Support for PyTorch 2.4

CHANGELOG.md

@@ -7,6 +7,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ## Unreleased
 ### Changed
 
+## v0.16.0
+### Changed
+- PyTorch v2.4 support
+
 ## v0.16.0
 ### Changed
 - PyTorch v2.3 support

Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "tch"
-version = "0.16.0"
+version = "0.17.0"
 authors = ["Laurent Mazare <[email protected]>"]
 edition = "2021"
 build = "build.rs"
@@ -22,7 +22,7 @@ libc = "0.2.0"
 ndarray = "0.15"
 rand = "0.8"
 thiserror = "1"
-torch-sys = { version = "0.16.0", path = "torch-sys" }
+torch-sys = { version = "0.17.0", path = "torch-sys" }
 zip = "0.6"
 half = "2"
 safetensors = "0.3.0"

README.md

@@ -18,7 +18,7 @@ The code generation part for the C api on top of libtorch comes from
 
 ## Getting Started
 
-This crate requires the C++ PyTorch library (libtorch) in version *v2.3.0* to be available on
+This crate requires the C++ PyTorch library (libtorch) in version *v2.4.0* to be available on
 your system. You can either:
 
 - Use the system-wide libtorch installation (default).
@@ -85,7 +85,7 @@ seem to include `libtorch.a` by default so this would have to be compiled
 manually, e.g. via the following:
 
 ```bash
-git clone -b v2.3.0 --recurse-submodule https://github.com/pytorch/pytorch.git pytorch-static --depth 1
+git clone -b v2.4.0 --recurse-submodule https://github.com/pytorch/pytorch.git pytorch-static --depth 1
 cd pytorch-static
 USE_CUDA=OFF BUILD_SHARED_LIBS=OFF python setup.py build
 # export LIBTORCH to point at the build directory in pytorch-static.

examples/min-gpt/main.rs

@@ -80,7 +80,7 @@ fn causal_self_attention(p: &nn::Path, cfg: Config) -> impl ModuleT {
         let q = xs.apply(&query).view(sizes).transpose(1, 2);
         let v = xs.apply(&value).view(sizes).transpose(1, 2);
         let att = q.matmul(&k.transpose(-2, -1)) * (1.0 / f64::sqrt(sizes[3] as f64));
-        let att = att.masked_fill(&mask.i((.., .., ..sz_t, ..sz_t)).eq(0.), std::f64::NEG_INFINITY);
+        let att = att.masked_fill(&mask.i((.., .., ..sz_t, ..sz_t)).eq(0.), f64::NEG_INFINITY);
         let att = att.softmax(-1, Kind::Float).dropout(cfg.attn_pdrop, train);
         let ys = att.matmul(&v).transpose(1, 2).contiguous().view([sz_b, sz_t, sz_c]);
         ys.apply(&proj).dropout(cfg.resid_pdrop, train)

examples/python-extension/Cargo.toml

@@ -18,6 +18,6 @@ crate-type = ["cdylib"]
 
 [dependencies]
 pyo3 = { version = "0.21", features = ["extension-module"] }
-pyo3-tch = { path = "../../pyo3-tch", version = "0.16.0" }
-tch = { path = "../..", features = ["python-extension"], version = "0.16.0" }
-torch-sys = { path = "../../torch-sys", features = ["python-extension"], version = "0.16.0" }
+pyo3-tch = { path = "../../pyo3-tch", version = "0.17.0" }
+tch = { path = "../..", features = ["python-extension"], version = "0.17.0" }
+torch-sys = { path = "../../torch-sys", features = ["python-extension"], version = "0.17.0" }

gen/gen.ml

@@ -32,6 +32,7 @@ let excluded_functions =
     ; "_cummax_helper"
     ; "retain_grad"
     ; "_validate_sparse_coo_tensor_args"
+    ; "_sparse_semi_structured_addmm"
     ; "_backward"
     ; "size"
     ; "stride"
@@ -92,6 +93,7 @@ let excluded_prefixes =
   ; "_amp_foreach"
   ; "_nested_tensor"
   ; "_fused_adam"
+  ; "_fused_adagrad"
   ; "sym_"
   ; "_fused_sgd"
   ]
@@ -168,6 +170,7 @@ module Func = struct
     | "at::tensoroptions" -> Some TensorOptions
     | "at::intarrayref" -> Some (if is_nullable then IntListOption else IntList)
     | "at::arrayref<double>" -> Some DoubleList
+    | "const c10::list<::std::optional<at::tensor>> &"
     | "const c10::list<c10::optional<at::tensor>> &" -> Some TensorOptList
     | "const at::itensorlistref &" | "at::tensorlist" -> Some TensorList
     | "at::device" -> Some Device
@@ -590,7 +593,7 @@ let write_cpp funcs filename =
       let pc s = p out_cpp s in
       let ph s = p out_h s in
       pc "// THIS FILE IS AUTOMATICALLY GENERATED, DO NOT EDIT BY HAND!";
-      pc "#include \"%s.h\"" (Caml.Filename.basename filename);
+      pc "#include \"%s.h\"" (Stdlib.Filename.basename filename);
       pc "";
       ph "// THIS FILE IS AUTOMATICALLY GENERATED, DO NOT EDIT BY HAND!";
       ph "#include \"torch_api.h\"";
@@ -879,7 +882,7 @@ let run
 
 let () =
   run
-    ~yaml_filename:"third_party/pytorch/Declarations-v2.3.0.yaml"
+    ~yaml_filename:"third_party/pytorch/Declarations-v2.4.0.yaml"
     ~cpp_filename:"torch-sys/libtch/torch_api_generated"
     ~ffi_filename:"torch-sys/src/c_generated.rs"
     ~wrapper_filename:"src/wrappers/tensor_generated.rs"

pyo3-tch/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "pyo3-tch"
-version = "0.16.0"
+version = "0.17.0"
 authors = ["Laurent Mazare <[email protected]>"]
 edition = "2021"
 build = "build.rs"
@@ -12,6 +12,6 @@ categories = ["science"]
 license = "MIT/Apache-2.0"
 
 [dependencies]
-tch = { path = "..", features = ["python-extension"], version = "0.16.0" }
-torch-sys = { path = "../torch-sys", features = ["python-extension"], version = "0.16.0" }
-pyo3 = { version = "0.21", features = ["extension-module"] }
+tch = { path = "..", features = ["python-extension"], version = "0.17.0" }
+torch-sys = { path = "../torch-sys", features = ["python-extension"], version = "0.17.0" }
+pyo3 = { version = "0.21", features = ["extension-module"] }

src/nn/init.rs

@@ -107,7 +107,7 @@ pub fn f_init(i: Init, dims: &[i64], device: Device) -> Result<Tensor, TchError>
             // Optimize the case for which a single C++ code can be done.
             if cst == 0. {
                 Tensor::f_zeros(dims, (Kind::Float, device))
-            } else if (cst - 1.).abs() <= std::f64::EPSILON {
+            } else if (cst - 1.).abs() <= f64::EPSILON {
                 Tensor::f_ones(dims, (Kind::Float, device))
             } else {
                 Tensor::f_ones(dims, (Kind::Float, device)).map(|t| t * cst)
@@ -117,7 +117,7 @@ pub fn f_init(i: Init, dims: &[i64], device: Device) -> Result<Tensor, TchError>
             Tensor::f_zeros(dims, (Kind::Float, device))?.f_uniform_(lo, up)
         }
         Init::Randn { mean, stdev } => {
-            if mean == 0. && (stdev - 1.).abs() <= std::f64::EPSILON {
+            if mean == 0. && (stdev - 1.).abs() <= f64::EPSILON {
                 Tensor::f_randn(dims, (Kind::Float, device))
             } else {
                 Tensor::f_randn(dims, (Kind::Float, device)).map(|t| t * stdev + mean)