clap::Parser; -use lstm::cli::Cli; -fn main() { - let cli = Cli::parse(); - #[cfg(any( - feature = "ndarray", - feature = "ndarray-blas-netlib", - feature = "ndarray-blas-openblas", - feature = "ndarray-blas-accelerate", - ))] - ndarray::run(cli); - #[cfg(feature = "tch-cpu")] - tch_cpu::run(cli); - #[cfg(feature = "tch-gpu")] - tch_gpu::run(cli); - #[cfg(feature = "wgpu")] - wgpu::run(cli); -} diff --git a/examples/modern-lstm/src/model.rs b/examples/modern-lstm/src/model.rs index fc06b341b1..ebf6b3bc08 100644 --- a/examples/modern-lstm/src/model.rs +++ b/examples/modern-lstm/src/model.rs @@ -6,21 +6,21 @@ use burn::{ prelude::*, }; -// LSTM Cell implementation with layer normalization. -// -// Mathematical formulation of LSTM: -// f_t = σ(W_f · [h_{t-1}, x_t] + b_f) # Forget gate -// i_t = σ(W_i · [h_{t-1}, x_t] + b_i] # Input gate -// g_t = tanh(W_g · [h_{t-1}, x_t] + b_g] # Candidate cell state -// o_t = σ(W_o · [h_{t-1}, x_t] + b_o) # Output gate -// -// c_t = f_t ⊙ c_{t-1} + i_t ⊙ g_t # New cell state -// h_t = o_t ⊙ tanh(c_t) # New hidden state -// -// where: -// - σ is the sigmoid function -// - ⊙ is the element-wise multiplication -// - [h_{t-1}, x_t] represents concatenation +/// LSTM Cell implementation with layer normalization. +/// +/// Mathematical formulation of LSTM: +/// f_t = σ(W_f · [h_{t-1}, x_t] + b_f) # Forget gate +/// i_t = σ(W_i · [h_{t-1}, x_t] + b_i] # Input gate +/// g_t = tanh(W_g · [h_{t-1}, x_t] + b_g] # Candidate cell state +/// o_t = σ(W_o · [h_{t-1}, x_t] + b_o) # Output gate +/// +/// c_t = f_t ⊙ c_{t-1} + i_t ⊙ g_t # New cell state +/// h_t = o_t ⊙ tanh(c_t) # New hidden state +/// +/// where: +/// - σ is the sigmoid function +/// - ⊙ is the element-wise multiplication +/// - [h_{t-1}, x_t] represents concatenation #[derive(Module, Debug)] pub struct LstmCell { @@ -37,7 +37,7 @@ pub struct LstmCell { pub dropout: Dropout, } -// Configuration to create a Lstm module using the init function. +/// Configuration to create a Lstm module using the init function. #[derive(Config, Debug)] pub struct LstmCellConfig { // The size of the input features @@ -92,12 +92,12 @@ impl LstmCellConfig { } impl LstmCell { - // Forward pass of LSTM cell. - // Args: - // x: Input tensor of shape (batch_size, input_size) - // state: Tuple of (h_{t-1}, c_{t-1}) each of shape (batch_size, hidden_size) - // Returns: - // Tuple of (h_t, c_t) representing new hidden and cell states + /// Forward pass of LSTM cell. + /// Args: + /// x: Input tensor of shape (batch_size, input_size) + /// state: Tuple of (h_{t-1}, c_{t-1}) each of shape (batch_size, hidden_size) + /// Returns: + /// Tuple of (h_t, c_t) representing new hidden and cell states pub fn forward(&self, x: Tensor, state: LstmState) -> LstmState { let (h_prev, c_prev) = (state.hidden, state.cell); @@ -147,8 +147,8 @@ impl LstmCell { } } -// Stacked LSTM implementation supporting multiple layers -// Each layer processes the output of the previous layer +/// Stacked LSTM implementation supporting multiple layers +/// Each layer processes the output of the previous layer #[derive(Module, Debug)] pub struct StackedLstm { pub layers: Vec>, @@ -196,15 +196,15 @@ impl StackedLstmConfig { } impl StackedLstm { - // Process input sequence through stacked LSTM layers. - // - // Args: - // x: Input tensor of shape (batch_size, seq_length, input_size) - // states: Optional initial states for each layer - // - // Returns: - // Tuple of (output, states) where output has shape (batch_size, seq_length, hidden_size) - // and states is a vector of length num_layers, both cell and hidden state in each element have shape (batch_size, hidden_size) + /// Process input sequence through stacked LSTM layers. + /// + /// Args: + /// x: Input tensor of shape (batch_size, seq_length, input_size) + /// states: Optional initial states for each layer + /// + /// Returns: + /// Tuple of (output, states) where output has shape (batch_size, seq_length, hidden_size) + /// and states is a vector of length num_layers, both cell and hidden state in each element have shape (batch_size, hidden_size) pub fn forward( &self, x: Tensor, @@ -247,12 +247,12 @@ impl StackedLstm { } } -// Complete LSTM network with bidirectional support. -// -// In bidirectional mode: -// - Forward LSTM processes sequence from left to right -// - Backward LSTM processes sequence from right to left -// - Outputs are concatenated for final prediction +/// Complete LSTM network with bidirectional support. +/// +/// In bidirectional mode: +/// - Forward LSTM processes sequence from left to right +/// - Backward LSTM processes sequence from right to left +/// - Outputs are concatenated for final prediction #[derive(Module, Debug)] pub struct LstmNetwork { // Forward direction LSTM @@ -317,20 +317,20 @@ impl LstmNetworkConfig { } impl LstmNetwork { - // Forward pass of the network. - // - // For bidirectional processing: - // 1. Process sequence normally with forward LSTM - // 2. Process reversed sequence with backward LSTM - // 3. Concatenate both outputs - // 4. Apply final linear transformation - // - // Args: - // x: Input tensor of shape (batch_size, seq_length, input_size) - // states: Optional initial states - // - // Returns: - // Output tensor of shape (batch_size, output_size) + /// Forward pass of the network. + /// + /// For bidirectional processing: + /// 1. Process sequence normally with forward LSTM + /// 2. Process reversed sequence with backward LSTM + /// 3. Concatenate both outputs + /// 4. Apply final linear transformation + /// + /// Args: + /// x: Input tensor of shape (batch_size, seq_length, input_size) + /// states: Optional initial states + /// + /// Returns: + /// Output tensor of shape (batch_size, output_size) pub fn forward(&self, x: Tensor, states: Option>>) -> Tensor { let seq_length = x.dims()[1] as i64; // Forward direction diff --git a/examples/modern-lstm/src/training.rs b/examples/modern-lstm/src/training.rs index 2da5f4c205..a74babd509 100644 --- a/examples/modern-lstm/src/training.rs +++ b/examples/modern-lstm/src/training.rs @@ -16,9 +16,14 @@ use burn::{ pub struct TrainingConfig { pub model: LstmNetworkConfig, pub optimizer: AdamConfig, + + #[config(default = 30)] pub num_epochs: usize, + #[config(default = 32)] pub batch_size: usize, + #[config(default = 2)] pub num_workers: usize, + #[config(default = 1e-3)] pub lr: f64, } From b27b228840230176b0de1b1e002205c52132e711 Mon Sep 17 00:00:00 2001 From: wangjiawen2013 Date: Fri, 31 Jan 2025 10:35:05 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insertions(+), 39 deletions(-) delete mode 100644 examples/modern-lstm/output/config.json delete mode 100644 examples/modern-lstm/output/model.mpk diff --git a/examples/modern-lstm/README.md b/examples/modern-lstm/README.md index 8a5f75ba95..f9225e31c0 100644 --- a/examples/modern-lstm/README.md +++ b/examples/modern-lstm/README.md @@ -29,9 +29,9 @@ cargo run --example train --release --features tch-gpu cargo run --example train --release --features tch-cpu # NdArray backend (CPU) -cargo run --example train --release --features ndarray # f32 - single thread -cargo run --example train --release --features ndarray-blas-openblas # f32 - blas with openblas -cargo run --example train --release --features ndarray-blas-netlib # f32 - blas with netlib +cargo run --example train --release --features ndarray +cargo run --example train --release --features ndarray-blas-openblas +cargo run --example train --release --features ndarray-blas-netlib ``` diff --git a/examples/modern-lstm/examples/infer.rs b/examples/modern-lstm/examples/infer.rs index 50cd80b541..e7618775f9 100644 --- a/examples/modern-lstm/examples/infer.rs +++ b/examples/modern-lstm/examples/infer.rs @@ -12,22 +12,20 @@ pub fn launch(device: B::Device) { ))] mod ndarray { use burn::backend::{ - ndarray::{NdArray, NdArrayDevice}, - Autodiff, + ndarray::{NdArray, NdArrayDevice} }; use crate::launch; pub fn run() { - launch::>(NdArrayDevice::Cpu); + launch::(NdArrayDevice::Cpu); } } #[cfg(feature = "tch-gpu")] mod tch_gpu { use burn::backend::{ - libtorch::{LibTorch, LibTorchDevice}, - Autodiff, + libtorch::{LibTorch, LibTorchDevice} }; use crate::launch; @@ -38,41 +36,40 @@ mod tch_gpu { #[cfg(target_os = "macos")] let device = LibTorchDevice::Mps; - launch::>(device); + launch::(device); } } #[cfg(feature = "tch-cpu")] mod tch_cpu { use burn::backend::{ - libtorch::{LibTorch, LibTorchDevice}, - Autodiff, + libtorch::{LibTorch, LibTorchDevice} }; use crate::launch; pub fn run() { - launch::>(LibTorchDevice::Cpu); + launch::(LibTorchDevice::Cpu); } } #[cfg(feature = "wgpu")] mod wgpu { use crate::launch; - use burn::backend::{wgpu::Wgpu, Autodiff}; + use burn::backend::wgpu::Wgpu; pub fn run() { - launch::>(Default::default()); + launch::(Default::default()); } } #[cfg(feature = "cuda-jit")] mod cuda_jit { use crate::launch; - use burn::backend::{Autodiff, CudaJit}; + use burn::backend::CudaJit; pub fn run() { - launch::>(Default::default()); + launch::(Default::default()); } } diff --git a/examples/modern-lstm/output/config.json b/examples/modern-lstm/output/config.json deleted file mode 100644 index cebd85f7ce..0000000000 --- a/examples/modern-lstm/output/config.json +++ /dev/null @@ -1,23 +0,0 @@ -{ - "model": { - "input_size": 1, - "hidden_size": 32, - "num_layers": 2, - "output_size": 1, - "dropout": 0.1, - "bidirectional": true - }, - "optimizer": { - "weight_decay": null, - "grad_clipping": { - "Norm": 1.0 - }, - "beta_1": 0.9, - "beta_2": 0.999, - 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z|BW|UWG*evkDEVgWXU3`@V74?TDKcPToajfScvu8NV%(K%Q!MZia@ynE){P_mkig>DM*$XxN|AzGBEu?I5 z#c@R|%#``XdDxmfIZ2gRkXW3wH0f~C;G_qMx06y6+mduiGm@G|uNYkpKbw#^C$TVT zK(an*XOcFlItfj@J9<{4kZ4ajl6X7u-;Kb?_+_@`RB2Ie-s0axw6-Q+H&?`W#772} z1#Fgu=1={&)pNLKg3sUX|2%$7z!Jy|82mT=F@Z#Div7`O$FuxD*naNtk+n^T4q_g%ngCGK)|A~wE0(?`9PTE zq~&5D%d#|Z-#^YeC-A@=>|f^33v94HF`xGj4p7z-b6-oF<)VL^#ba(UCj{~Xjh1-J zg1|$|S^r9lFK{c6YHm856Brtp?XNwY7Px20w3G(cSe67_{#d`)|GFjb$Xtsu@LvPE zJ2}WX-?plxpdp4QP*5ZGBvwmLQu73d0 Chd=@V From 68a0f93219c6482a7ef853dfac8a0e936c0b1fc1 Mon Sep 17 00:00:00 2001 From: wangjiawen2013 Date: Fri, 31 Jan 2025 11:24:50 +0800 Subject: [PATCH 12/14] update readme --- examples/modern-lstm/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/modern-lstm/README.md b/examples/modern-lstm/README.md index f9225e31c0..d5a851baa1 100644 --- a/examples/modern-lstm/README.md +++ b/examples/modern-lstm/README.md @@ -1,5 +1,5 @@ # Advanced LSTM Implementation with Burn -A sophisticated implementation of Long Short-Term Memory (LSTM) networks in Burn, featuring state-of-the-art architectural enhancements and optimizations. This implementation includes bidirectional processing capabilities and advanced regularization techniques, making it suitable for both research and production environments. More details can be found at the [PyTorch implementation](https://github.com/shiv08/Advanced-LSTM-Implementation-with-PyTorch). +A sophisticated implementation of Long Short-Term Memory (LSTM) networks in Burn, featuring state-of-the-art architectural enhancements and optimizations. This implementation includes bidirectional processing capabilities and advanced regularization techniques. More details can be found at the [PyTorch implementation](https://github.com/shiv08/Advanced-LSTM-Implementation-with-PyTorch). `LstmNetwork` is the top-level module with bidirectional support and output projection. It can support multiple LSTM variants by setting appropriate `bidirectional` and `num_layers`： * LSTM: `num_layers = 1` and `bidirectional = false` From 9bdc8e90317539de7ef6296634c9969fec2d39cd Mon Sep 17 00:00:00 2001 From: Guillaume Lagrange Date: Mon, 3 Feb 2025 10:38:55 -0500 Subject: [PATCH 13/14] Update README + fix main changes --- Cargo.lock | 11 ++++++ examples/modern-lstm/Cargo.toml | 2 +- examples/modern-lstm/README.md | 38 ++++++++++--------- .../examples/{infer.rs => lstm-infer.rs} | 24 +++++------- .../examples/{train.rs => lstm-train.rs} | 18 ++++----- 5 files changed, 50 insertions(+), 43 deletions(-) rename examples/modern-lstm/examples/{infer.rs => lstm-infer.rs} (78%) rename examples/modern-lstm/examples/{train.rs => lstm-train.rs} (85%) diff --git a/Cargo.lock b/Cargo.lock index ab9eddb0bd..c9ce522699 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -3855,6 +3855,17 @@ dependencies = [ "burn-import", ] +[[package]] +name = "modern-lstm" +version = "0.1.0" +dependencies = [ + "burn", + "polars", + "rand", + "rand_distr", + "serde", +] + [[package]] name = "monostate" version = "0.1.13" diff --git a/examples/modern-lstm/Cargo.toml b/examples/modern-lstm/Cargo.toml index 74f0e62015..86855e9ad4 100644 --- a/examples/modern-lstm/Cargo.toml +++ b/examples/modern-lstm/Cargo.toml @@ -11,7 +11,7 @@ ndarray-blas-openblas = ["burn/ndarray", "burn/openblas"] tch-cpu = ["burn/tch"] tch-gpu = ["burn/tch"] wgpu = ["burn/wgpu"] -cuda-jit = ["burn/cuda-jit"] +cuda = ["burn/cuda"] [dependencies] burn = { path = "../../crates/burn", features=["train"] } diff --git a/examples/modern-lstm/README.md b/examples/modern-lstm/README.md index d5a851baa1..832851a1f0 100644 --- a/examples/modern-lstm/README.md +++ b/examples/modern-lstm/README.md @@ -1,42 +1,46 @@ # Advanced LSTM Implementation with Burn -A sophisticated implementation of Long Short-Term Memory (LSTM) networks in Burn, featuring state-of-the-art architectural enhancements and optimizations. This implementation includes bidirectional processing capabilities and advanced regularization techniques. More details can be found at the [PyTorch implementation](https://github.com/shiv08/Advanced-LSTM-Implementation-with-PyTorch). -`LstmNetwork` is the top-level module with bidirectional support and output projection. It can support multiple LSTM variants by setting appropriate `bidirectional` and `num_layers`： -* LSTM: `num_layers = 1` and `bidirectional = false` -* Stacked LSTM: `num_layers > 1` and `bidirectional = false` -* Bidirectional LSTM: `num_layers = 1` and `bidirectional = true` -* Bidirectional Stacked LSTM: `num_layers > 1` and `bidirectional = true` +A more advanced implementation of Long Short-Term Memory (LSTM) networks in Burn with combined +weight matrices for the input and hidden states, based on the +[PyTorch implementation](https://github.com/shiv08/Advanced-LSTM-Implementation-with-PyTorch). -This implementation is complementary to Burn's official LSTM, users can choose either one depends on the project's specific needs. +`LstmNetwork` is the top-level module with bidirectional and regularization support. The LSTM +variants differ by `bidirectional` and `num_layers` settings： -## Usage +- LSTM: `num_layers = 1` and `bidirectional = false` +- Stacked LSTM: `num_layers > 1` and `bidirectional = false` +- Bidirectional LSTM: `num_layers = 1` and `bidirectional = true` +- Bidirectional Stacked LSTM: `num_layers > 1` and `bidirectional = true` + +This implementation is complementary to Burn's official LSTM, users can choose either one depends on +the project's specific needs. +## Usage ## Training ```sh # Cuda backend -cargo run --example train --release --features cuda-jit +cargo run --example lstm-train --release --features cuda-jit # Wgpu backend -cargo run --example train --release --features wgpu +cargo run --example lstm-train --release --features wgpu # Tch GPU backend export TORCH_CUDA_VERSION=cu121 # Set the cuda version -cargo run --example train --release --features tch-gpu +cargo run --example lstm-train --release --features tch-gpu # Tch CPU backend -cargo run --example train --release --features tch-cpu +cargo run --example lstm-train --release --features tch-cpu # NdArray backend (CPU) -cargo run --example train --release --features ndarray -cargo run --example train --release --features ndarray-blas-openblas -cargo run --example train --release --features ndarray-blas-netlib +cargo run --example lstm-train --release --features ndarray +cargo run --example lstm-train --release --features ndarray-blas-openblas +cargo run --example lstm-train --release --features ndarray-blas-netlib ``` - ### Inference ```sh -cargo run --example infer --release --features cuda-jit +cargo run --example lstm-infer --release --features cuda-jit ``` diff --git a/examples/modern-lstm/examples/infer.rs b/examples/modern-lstm/examples/lstm-infer.rs similarity index 78% rename from examples/modern-lstm/examples/infer.rs rename to examples/modern-lstm/examples/lstm-infer.rs index e7618775f9..f601d08c79 100644 --- a/examples/modern-lstm/examples/infer.rs +++ b/examples/modern-lstm/examples/lstm-infer.rs @@ -11,9 +11,7 @@ pub fn launch(device: B::Device) { feature = "ndarray-blas-accelerate", ))] mod ndarray { - use burn::backend::{ - ndarray::{NdArray, NdArrayDevice} - }; + use burn::backend::ndarray::{NdArray, NdArrayDevice}; use crate::launch; @@ -24,9 +22,7 @@ mod ndarray { #[cfg(feature = "tch-gpu")] mod tch_gpu { - use burn::backend::{ - libtorch::{LibTorch, LibTorchDevice} - }; + use burn::backend::libtorch::{LibTorch, LibTorchDevice}; use crate::launch; @@ -42,9 +38,7 @@ mod tch_gpu { #[cfg(feature = "tch-cpu")] mod tch_cpu { - use burn::backend::{ - libtorch::{LibTorch, LibTorchDevice} - }; + use burn::backend::libtorch::{LibTorch, LibTorchDevice}; use crate::launch; @@ -63,13 +57,13 @@ mod wgpu { } } -#[cfg(feature = "cuda-jit")] -mod cuda_jit { +#[cfg(feature = "cuda")] +mod cuda { use crate::launch; - use burn::backend::CudaJit; + use burn::backend::Cuda; pub fn run() { - launch::(Default::default()); + launch::(Default::default()); } } @@ -87,6 +81,6 @@ fn main() { tch_cpu::run(); #[cfg(feature = "wgpu")] wgpu::run(); - #[cfg(feature = "cuda-jit")] - cuda_jit::run(); + #[cfg(feature = "cuda")] + cuda::run(); } diff --git a/examples/modern-lstm/examples/train.rs b/examples/modern-lstm/examples/lstm-train.rs similarity index 85% rename from examples/modern-lstm/examples/train.rs rename to examples/modern-lstm/examples/lstm-train.rs index a4288564b5..454263d331 100644 --- a/examples/modern-lstm/examples/train.rs +++ b/examples/modern-lstm/examples/lstm-train.rs @@ -1,7 +1,5 @@ use burn::{ - grad_clipping::GradientClippingConfig, - optim::AdamConfig, - tensor::backend::AutodiffBackend + grad_clipping::GradientClippingConfig, optim::AdamConfig, tensor::backend::AutodiffBackend, }; use modern_lstm::{model::LstmNetworkConfig, training::TrainingConfig}; @@ -9,7 +7,7 @@ pub fn launch(device: B::Device) { let config = TrainingConfig::new( LstmNetworkConfig::new(), // Gradient clipping via optimizer config - AdamConfig::new().with_grad_clipping(Some(GradientClippingConfig::Norm(1.0))) + AdamConfig::new().with_grad_clipping(Some(GradientClippingConfig::Norm(1.0))), ); modern_lstm::training::train::("/tmp/modern-lstm", config, device); @@ -77,13 +75,13 @@ mod wgpu { } } -#[cfg(feature = "cuda-jit")] -mod cuda_jit { +#[cfg(feature = "cuda")] +mod cuda { use crate::launch; - use burn::backend::{cuda_jit::CudaDevice, Autodiff, CudaJit}; + use burn::backend::{cuda::CudaDevice, Autodiff, Cuda}; pub fn run() { - launch::>(CudaDevice::default()); + launch::>(CudaDevice::default()); } } @@ -101,6 +99,6 @@ fn main() { tch_cpu::run(); #[cfg(feature = "wgpu")] wgpu::run(); - #[cfg(feature = "cuda-jit")] - cuda_jit::run(); + #[cfg(feature = "cuda")] + cuda::run(); } From af755645b20b2b2bac8fb0b634d4690e83afaffd Mon Sep 17 00:00:00 2001 From: Guillaume Lagrange Date: Mon, 3 Feb 2025 10:48:31 -0500 Subject: [PATCH 14/14] Fix clippy --- examples/modern-lstm/src/model.rs | 7 +++---- examples/modern-lstm/src/training.rs | 4 ++-- 2 files changed, 5 insertions(+), 6 deletions(-) diff --git a/examples/modern-lstm/src/model.rs b/examples/modern-lstm/src/model.rs index ebf6b3bc08..268de59a0b 100644 --- a/examples/modern-lstm/src/model.rs +++ b/examples/modern-lstm/src/model.rs @@ -52,6 +52,7 @@ impl LstmCellConfig { // Initialize parameters using best practices: // 1. Orthogonal initialization for better gradient flow (here we use Xavier because of the lack of Orthogonal in burn) // 2. Initialize forget gate bias to 1.0 to prevent forgetting at start of training + #[allow(clippy::single_range_in_vec_init)] pub fn init(&self, device: &B::Device) -> LstmCell { let initializer = Initializer::XavierNormal { gain: 1.0 }; let init_bias = Tensor::::ones([self.hidden_size], device); @@ -352,12 +353,10 @@ impl LstmNetwork { // Apply dropout before final layer output = self.dropout.forward(output); // Use final timestep output for prediction - let final_output = self.fc.forward( + self.fc.forward( output .slice([None, Some((seq_length - 1, seq_length)), None]) .squeeze::<2>(1), - ); - - final_output + ) } } diff --git a/examples/modern-lstm/src/training.rs b/examples/modern-lstm/src/training.rs index a74babd509..9f6af81328 100644 --- a/examples/modern-lstm/src/training.rs +++ b/examples/modern-lstm/src/training.rs @@ -82,7 +82,7 @@ pub fn train(artifact_dir: &str, config: TrainingConfig, dev let mut valid_loss = 0.0; // Implement our training loop - for (_iteration, batch) in dataloader_train.iter().enumerate() { + for batch in dataloader_train.iter() { let output = model.forward(batch.sequences, None); let loss = MseLoss::new().forward(output, batch.targets.clone(), Mean); train_loss += loss.clone().into_scalar().elem::() * batch.targets.dims()[0] as f32; @@ -103,7 +103,7 @@ pub fn train(artifact_dir: &str, config: TrainingConfig, dev let valid_model = model.valid(); // Implement our validation loop - for (_iteration, batch) in dataloader_valid.iter().enumerate() { + for batch in dataloader_valid.iter() { let output = valid_model.forward(batch.sequences, None); let loss = MseLoss::new().forward(output, batch.targets.clone(), Mean); valid_loss += loss.clone().into_scalar().elem::() * batch.targets.dims()[0] as f32;